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Pandas documentation errors
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "%matplotlib inline" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "The concept from Marc Garcia (@datapythonista) is to eliminate a specific validation error from the current code entirely, so this can be added to the CI and we can overcome future issues of the same type" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "An overview of the errors can be found in the `validate_docstring.py` scripts in the scripts folder:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 51, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| " 'GL01': 'Docstring text (summary) should start in the line immediately '\n", | |
| " 'GL02': 'Closing quotes should be placed in the line after the last text '\n", | |
| " 'GL03': 'Double line break found; please use only one blank line to '\n", | |
| " 'GL04': 'Private classes ({mentioned_private_classes}) should not be '\n", | |
| " 'GL05': 'Tabs found at the start of line \"{line_with_tabs}\", please use '\n", | |
| " 'GL06': 'Found unknown section \"{section}\". Allowed sections are: '\n", | |
| " 'GL07': 'Sections are in the wrong order. Correct order is: '\n", | |
| " 'GL08': 'The object does not have a docstring',\n", | |
| " 'GL09': 'Deprecation warning should precede extended summary',\n", | |
| " 'SS01': 'No summary found (a short summary in a single line should be '\n", | |
| " 'SS02': 'Summary does not start with a capital letter',\n", | |
| " 'SS03': 'Summary does not end with a period',\n", | |
| " 'SS04': 'Summary contains heading whitespaces',\n", | |
| " 'SS05': 'Summary must start with infinitive verb, not third person '\n", | |
| " 'SS06': 'Summary should fit in a single line',\n", | |
| " 'ES01': 'No extended summary found',\n", | |
| " 'PR01': 'Parameters {missing_params} not documented',\n", | |
| " 'PR02': 'Unknown parameters {unknown_params}',\n", | |
| " 'PR03': 'Wrong parameters order. Actual: {actual_params}. '\n", | |
| " 'PR04': 'Parameter \"{param_name}\" has no type',\n", | |
| " 'PR05': 'Parameter \"{param_name}\" type should not finish with \".\"',\n", | |
| " 'PR06': 'Parameter \"{param_name}\" type should use \"{right_type}\" instead '\n", | |
| " 'PR07': 'Parameter \"{param_name}\" has no description',\n", | |
| " 'PR08': 'Parameter \"{param_name}\" description should start with a '\n", | |
| " 'PR09': 'Parameter \"{param_name}\" description should finish with \".\"',\n", | |
| " 'PR10': 'Parameter \"{param_name}\" requires a space before the colon '\n", | |
| " 'RT01': 'No Returns section found',\n", | |
| " 'RT02': 'The first line of the Returns section should contain only the '\n", | |
| " 'RT03': 'Return value has no description',\n", | |
| " 'RT04': 'Return value description should start with a capital letter',\n", | |
| " 'RT05': 'Return value description should finish with \".\"',\n", | |
| " 'YD01': 'No Yields section found',\n", | |
| " 'SA01': 'See Also section not found',\n", | |
| " 'SA02': 'Missing period at end of description for See Also '\n", | |
| " 'SA03': 'Description should be capitalized for See Also '\n", | |
| " 'SA04': 'Missing description for See Also \"{reference_name}\" reference',\n", | |
| " 'SA05': '{reference_name} in `See Also` section does not need `pandas` '\n", | |
| " 'EX01': 'No examples section found',\n", | |
| " 'EX02': 'Examples do not pass tests:\\n{doctest_log}',\n", | |
| " 'EX03': 'flake8 error: {error_code} {error_message}{times_happening}',\n", | |
| " 'EX04': 'Do not import {imported_library}, as it is imported '\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "!cat scripts/validate_docstrings.py | grep \"': '\"" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "In order to get an overview of the current validation errors, run the validate script in your terminal:\n", | |
| "\n", | |
| "```\n", | |
| "./scripts/validate_docstrings.py --format=json > validation_errors.json\n", | |
| "```" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "There is a great tool to read in the resulting json file ;-). It is called Pandas!" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 52, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "errors = pd.read_json('validation_errors.json', orient='index')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Wrangling the columns containing the errors:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 54, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "errors[\"errors_delimited\"] = (errors\n", | |
| " .loc[:, 'errors']\n", | |
| " .map(lambda err_list: '|'.join([err[0] for err in err_list])))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 55, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/html": [ | |
| "<div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>deprecated</th>\n", | |
| " <th>docstring</th>\n", | |
| " <th>errors</th>\n", | |
| " <th>examples_errors</th>\n", | |
| " <th>file</th>\n", | |
| " <th>file_line</th>\n", | |
| " <th>github_link</th>\n", | |
| " <th>in_api</th>\n", | |
| " <th>section</th>\n", | |
| " <th>shared_code_with</th>\n", | |
| " <th>subsection</th>\n", | |
| " <th>type</th>\n", | |
| " <th>warnings</th>\n", | |
| " <th>errors_delimited</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>pandas.describe_option</th>\n", | |
| " <td>0</td>\n", | |
| " <td>describe_option(pat, _print_desc=False)\\n\\nPri...</td>\n", | |
| " <td>[[GL02, Closing quotes should be placed in the...</td>\n", | |
| " <td></td>\n", | |
| " <td>None</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>https://github.com/pandas-dev/pandas/blob/mast...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Working with options</td>\n", | |
| " <td></td>\n", | |
| " <td></td>\n", | |
| " <td>CallableDynamicDoc</td>\n", | |
| " <td>[[SA01, See Also section not found], [EX01, No...</td>\n", | |
| " <td>GL02|GL03|PR01|PR02</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>pandas.reset_option</th>\n", | |
| " <td>0</td>\n", | |
| " <td>reset_option(pat)\\n\\nReset one or more options...</td>\n", | |
| " <td>[[GL02, Closing quotes should be placed in the...</td>\n", | |
| " <td></td>\n", | |
| " <td>None</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>https://github.com/pandas-dev/pandas/blob/mast...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Working with options</td>\n", | |
| " <td>pandas.describe_option</td>\n", | |
| " <td></td>\n", | |
| " <td>CallableDynamicDoc</td>\n", | |
| " <td>[[SA01, See Also section not found], [EX01, No...</td>\n", | |
| " <td>GL02|GL03|PR01|PR02</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>pandas.get_option</th>\n", | |
| " <td>0</td>\n", | |
| " <td>get_option(pat)\\n\\nRetrieves the value of the ...</td>\n", | |
| " <td>[[GL02, Closing quotes should be placed in the...</td>\n", | |
| " <td></td>\n", | |
| " <td>None</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>https://github.com/pandas-dev/pandas/blob/mast...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Working with options</td>\n", | |
| " <td>pandas.reset_option</td>\n", | |
| " <td></td>\n", | |
| " <td>CallableDynamicDoc</td>\n", | |
| " <td>[[SA01, See Also section not found], [EX01, No...</td>\n", | |
| " <td>GL02|GL03|PR01|PR02</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>pandas.set_option</th>\n", | |
| " <td>0</td>\n", | |
| " <td>set_option(pat, value)\\n\\nSets the value of th...</td>\n", | |
| " <td>[[GL02, Closing quotes should be placed in the...</td>\n", | |
| " <td></td>\n", | |
| " <td>None</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>https://github.com/pandas-dev/pandas/blob/mast...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Working with options</td>\n", | |
| " <td>pandas.get_option</td>\n", | |
| " <td></td>\n", | |
| " <td>CallableDynamicDoc</td>\n", | |
| " <td>[[SA01, See Also section not found], [EX01, No...</td>\n", | |
| " <td>GL02|GL03|PR01|PR02|PR10|PR08</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>pandas.option_context</th>\n", | |
| " <td>0</td>\n", | |
| " <td>Context manager to temporarily set options in ...</td>\n", | |
| " <td>[[PR01, Parameters {*args} not documented], [E...</td>\n", | |
| " <td>**********************************************...</td>\n", | |
| " <td>pandas/core/config.py</td>\n", | |
| " <td>377.0</td>\n", | |
| " <td>https://github.com/pandas-dev/pandas/blob/mast...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Working with options</td>\n", | |
| " <td></td>\n", | |
| " <td></td>\n", | |
| " <td>type</td>\n", | |
| " <td>[[SA01, See Also section not found]]</td>\n", | |
| " <td>PR01|EX02|EX03</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " deprecated \\\n", | |
| "pandas.describe_option 0 \n", | |
| "pandas.reset_option 0 \n", | |
| "pandas.get_option 0 \n", | |
| "pandas.set_option 0 \n", | |
| "pandas.option_context 0 \n", | |
| "\n", | |
| " docstring \\\n", | |
| "pandas.describe_option describe_option(pat, _print_desc=False)\\n\\nPri... \n", | |
| "pandas.reset_option reset_option(pat)\\n\\nReset one or more options... \n", | |
| "pandas.get_option get_option(pat)\\n\\nRetrieves the value of the ... \n", | |
| "pandas.set_option set_option(pat, value)\\n\\nSets the value of th... \n", | |
| "pandas.option_context Context manager to temporarily set options in ... \n", | |
| "\n", | |
| " errors \\\n", | |
| "pandas.describe_option [[GL02, Closing quotes should be placed in the... \n", | |
| "pandas.reset_option [[GL02, Closing quotes should be placed in the... \n", | |
| "pandas.get_option [[GL02, Closing quotes should be placed in the... \n", | |
| "pandas.set_option [[GL02, Closing quotes should be placed in the... \n", | |
| "pandas.option_context [[PR01, Parameters {*args} not documented], [E... \n", | |
| "\n", | |
| " examples_errors \\\n", | |
| "pandas.describe_option \n", | |
| "pandas.reset_option \n", | |
| "pandas.get_option \n", | |
| "pandas.set_option \n", | |
| "pandas.option_context **********************************************... \n", | |
| "\n", | |
| " file file_line \\\n", | |
| "pandas.describe_option None NaN \n", | |
| "pandas.reset_option None NaN \n", | |
| "pandas.get_option None NaN \n", | |
| "pandas.set_option None NaN \n", | |
| "pandas.option_context pandas/core/config.py 377.0 \n", | |
| "\n", | |
| " github_link \\\n", | |
| "pandas.describe_option https://github.com/pandas-dev/pandas/blob/mast... \n", | |
| "pandas.reset_option https://github.com/pandas-dev/pandas/blob/mast... \n", | |
| "pandas.get_option https://github.com/pandas-dev/pandas/blob/mast... \n", | |
| "pandas.set_option https://github.com/pandas-dev/pandas/blob/mast... \n", | |
| "pandas.option_context https://github.com/pandas-dev/pandas/blob/mast... \n", | |
| "\n", | |
| " in_api section shared_code_with \\\n", | |
| "pandas.describe_option 1 Working with options \n", | |
| "pandas.reset_option 1 Working with options pandas.describe_option \n", | |
| "pandas.get_option 1 Working with options pandas.reset_option \n", | |
| "pandas.set_option 1 Working with options pandas.get_option \n", | |
| "pandas.option_context 1 Working with options \n", | |
| "\n", | |
| " subsection type \\\n", | |
| "pandas.describe_option CallableDynamicDoc \n", | |
| "pandas.reset_option CallableDynamicDoc \n", | |
| "pandas.get_option CallableDynamicDoc \n", | |
| "pandas.set_option CallableDynamicDoc \n", | |
| "pandas.option_context type \n", | |
| "\n", | |
| " warnings \\\n", | |
| "pandas.describe_option [[SA01, See Also section not found], [EX01, No... \n", | |
| "pandas.reset_option [[SA01, See Also section not found], [EX01, No... \n", | |
| "pandas.get_option [[SA01, See Also section not found], [EX01, No... \n", | |
| "pandas.set_option [[SA01, See Also section not found], [EX01, No... \n", | |
| "pandas.option_context [[SA01, See Also section not found]] \n", | |
| "\n", | |
| " errors_delimited \n", | |
| "pandas.describe_option GL02|GL03|PR01|PR02 \n", | |
| "pandas.reset_option GL02|GL03|PR01|PR02 \n", | |
| "pandas.get_option GL02|GL03|PR01|PR02 \n", | |
| "pandas.set_option GL02|GL03|PR01|PR02|PR10|PR08 \n", | |
| "pandas.option_context PR01|EX02|EX03 " | |
| ] | |
| }, | |
| "execution_count": 55, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "errors.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "An overview count of the error type counts:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 56, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7fb3b66edfd0>" | |
| ] | |
| }, | |
| "execution_count": 56, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
| "image/png": 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\n", | |
| "text/plain": [ | |
| "<Figure size 720x720 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "%matplotlib inline\n", | |
| "\n", | |
| "(errors\n", | |
| " .loc[:, 'errors']\n", | |
| " .map(lambda err_list: '|'.join([err[0] for err in err_list]))\n", | |
| " .str.get_dummies('|')\n", | |
| " .sum(axis='index')\n", | |
| " .sort_values()\n", | |
| " .plot.barh(figsize = (10, 10)))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Hence, the strategy is to work __bottom to top__ eliminating those with a few errors. For example, the `PR10` type of errors first, would involve tackling the following issues:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 50, | |
| "metadata": {}, | |
| "outputs": [ | |
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| "data": { | |
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| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>deprecated</th>\n", | |
| " <th>docstring</th>\n", | |
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| " <th>github_link</th>\n", | |
| " <th>in_api</th>\n", | |
| " <th>section</th>\n", | |
| " <th>shared_code_with</th>\n", | |
| " <th>subsection</th>\n", | |
| " <th>type</th>\n", | |
| " <th>warnings</th>\n", | |
| " <th>errors_delimited</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>pandas.set_option</th>\n", | |
| " <td>0</td>\n", | |
| " <td>set_option(pat, value)\\n\\nSets the value of th...</td>\n", | |
| " <td>[[GL02, Closing quotes should be placed in the...</td>\n", | |
| " <td></td>\n", | |
| " <td>None</td>\n", | |
| " <td>NaN</td>\n", | |
| " <td>https://github.com/pandas-dev/pandas/blob/mast...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Working with options</td>\n", | |
| " <td>pandas.get_option</td>\n", | |
| " <td></td>\n", | |
| " <td>CallableDynamicDoc</td>\n", | |
| " <td>[[SA01, See Also section not found], [EX01, No...</td>\n", | |
| " <td>GL02|GL03|PR01|PR02|PR10|PR08</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>pandas.ExcelWriter</th>\n", | |
| " <td>0</td>\n", | |
| " <td>Class for writing DataFrame objects into excel...</td>\n", | |
| " <td>[[SS06, Summary should fit in a single line], ...</td>\n", | |
| " <td>**********************************************...</td>\n", | |
| " <td>pandas/io/excel.py</td>\n", | |
| " <td>984.0</td>\n", | |
| " <td>https://github.com/pandas-dev/pandas/blob/mast...</td>\n", | |
| " <td>1</td>\n", | |
| " <td></td>\n", | |
| " <td></td>\n", | |
| " <td>Excel</td>\n", | |
| " <td>ABCMeta</td>\n", | |
| " <td>[[SA01, See Also section not found]]</td>\n", | |
| " <td>SS06|PR01|PR02|PR06|PR06|PR06|PR09|PR06|PR09|P...</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>pandas.core.groupby.DataFrameGroupBy.boxplot</th>\n", | |
| " <td>0</td>\n", | |
| " <td>Make box plots from DataFrameGroupBy data.\\n\\n...</td>\n", | |
| " <td>[[PR01, Parameters {subplots, **kwds} not docu...</td>\n", | |
| " <td>**********************************************...</td>\n", | |
| " <td>pandas/plotting/_core.py</td>\n", | |
| " <td>2542.0</td>\n", | |
| " <td>https://github.com/pandas-dev/pandas/blob/mast...</td>\n", | |
| " <td>1</td>\n", | |
| " <td>Computations / Descriptive Stats</td>\n", | |
| " <td></td>\n", | |
| " <td></td>\n", | |
| " <td>function</td>\n", | |
| " <td>[[ES01, No extended summary found], [SA01, See...</td>\n", | |
| " <td>PR01|PR02|PR07|PR10|PR08|PR09|PR09|PR06|PR07|P...</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " deprecated \\\n", | |
| "pandas.set_option 0 \n", | |
| "pandas.ExcelWriter 0 \n", | |
| "pandas.core.groupby.DataFrameGroupBy.boxplot 0 \n", | |
| "\n", | |
| " docstring \\\n", | |
| "pandas.set_option set_option(pat, value)\\n\\nSets the value of th... \n", | |
| "pandas.ExcelWriter Class for writing DataFrame objects into excel... \n", | |
| "pandas.core.groupby.DataFrameGroupBy.boxplot Make box plots from DataFrameGroupBy data.\\n\\n... \n", | |
| "\n", | |
| " errors \\\n", | |
| "pandas.set_option [[GL02, Closing quotes should be placed in the... \n", | |
| "pandas.ExcelWriter [[SS06, Summary should fit in a single line], ... \n", | |
| "pandas.core.groupby.DataFrameGroupBy.boxplot [[PR01, Parameters {subplots, **kwds} not docu... \n", | |
| "\n", | |
| " examples_errors \\\n", | |
| "pandas.set_option \n", | |
| "pandas.ExcelWriter **********************************************... \n", | |
| "pandas.core.groupby.DataFrameGroupBy.boxplot **********************************************... \n", | |
| "\n", | |
| " file \\\n", | |
| "pandas.set_option None \n", | |
| "pandas.ExcelWriter pandas/io/excel.py \n", | |
| "pandas.core.groupby.DataFrameGroupBy.boxplot pandas/plotting/_core.py \n", | |
| "\n", | |
| " file_line \\\n", | |
| "pandas.set_option NaN \n", | |
| "pandas.ExcelWriter 984.0 \n", | |
| "pandas.core.groupby.DataFrameGroupBy.boxplot 2542.0 \n", | |
| "\n", | |
| " github_link \\\n", | |
| "pandas.set_option https://github.com/pandas-dev/pandas/blob/mast... \n", | |
| "pandas.ExcelWriter https://github.com/pandas-dev/pandas/blob/mast... \n", | |
| "pandas.core.groupby.DataFrameGroupBy.boxplot https://github.com/pandas-dev/pandas/blob/mast... \n", | |
| "\n", | |
| " in_api \\\n", | |
| "pandas.set_option 1 \n", | |
| "pandas.ExcelWriter 1 \n", | |
| "pandas.core.groupby.DataFrameGroupBy.boxplot 1 \n", | |
| "\n", | |
| " section \\\n", | |
| "pandas.set_option Working with options \n", | |
| "pandas.ExcelWriter \n", | |
| "pandas.core.groupby.DataFrameGroupBy.boxplot Computations / Descriptive Stats \n", | |
| "\n", | |
| " shared_code_with subsection \\\n", | |
| "pandas.set_option pandas.get_option \n", | |
| "pandas.ExcelWriter Excel \n", | |
| "pandas.core.groupby.DataFrameGroupBy.boxplot \n", | |
| "\n", | |
| " type \\\n", | |
| "pandas.set_option CallableDynamicDoc \n", | |
| "pandas.ExcelWriter ABCMeta \n", | |
| "pandas.core.groupby.DataFrameGroupBy.boxplot function \n", | |
| "\n", | |
| " warnings \\\n", | |
| "pandas.set_option [[SA01, See Also section not found], [EX01, No... \n", | |
| "pandas.ExcelWriter [[SA01, See Also section not found]] \n", | |
| "pandas.core.groupby.DataFrameGroupBy.boxplot [[ES01, No extended summary found], [SA01, See... \n", | |
| "\n", | |
| " errors_delimited \n", | |
| "pandas.set_option GL02|GL03|PR01|PR02|PR10|PR08 \n", | |
| "pandas.ExcelWriter SS06|PR01|PR02|PR06|PR06|PR06|PR09|PR06|PR09|P... \n", | |
| "pandas.core.groupby.DataFrameGroupBy.boxplot PR01|PR02|PR07|PR10|PR08|PR09|PR09|PR06|PR07|P... " | |
| ] | |
| }, | |
| "execution_count": 50, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "errors[errors[\"errors_delimited\"].str.contains(\"PR10\")]" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "__Note__: you can ignore the `YD01` errors probaby, as this can be derivded from a shortcoming in the validation script as well..." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": {}, | |
| "outputs": [], | |
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| "version": 3 | |
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| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.7.0" | |
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| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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| {"pandas.describe_option": {"type": "CallableDynamicDoc", "docstring": "describe_option(pat, _print_desc=False)\n\nPrints the description for one or more registered options.\n\nCall with not arguments to get a listing for all registered options.\n\nAvailable options:\n\n- compute.[use_bottleneck, use_numexpr]\n- display.[chop_threshold, colheader_justify, column_space, date_dayfirst,\n date_yearfirst, encoding, expand_frame_repr, float_format]\n- display.html.[border, table_schema, use_mathjax]\n- display.[large_repr]\n- display.latex.[escape, longtable, multicolumn, multicolumn_format, multirow,\n repr]\n- display.[max_categories, max_columns, max_colwidth, max_info_columns,\n max_info_rows, max_rows, max_seq_items, memory_usage, multi_sparse,\n notebook_repr_html, pprint_nest_depth, precision, show_dimensions]\n- display.unicode.[ambiguous_as_wide, east_asian_width]\n- display.[width]\n- html.[border]\n- io.excel.xls.[writer]\n- io.excel.xlsm.[writer]\n- io.excel.xlsx.[writer]\n- io.hdf.[default_format, dropna_table]\n- io.parquet.[engine]\n- mode.[chained_assignment, sim_interactive, use_inf_as_na, use_inf_as_null]\n- plotting.matplotlib.[register_converters]\n\nParameters\n----------\npat : str\n Regexp pattern. All matching keys will have their description displayed.\n_print_desc : bool, default True\n If True (default) the description(s) will be printed to stdout.\n Otherwise, the description(s) will be returned as a unicode string\n (for testing).\n\nReturns\n-------\nNone by default, the description(s) as a unicode string if _print_desc\nis False\n\nNotes\n-----\nThe available options with its descriptions:\n\ncompute.use_bottleneck : bool\n Use the bottleneck library to accelerate if it is installed,\n the default is True\n Valid values: False,True\n [default: True] [currently: True]\n\ncompute.use_numexpr : bool\n Use the numexpr library to accelerate computation if it is installed,\n the default is True\n Valid values: False,True\n [default: True] [currently: True]\n\ndisplay.chop_threshold : float or None\n if set to a float value, all float values smaller then the given threshold\n will be displayed as exactly 0 by repr and friends.\n [default: None] [currently: None]\n\ndisplay.colheader_justify : 'left'/'right'\n Controls the justification of column headers. used by DataFrameFormatter.\n [default: right] [currently: right]\n\ndisplay.column_space No description available.\n [default: 12] [currently: 12]\n\ndisplay.date_dayfirst : boolean\n When True, prints and parses dates with the day first, eg 20/01/2005\n [default: False] [currently: False]\n\ndisplay.date_yearfirst : boolean\n When True, prints and parses dates with the year first, eg 2005/01/20\n [default: False] [currently: False]\n\ndisplay.encoding : str/unicode\n Defaults to the detected encoding of the console.\n Specifies the encoding to be used for strings returned by to_string,\n these are generally strings meant to be displayed on the console.\n [default: UTF-8] [currently: UTF-8]\n\ndisplay.expand_frame_repr : boolean\n Whether to print out the full DataFrame repr for wide DataFrames across\n multiple lines, `max_columns` is still respected, but the output will\n wrap-around across multiple \"pages\" if its width exceeds `display.width`.\n [default: True] [currently: True]\n\ndisplay.float_format : callable\n The callable should accept a floating point number and return\n a string with the desired format of the number. This is used\n in some places like SeriesFormatter.\n See formats.format.EngFormatter for an example.\n [default: None] [currently: None]\n\ndisplay.html.border : int\n A ``border=value`` attribute is inserted in the ``<table>`` tag\n for the DataFrame HTML repr.\n [default: 1] [currently: 1]\n\ndisplay.html.table_schema : boolean\n Whether to publish a Table Schema representation for frontends\n that support it.\n (default: False)\n [default: False] [currently: False]\n\ndisplay.html.use_mathjax : boolean\n When True, Jupyter notebook will process table contents using MathJax,\n rendering mathematical expressions enclosed by the dollar symbol.\n (default: True)\n [default: True] [currently: True]\n\ndisplay.large_repr : 'truncate'/'info'\n For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can\n show a truncated table (the default from 0.13), or switch to the view from\n df.info() (the behaviour in earlier versions of pandas).\n [default: truncate] [currently: truncate]\n\ndisplay.latex.escape : bool\n This specifies if the to_latex method of a Dataframe uses escapes special\n characters.\n Valid values: False,True\n [default: True] [currently: True]\n\ndisplay.latex.longtable :bool\n This specifies if the to_latex method of a Dataframe uses the longtable\n format.\n Valid values: False,True\n [default: False] [currently: False]\n\ndisplay.latex.multicolumn : bool\n This specifies if the to_latex method of a Dataframe uses multicolumns\n to pretty-print MultiIndex columns.\n Valid values: False,True\n [default: True] [currently: True]\n\ndisplay.latex.multicolumn_format : bool\n This specifies if the to_latex method of a Dataframe uses multicolumns\n to pretty-print MultiIndex columns.\n Valid values: False,True\n [default: l] [currently: l]\n\ndisplay.latex.multirow : bool\n This specifies if the to_latex method of a Dataframe uses multirows\n to pretty-print MultiIndex rows.\n Valid values: False,True\n [default: False] [currently: False]\n\ndisplay.latex.repr : boolean\n Whether to produce a latex DataFrame representation for jupyter\n environments that support it.\n (default: False)\n [default: False] [currently: False]\n\ndisplay.max_categories : int\n This sets the maximum number of categories pandas should output when\n printing out a `Categorical` or a Series of dtype \"category\".\n [default: 8] [currently: 8]\n\ndisplay.max_columns : int\n If max_cols is exceeded, switch to truncate view. Depending on\n `large_repr`, objects are either centrally truncated or printed as\n a summary view. 'None' value means unlimited.\n\n In case python/IPython is running in a terminal and `large_repr`\n equals 'truncate' this can be set to 0 and pandas will auto-detect\n the width of the terminal and print a truncated object which fits\n the screen width. The IPython notebook, IPython qtconsole, or IDLE\n do not run in a terminal and hence it is not possible to do\n correct auto-detection.\n [default: 0] [currently: 0]\n\ndisplay.max_colwidth : int\n The maximum width in characters of a column in the repr of\n a pandas data structure. When the column overflows, a \"...\"\n placeholder is embedded in the output.\n [default: 50] [currently: 50]\n\ndisplay.max_info_columns : int\n max_info_columns is used in DataFrame.info method to decide if\n per column information will be printed.\n [default: 100] [currently: 100]\n\ndisplay.max_info_rows : int or None\n df.info() will usually show null-counts for each column.\n For large frames this can be quite slow. max_info_rows and max_info_cols\n limit this null check only to frames with smaller dimensions than\n specified.\n [default: 1690785] [currently: 1690785]\n\ndisplay.max_rows : int\n If max_rows is exceeded, switch to truncate view. Depending on\n `large_repr`, objects are either centrally truncated or printed as\n a summary view. 'None' value means unlimited.\n\n In case python/IPython is running in a terminal and `large_repr`\n equals 'truncate' this can be set to 0 and pandas will auto-detect\n the height of the terminal and print a truncated object which fits\n the screen height. The IPython notebook, IPython qtconsole, or\n IDLE do not run in a terminal and hence it is not possible to do\n correct auto-detection.\n [default: 60] [currently: 60]\n\ndisplay.max_seq_items : int or None\n when pretty-printing a long sequence, no more then `max_seq_items`\n will be printed. If items are omitted, they will be denoted by the\n addition of \"...\" to the resulting string.\n\n If set to None, the number of items to be printed is unlimited.\n [default: 100] [currently: 100]\n\ndisplay.memory_usage : bool, string or None\n This specifies if the memory usage of a DataFrame should be displayed when\n df.info() is called. Valid values True,False,'deep'\n [default: True] [currently: True]\n\ndisplay.multi_sparse : boolean\n \"sparsify\" MultiIndex display (don't display repeated\n elements in outer levels within groups)\n [default: True] [currently: True]\n\ndisplay.notebook_repr_html : boolean\n When True, IPython notebook will use html representation for\n pandas objects (if it is available).\n [default: True] [currently: True]\n\ndisplay.pprint_nest_depth : int\n Controls the number of nested levels to process when pretty-printing\n [default: 3] [currently: 3]\n\ndisplay.precision : int\n Floating point output precision (number of significant digits). This is\n only a suggestion\n [default: 6] [currently: 6]\n\ndisplay.show_dimensions : boolean or 'truncate'\n Whether to print out dimensions at the end of DataFrame repr.\n If 'truncate' is specified, only print out the dimensions if the\n frame is truncated (e.g. not display all rows and/or columns)\n [default: truncate] [currently: truncate]\n\ndisplay.unicode.ambiguous_as_wide : boolean\n Whether to use the Unicode East Asian Width to calculate the display text\n width.\n Enabling this may affect to the performance (default: False)\n [default: False] [currently: False]\n\ndisplay.unicode.east_asian_width : boolean\n Whether to use the Unicode East Asian Width to calculate the display text\n width.\n Enabling this may affect to the performance (default: False)\n [default: False] [currently: False]\n\ndisplay.width : int\n Width of the display in characters. In case python/IPython is running in\n a terminal this can be set to None and pandas will correctly auto-detect\n the width.\n Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a\n terminal and hence it is not possible to correctly detect the width.\n [default: 80] [currently: 80]\n\nhtml.border : int\n A ``border=value`` attribute is inserted in the ``<table>`` tag\n for the DataFrame HTML repr.\n [default: 1] [currently: 1]\n (Deprecated, use `display.html.border` instead.)\n\nio.excel.xls.writer : string\n The default Excel writer engine for 'xls' files. Available options:\n auto, xlwt.\n [default: auto] [currently: auto]\n\nio.excel.xlsm.writer : string\n The default Excel writer engine for 'xlsm' files. Available options:\n auto, openpyxl.\n [default: auto] [currently: auto]\n\nio.excel.xlsx.writer : string\n The default Excel writer engine for 'xlsx' files. Available options:\n auto, openpyxl, xlsxwriter.\n [default: auto] [currently: auto]\n\nio.hdf.default_format : format\n default format writing format, if None, then\n put will default to 'fixed' and append will default to 'table'\n [default: None] [currently: None]\n\nio.hdf.dropna_table : boolean\n drop ALL nan rows when appending to a table\n [default: False] [currently: False]\n\nio.parquet.engine : string\n The default parquet reader/writer engine. Available options:\n 'auto', 'pyarrow', 'fastparquet', the default is 'auto'\n [default: auto] [currently: auto]\n\nmode.chained_assignment : string\n Raise an exception, warn, or no action if trying to use chained assignment,\n The default is warn\n [default: warn] [currently: warn]\n\nmode.sim_interactive : boolean\n Whether to simulate interactive mode for purposes of testing\n [default: False] [currently: False]\n\nmode.use_inf_as_na : boolean\n True means treat None, NaN, INF, -INF as NA (old way),\n False means None and NaN are null, but INF, -INF are not NA\n (new way).\n [default: False] [currently: False]\n\nmode.use_inf_as_null : boolean\n use_inf_as_null had been deprecated and will be removed in a future\n version. Use `use_inf_as_na` instead.\n [default: False] [currently: False]\n (Deprecated, use `mode.use_inf_as_na` instead.)\n\nplotting.matplotlib.register_converters : bool\n Whether to register converters with matplotlib's units registry for\n dates, times, datetimes, and Periods. Toggling to False will remove\n the converters, restoring any converters that pandas overwrote.\n [default: True] [currently: True]", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"], ["PR01", "Parameters {**kwds, *args} not documented"], ["PR02", "Unknown parameters {pat, _print_desc}"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Working with options", "subsection": "", "shared_code_with": ""}, "pandas.reset_option": {"type": "CallableDynamicDoc", "docstring": "reset_option(pat)\n\nReset one or more options to their default value.\n\nPass \"all\" as argument to reset all options.\n\nAvailable options:\n\n- compute.[use_bottleneck, use_numexpr]\n- display.[chop_threshold, colheader_justify, column_space, date_dayfirst,\n date_yearfirst, encoding, expand_frame_repr, float_format]\n- display.html.[border, table_schema, use_mathjax]\n- display.[large_repr]\n- display.latex.[escape, longtable, multicolumn, multicolumn_format, multirow,\n repr]\n- display.[max_categories, max_columns, max_colwidth, max_info_columns,\n max_info_rows, max_rows, max_seq_items, memory_usage, multi_sparse,\n notebook_repr_html, pprint_nest_depth, precision, show_dimensions]\n- display.unicode.[ambiguous_as_wide, east_asian_width]\n- display.[width]\n- html.[border]\n- io.excel.xls.[writer]\n- io.excel.xlsm.[writer]\n- io.excel.xlsx.[writer]\n- io.hdf.[default_format, dropna_table]\n- io.parquet.[engine]\n- mode.[chained_assignment, sim_interactive, use_inf_as_na, use_inf_as_null]\n- plotting.matplotlib.[register_converters]\n\nParameters\n----------\npat : str/regex\n If specified only options matching `prefix*` will be reset.\n Note: partial matches are supported for convenience, but unless you\n use the full option name (e.g. x.y.z.option_name), your code may break\n in future versions if new options with similar names are introduced.\n\nReturns\n-------\nNone\n\nNotes\n-----\nThe available options with its descriptions:\n\ncompute.use_bottleneck : bool\n Use the bottleneck library to accelerate if it is installed,\n the default is True\n Valid values: False,True\n [default: True] [currently: True]\n\ncompute.use_numexpr : bool\n Use the numexpr library to accelerate computation if it is installed,\n the default is True\n Valid values: False,True\n [default: True] [currently: True]\n\ndisplay.chop_threshold : float or None\n if set to a float value, all float values smaller then the given threshold\n will be displayed as exactly 0 by repr and friends.\n [default: None] [currently: None]\n\ndisplay.colheader_justify : 'left'/'right'\n Controls the justification of column headers. used by DataFrameFormatter.\n [default: right] [currently: right]\n\ndisplay.column_space No description available.\n [default: 12] [currently: 12]\n\ndisplay.date_dayfirst : boolean\n When True, prints and parses dates with the day first, eg 20/01/2005\n [default: False] [currently: False]\n\ndisplay.date_yearfirst : boolean\n When True, prints and parses dates with the year first, eg 2005/01/20\n [default: False] [currently: False]\n\ndisplay.encoding : str/unicode\n Defaults to the detected encoding of the console.\n Specifies the encoding to be used for strings returned by to_string,\n these are generally strings meant to be displayed on the console.\n [default: UTF-8] [currently: UTF-8]\n\ndisplay.expand_frame_repr : boolean\n Whether to print out the full DataFrame repr for wide DataFrames across\n multiple lines, `max_columns` is still respected, but the output will\n wrap-around across multiple \"pages\" if its width exceeds `display.width`.\n [default: True] [currently: True]\n\ndisplay.float_format : callable\n The callable should accept a floating point number and return\n a string with the desired format of the number. This is used\n in some places like SeriesFormatter.\n See formats.format.EngFormatter for an example.\n [default: None] [currently: None]\n\ndisplay.html.border : int\n A ``border=value`` attribute is inserted in the ``<table>`` tag\n for the DataFrame HTML repr.\n [default: 1] [currently: 1]\n\ndisplay.html.table_schema : boolean\n Whether to publish a Table Schema representation for frontends\n that support it.\n (default: False)\n [default: False] [currently: False]\n\ndisplay.html.use_mathjax : boolean\n When True, Jupyter notebook will process table contents using MathJax,\n rendering mathematical expressions enclosed by the dollar symbol.\n (default: True)\n [default: True] [currently: True]\n\ndisplay.large_repr : 'truncate'/'info'\n For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can\n show a truncated table (the default from 0.13), or switch to the view from\n df.info() (the behaviour in earlier versions of pandas).\n [default: truncate] [currently: truncate]\n\ndisplay.latex.escape : bool\n This specifies if the to_latex method of a Dataframe uses escapes special\n characters.\n Valid values: False,True\n [default: True] [currently: True]\n\ndisplay.latex.longtable :bool\n This specifies if the to_latex method of a Dataframe uses the longtable\n format.\n Valid values: False,True\n [default: False] [currently: False]\n\ndisplay.latex.multicolumn : bool\n This specifies if the to_latex method of a Dataframe uses multicolumns\n to pretty-print MultiIndex columns.\n Valid values: False,True\n [default: True] [currently: True]\n\ndisplay.latex.multicolumn_format : bool\n This specifies if the to_latex method of a Dataframe uses multicolumns\n to pretty-print MultiIndex columns.\n Valid values: False,True\n [default: l] [currently: l]\n\ndisplay.latex.multirow : bool\n This specifies if the to_latex method of a Dataframe uses multirows\n to pretty-print MultiIndex rows.\n Valid values: False,True\n [default: False] [currently: False]\n\ndisplay.latex.repr : boolean\n Whether to produce a latex DataFrame representation for jupyter\n environments that support it.\n (default: False)\n [default: False] [currently: False]\n\ndisplay.max_categories : int\n This sets the maximum number of categories pandas should output when\n printing out a `Categorical` or a Series of dtype \"category\".\n [default: 8] [currently: 8]\n\ndisplay.max_columns : int\n If max_cols is exceeded, switch to truncate view. Depending on\n `large_repr`, objects are either centrally truncated or printed as\n a summary view. 'None' value means unlimited.\n\n In case python/IPython is running in a terminal and `large_repr`\n equals 'truncate' this can be set to 0 and pandas will auto-detect\n the width of the terminal and print a truncated object which fits\n the screen width. The IPython notebook, IPython qtconsole, or IDLE\n do not run in a terminal and hence it is not possible to do\n correct auto-detection.\n [default: 0] [currently: 0]\n\ndisplay.max_colwidth : int\n The maximum width in characters of a column in the repr of\n a pandas data structure. When the column overflows, a \"...\"\n placeholder is embedded in the output.\n [default: 50] [currently: 50]\n\ndisplay.max_info_columns : int\n max_info_columns is used in DataFrame.info method to decide if\n per column information will be printed.\n [default: 100] [currently: 100]\n\ndisplay.max_info_rows : int or None\n df.info() will usually show null-counts for each column.\n For large frames this can be quite slow. max_info_rows and max_info_cols\n limit this null check only to frames with smaller dimensions than\n specified.\n [default: 1690785] [currently: 1690785]\n\ndisplay.max_rows : int\n If max_rows is exceeded, switch to truncate view. Depending on\n `large_repr`, objects are either centrally truncated or printed as\n a summary view. 'None' value means unlimited.\n\n In case python/IPython is running in a terminal and `large_repr`\n equals 'truncate' this can be set to 0 and pandas will auto-detect\n the height of the terminal and print a truncated object which fits\n the screen height. The IPython notebook, IPython qtconsole, or\n IDLE do not run in a terminal and hence it is not possible to do\n correct auto-detection.\n [default: 60] [currently: 60]\n\ndisplay.max_seq_items : int or None\n when pretty-printing a long sequence, no more then `max_seq_items`\n will be printed. If items are omitted, they will be denoted by the\n addition of \"...\" to the resulting string.\n\n If set to None, the number of items to be printed is unlimited.\n [default: 100] [currently: 100]\n\ndisplay.memory_usage : bool, string or None\n This specifies if the memory usage of a DataFrame should be displayed when\n df.info() is called. Valid values True,False,'deep'\n [default: True] [currently: True]\n\ndisplay.multi_sparse : boolean\n \"sparsify\" MultiIndex display (don't display repeated\n elements in outer levels within groups)\n [default: True] [currently: True]\n\ndisplay.notebook_repr_html : boolean\n When True, IPython notebook will use html representation for\n pandas objects (if it is available).\n [default: True] [currently: True]\n\ndisplay.pprint_nest_depth : int\n Controls the number of nested levels to process when pretty-printing\n [default: 3] [currently: 3]\n\ndisplay.precision : int\n Floating point output precision (number of significant digits). This is\n only a suggestion\n [default: 6] [currently: 6]\n\ndisplay.show_dimensions : boolean or 'truncate'\n Whether to print out dimensions at the end of DataFrame repr.\n If 'truncate' is specified, only print out the dimensions if the\n frame is truncated (e.g. not display all rows and/or columns)\n [default: truncate] [currently: truncate]\n\ndisplay.unicode.ambiguous_as_wide : boolean\n Whether to use the Unicode East Asian Width to calculate the display text\n width.\n Enabling this may affect to the performance (default: False)\n [default: False] [currently: False]\n\ndisplay.unicode.east_asian_width : boolean\n Whether to use the Unicode East Asian Width to calculate the display text\n width.\n Enabling this may affect to the performance (default: False)\n [default: False] [currently: False]\n\ndisplay.width : int\n Width of the display in characters. In case python/IPython is running in\n a terminal this can be set to None and pandas will correctly auto-detect\n the width.\n Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a\n terminal and hence it is not possible to correctly detect the width.\n [default: 80] [currently: 80]\n\nhtml.border : int\n A ``border=value`` attribute is inserted in the ``<table>`` tag\n for the DataFrame HTML repr.\n [default: 1] [currently: 1]\n (Deprecated, use `display.html.border` instead.)\n\nio.excel.xls.writer : string\n The default Excel writer engine for 'xls' files. Available options:\n auto, xlwt.\n [default: auto] [currently: auto]\n\nio.excel.xlsm.writer : string\n The default Excel writer engine for 'xlsm' files. Available options:\n auto, openpyxl.\n [default: auto] [currently: auto]\n\nio.excel.xlsx.writer : string\n The default Excel writer engine for 'xlsx' files. Available options:\n auto, openpyxl, xlsxwriter.\n [default: auto] [currently: auto]\n\nio.hdf.default_format : format\n default format writing format, if None, then\n put will default to 'fixed' and append will default to 'table'\n [default: None] [currently: None]\n\nio.hdf.dropna_table : boolean\n drop ALL nan rows when appending to a table\n [default: False] [currently: False]\n\nio.parquet.engine : string\n The default parquet reader/writer engine. Available options:\n 'auto', 'pyarrow', 'fastparquet', the default is 'auto'\n [default: auto] [currently: auto]\n\nmode.chained_assignment : string\n Raise an exception, warn, or no action if trying to use chained assignment,\n The default is warn\n [default: warn] [currently: warn]\n\nmode.sim_interactive : boolean\n Whether to simulate interactive mode for purposes of testing\n [default: False] [currently: False]\n\nmode.use_inf_as_na : boolean\n True means treat None, NaN, INF, -INF as NA (old way),\n False means None and NaN are null, but INF, -INF are not NA\n (new way).\n [default: False] [currently: False]\n\nmode.use_inf_as_null : boolean\n use_inf_as_null had been deprecated and will be removed in a future\n version. Use `use_inf_as_na` instead.\n [default: False] [currently: False]\n (Deprecated, use `mode.use_inf_as_na` instead.)\n\nplotting.matplotlib.register_converters : bool\n Whether to register converters with matplotlib's units registry for\n dates, times, datetimes, and Periods. Toggling to False will remove\n the converters, restoring any converters that pandas overwrote.\n [default: True] [currently: True]", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"], ["PR01", "Parameters {**kwds, *args} not documented"], ["PR02", "Unknown parameters {pat}"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Working with options", "subsection": "", "shared_code_with": "pandas.describe_option"}, "pandas.get_option": {"type": "CallableDynamicDoc", "docstring": "get_option(pat)\n\nRetrieves the value of the specified option.\n\nAvailable options:\n\n- compute.[use_bottleneck, use_numexpr]\n- display.[chop_threshold, colheader_justify, column_space, date_dayfirst,\n date_yearfirst, encoding, expand_frame_repr, float_format]\n- display.html.[border, table_schema, use_mathjax]\n- display.[large_repr]\n- display.latex.[escape, longtable, multicolumn, multicolumn_format, multirow,\n repr]\n- display.[max_categories, max_columns, max_colwidth, max_info_columns,\n max_info_rows, max_rows, max_seq_items, memory_usage, multi_sparse,\n notebook_repr_html, pprint_nest_depth, precision, show_dimensions]\n- display.unicode.[ambiguous_as_wide, east_asian_width]\n- display.[width]\n- html.[border]\n- io.excel.xls.[writer]\n- io.excel.xlsm.[writer]\n- io.excel.xlsx.[writer]\n- io.hdf.[default_format, dropna_table]\n- io.parquet.[engine]\n- mode.[chained_assignment, sim_interactive, use_inf_as_na, use_inf_as_null]\n- plotting.matplotlib.[register_converters]\n\nParameters\n----------\npat : str\n Regexp which should match a single option.\n Note: partial matches are supported for convenience, but unless you use the\n full option name (e.g. x.y.z.option_name), your code may break in future\n versions if new options with similar names are introduced.\n\nReturns\n-------\nresult : the value of the option\n\nRaises\n------\nOptionError : if no such option exists\n\nNotes\n-----\nThe available options with its descriptions:\n\ncompute.use_bottleneck : bool\n Use the bottleneck library to accelerate if it is installed,\n the default is True\n Valid values: False,True\n [default: True] [currently: True]\n\ncompute.use_numexpr : bool\n Use the numexpr library to accelerate computation if it is installed,\n the default is True\n Valid values: False,True\n [default: True] [currently: True]\n\ndisplay.chop_threshold : float or None\n if set to a float value, all float values smaller then the given threshold\n will be displayed as exactly 0 by repr and friends.\n [default: None] [currently: None]\n\ndisplay.colheader_justify : 'left'/'right'\n Controls the justification of column headers. used by DataFrameFormatter.\n [default: right] [currently: right]\n\ndisplay.column_space No description available.\n [default: 12] [currently: 12]\n\ndisplay.date_dayfirst : boolean\n When True, prints and parses dates with the day first, eg 20/01/2005\n [default: False] [currently: False]\n\ndisplay.date_yearfirst : boolean\n When True, prints and parses dates with the year first, eg 2005/01/20\n [default: False] [currently: False]\n\ndisplay.encoding : str/unicode\n Defaults to the detected encoding of the console.\n Specifies the encoding to be used for strings returned by to_string,\n these are generally strings meant to be displayed on the console.\n [default: UTF-8] [currently: UTF-8]\n\ndisplay.expand_frame_repr : boolean\n Whether to print out the full DataFrame repr for wide DataFrames across\n multiple lines, `max_columns` is still respected, but the output will\n wrap-around across multiple \"pages\" if its width exceeds `display.width`.\n [default: True] [currently: True]\n\ndisplay.float_format : callable\n The callable should accept a floating point number and return\n a string with the desired format of the number. This is used\n in some places like SeriesFormatter.\n See formats.format.EngFormatter for an example.\n [default: None] [currently: None]\n\ndisplay.html.border : int\n A ``border=value`` attribute is inserted in the ``<table>`` tag\n for the DataFrame HTML repr.\n [default: 1] [currently: 1]\n\ndisplay.html.table_schema : boolean\n Whether to publish a Table Schema representation for frontends\n that support it.\n (default: False)\n [default: False] [currently: False]\n\ndisplay.html.use_mathjax : boolean\n When True, Jupyter notebook will process table contents using MathJax,\n rendering mathematical expressions enclosed by the dollar symbol.\n (default: True)\n [default: True] [currently: True]\n\ndisplay.large_repr : 'truncate'/'info'\n For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can\n show a truncated table (the default from 0.13), or switch to the view from\n df.info() (the behaviour in earlier versions of pandas).\n [default: truncate] [currently: truncate]\n\ndisplay.latex.escape : bool\n This specifies if the to_latex method of a Dataframe uses escapes special\n characters.\n Valid values: False,True\n [default: True] [currently: True]\n\ndisplay.latex.longtable :bool\n This specifies if the to_latex method of a Dataframe uses the longtable\n format.\n Valid values: False,True\n [default: False] [currently: False]\n\ndisplay.latex.multicolumn : bool\n This specifies if the to_latex method of a Dataframe uses multicolumns\n to pretty-print MultiIndex columns.\n Valid values: False,True\n [default: True] [currently: True]\n\ndisplay.latex.multicolumn_format : bool\n This specifies if the to_latex method of a Dataframe uses multicolumns\n to pretty-print MultiIndex columns.\n Valid values: False,True\n [default: l] [currently: l]\n\ndisplay.latex.multirow : bool\n This specifies if the to_latex method of a Dataframe uses multirows\n to pretty-print MultiIndex rows.\n Valid values: False,True\n [default: False] [currently: False]\n\ndisplay.latex.repr : boolean\n Whether to produce a latex DataFrame representation for jupyter\n environments that support it.\n (default: False)\n [default: False] [currently: False]\n\ndisplay.max_categories : int\n This sets the maximum number of categories pandas should output when\n printing out a `Categorical` or a Series of dtype \"category\".\n [default: 8] [currently: 8]\n\ndisplay.max_columns : int\n If max_cols is exceeded, switch to truncate view. Depending on\n `large_repr`, objects are either centrally truncated or printed as\n a summary view. 'None' value means unlimited.\n\n In case python/IPython is running in a terminal and `large_repr`\n equals 'truncate' this can be set to 0 and pandas will auto-detect\n the width of the terminal and print a truncated object which fits\n the screen width. The IPython notebook, IPython qtconsole, or IDLE\n do not run in a terminal and hence it is not possible to do\n correct auto-detection.\n [default: 0] [currently: 0]\n\ndisplay.max_colwidth : int\n The maximum width in characters of a column in the repr of\n a pandas data structure. When the column overflows, a \"...\"\n placeholder is embedded in the output.\n [default: 50] [currently: 50]\n\ndisplay.max_info_columns : int\n max_info_columns is used in DataFrame.info method to decide if\n per column information will be printed.\n [default: 100] [currently: 100]\n\ndisplay.max_info_rows : int or None\n df.info() will usually show null-counts for each column.\n For large frames this can be quite slow. max_info_rows and max_info_cols\n limit this null check only to frames with smaller dimensions than\n specified.\n [default: 1690785] [currently: 1690785]\n\ndisplay.max_rows : int\n If max_rows is exceeded, switch to truncate view. Depending on\n `large_repr`, objects are either centrally truncated or printed as\n a summary view. 'None' value means unlimited.\n\n In case python/IPython is running in a terminal and `large_repr`\n equals 'truncate' this can be set to 0 and pandas will auto-detect\n the height of the terminal and print a truncated object which fits\n the screen height. The IPython notebook, IPython qtconsole, or\n IDLE do not run in a terminal and hence it is not possible to do\n correct auto-detection.\n [default: 60] [currently: 60]\n\ndisplay.max_seq_items : int or None\n when pretty-printing a long sequence, no more then `max_seq_items`\n will be printed. If items are omitted, they will be denoted by the\n addition of \"...\" to the resulting string.\n\n If set to None, the number of items to be printed is unlimited.\n [default: 100] [currently: 100]\n\ndisplay.memory_usage : bool, string or None\n This specifies if the memory usage of a DataFrame should be displayed when\n df.info() is called. Valid values True,False,'deep'\n [default: True] [currently: True]\n\ndisplay.multi_sparse : boolean\n \"sparsify\" MultiIndex display (don't display repeated\n elements in outer levels within groups)\n [default: True] [currently: True]\n\ndisplay.notebook_repr_html : boolean\n When True, IPython notebook will use html representation for\n pandas objects (if it is available).\n [default: True] [currently: True]\n\ndisplay.pprint_nest_depth : int\n Controls the number of nested levels to process when pretty-printing\n [default: 3] [currently: 3]\n\ndisplay.precision : int\n Floating point output precision (number of significant digits). This is\n only a suggestion\n [default: 6] [currently: 6]\n\ndisplay.show_dimensions : boolean or 'truncate'\n Whether to print out dimensions at the end of DataFrame repr.\n If 'truncate' is specified, only print out the dimensions if the\n frame is truncated (e.g. not display all rows and/or columns)\n [default: truncate] [currently: truncate]\n\ndisplay.unicode.ambiguous_as_wide : boolean\n Whether to use the Unicode East Asian Width to calculate the display text\n width.\n Enabling this may affect to the performance (default: False)\n [default: False] [currently: False]\n\ndisplay.unicode.east_asian_width : boolean\n Whether to use the Unicode East Asian Width to calculate the display text\n width.\n Enabling this may affect to the performance (default: False)\n [default: False] [currently: False]\n\ndisplay.width : int\n Width of the display in characters. In case python/IPython is running in\n a terminal this can be set to None and pandas will correctly auto-detect\n the width.\n Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a\n terminal and hence it is not possible to correctly detect the width.\n [default: 80] [currently: 80]\n\nhtml.border : int\n A ``border=value`` attribute is inserted in the ``<table>`` tag\n for the DataFrame HTML repr.\n [default: 1] [currently: 1]\n (Deprecated, use `display.html.border` instead.)\n\nio.excel.xls.writer : string\n The default Excel writer engine for 'xls' files. Available options:\n auto, xlwt.\n [default: auto] [currently: auto]\n\nio.excel.xlsm.writer : string\n The default Excel writer engine for 'xlsm' files. Available options:\n auto, openpyxl.\n [default: auto] [currently: auto]\n\nio.excel.xlsx.writer : string\n The default Excel writer engine for 'xlsx' files. Available options:\n auto, openpyxl, xlsxwriter.\n [default: auto] [currently: auto]\n\nio.hdf.default_format : format\n default format writing format, if None, then\n put will default to 'fixed' and append will default to 'table'\n [default: None] [currently: None]\n\nio.hdf.dropna_table : boolean\n drop ALL nan rows when appending to a table\n [default: False] [currently: False]\n\nio.parquet.engine : string\n The default parquet reader/writer engine. Available options:\n 'auto', 'pyarrow', 'fastparquet', the default is 'auto'\n [default: auto] [currently: auto]\n\nmode.chained_assignment : string\n Raise an exception, warn, or no action if trying to use chained assignment,\n The default is warn\n [default: warn] [currently: warn]\n\nmode.sim_interactive : boolean\n Whether to simulate interactive mode for purposes of testing\n [default: False] [currently: False]\n\nmode.use_inf_as_na : boolean\n True means treat None, NaN, INF, -INF as NA (old way),\n False means None and NaN are null, but INF, -INF are not NA\n (new way).\n [default: False] [currently: False]\n\nmode.use_inf_as_null : boolean\n use_inf_as_null had been deprecated and will be removed in a future\n version. Use `use_inf_as_na` instead.\n [default: False] [currently: False]\n (Deprecated, use `mode.use_inf_as_na` instead.)\n\nplotting.matplotlib.register_converters : bool\n Whether to register converters with matplotlib's units registry for\n dates, times, datetimes, and Periods. Toggling to False will remove\n the converters, restoring any converters that pandas overwrote.\n [default: True] [currently: True]", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"], ["PR01", "Parameters {**kwds, *args} not documented"], ["PR02", "Unknown parameters {pat}"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Working with options", "subsection": "", "shared_code_with": "pandas.reset_option"}, "pandas.set_option": {"type": "CallableDynamicDoc", "docstring": "set_option(pat, value)\n\nSets the value of the specified option.\n\nAvailable options:\n\n- compute.[use_bottleneck, use_numexpr]\n- display.[chop_threshold, colheader_justify, column_space, date_dayfirst,\n date_yearfirst, encoding, expand_frame_repr, float_format]\n- display.html.[border, table_schema, use_mathjax]\n- display.[large_repr]\n- display.latex.[escape, longtable, multicolumn, multicolumn_format, multirow,\n repr]\n- display.[max_categories, max_columns, max_colwidth, max_info_columns,\n max_info_rows, max_rows, max_seq_items, memory_usage, multi_sparse,\n notebook_repr_html, pprint_nest_depth, precision, show_dimensions]\n- display.unicode.[ambiguous_as_wide, east_asian_width]\n- display.[width]\n- html.[border]\n- io.excel.xls.[writer]\n- io.excel.xlsm.[writer]\n- io.excel.xlsx.[writer]\n- io.hdf.[default_format, dropna_table]\n- io.parquet.[engine]\n- mode.[chained_assignment, sim_interactive, use_inf_as_na, use_inf_as_null]\n- plotting.matplotlib.[register_converters]\n\nParameters\n----------\npat : str\n Regexp which should match a single option.\n Note: partial matches are supported for convenience, but unless you use the\n full option name (e.g. x.y.z.option_name), your code may break in future\n versions if new options with similar names are introduced.\nvalue :\n new value of option.\n\nReturns\n-------\nNone\n\nRaises\n------\nOptionError if no such option exists\n\nNotes\n-----\nThe available options with its descriptions:\n\ncompute.use_bottleneck : bool\n Use the bottleneck library to accelerate if it is installed,\n the default is True\n Valid values: False,True\n [default: True] [currently: True]\n\ncompute.use_numexpr : bool\n Use the numexpr library to accelerate computation if it is installed,\n the default is True\n Valid values: False,True\n [default: True] [currently: True]\n\ndisplay.chop_threshold : float or None\n if set to a float value, all float values smaller then the given threshold\n will be displayed as exactly 0 by repr and friends.\n [default: None] [currently: None]\n\ndisplay.colheader_justify : 'left'/'right'\n Controls the justification of column headers. used by DataFrameFormatter.\n [default: right] [currently: right]\n\ndisplay.column_space No description available.\n [default: 12] [currently: 12]\n\ndisplay.date_dayfirst : boolean\n When True, prints and parses dates with the day first, eg 20/01/2005\n [default: False] [currently: False]\n\ndisplay.date_yearfirst : boolean\n When True, prints and parses dates with the year first, eg 2005/01/20\n [default: False] [currently: False]\n\ndisplay.encoding : str/unicode\n Defaults to the detected encoding of the console.\n Specifies the encoding to be used for strings returned by to_string,\n these are generally strings meant to be displayed on the console.\n [default: UTF-8] [currently: UTF-8]\n\ndisplay.expand_frame_repr : boolean\n Whether to print out the full DataFrame repr for wide DataFrames across\n multiple lines, `max_columns` is still respected, but the output will\n wrap-around across multiple \"pages\" if its width exceeds `display.width`.\n [default: True] [currently: True]\n\ndisplay.float_format : callable\n The callable should accept a floating point number and return\n a string with the desired format of the number. This is used\n in some places like SeriesFormatter.\n See formats.format.EngFormatter for an example.\n [default: None] [currently: None]\n\ndisplay.html.border : int\n A ``border=value`` attribute is inserted in the ``<table>`` tag\n for the DataFrame HTML repr.\n [default: 1] [currently: 1]\n\ndisplay.html.table_schema : boolean\n Whether to publish a Table Schema representation for frontends\n that support it.\n (default: False)\n [default: False] [currently: False]\n\ndisplay.html.use_mathjax : boolean\n When True, Jupyter notebook will process table contents using MathJax,\n rendering mathematical expressions enclosed by the dollar symbol.\n (default: True)\n [default: True] [currently: True]\n\ndisplay.large_repr : 'truncate'/'info'\n For DataFrames exceeding max_rows/max_cols, the repr (and HTML repr) can\n show a truncated table (the default from 0.13), or switch to the view from\n df.info() (the behaviour in earlier versions of pandas).\n [default: truncate] [currently: truncate]\n\ndisplay.latex.escape : bool\n This specifies if the to_latex method of a Dataframe uses escapes special\n characters.\n Valid values: False,True\n [default: True] [currently: True]\n\ndisplay.latex.longtable :bool\n This specifies if the to_latex method of a Dataframe uses the longtable\n format.\n Valid values: False,True\n [default: False] [currently: False]\n\ndisplay.latex.multicolumn : bool\n This specifies if the to_latex method of a Dataframe uses multicolumns\n to pretty-print MultiIndex columns.\n Valid values: False,True\n [default: True] [currently: True]\n\ndisplay.latex.multicolumn_format : bool\n This specifies if the to_latex method of a Dataframe uses multicolumns\n to pretty-print MultiIndex columns.\n Valid values: False,True\n [default: l] [currently: l]\n\ndisplay.latex.multirow : bool\n This specifies if the to_latex method of a Dataframe uses multirows\n to pretty-print MultiIndex rows.\n Valid values: False,True\n [default: False] [currently: False]\n\ndisplay.latex.repr : boolean\n Whether to produce a latex DataFrame representation for jupyter\n environments that support it.\n (default: False)\n [default: False] [currently: False]\n\ndisplay.max_categories : int\n This sets the maximum number of categories pandas should output when\n printing out a `Categorical` or a Series of dtype \"category\".\n [default: 8] [currently: 8]\n\ndisplay.max_columns : int\n If max_cols is exceeded, switch to truncate view. Depending on\n `large_repr`, objects are either centrally truncated or printed as\n a summary view. 'None' value means unlimited.\n\n In case python/IPython is running in a terminal and `large_repr`\n equals 'truncate' this can be set to 0 and pandas will auto-detect\n the width of the terminal and print a truncated object which fits\n the screen width. The IPython notebook, IPython qtconsole, or IDLE\n do not run in a terminal and hence it is not possible to do\n correct auto-detection.\n [default: 0] [currently: 0]\n\ndisplay.max_colwidth : int\n The maximum width in characters of a column in the repr of\n a pandas data structure. When the column overflows, a \"...\"\n placeholder is embedded in the output.\n [default: 50] [currently: 50]\n\ndisplay.max_info_columns : int\n max_info_columns is used in DataFrame.info method to decide if\n per column information will be printed.\n [default: 100] [currently: 100]\n\ndisplay.max_info_rows : int or None\n df.info() will usually show null-counts for each column.\n For large frames this can be quite slow. max_info_rows and max_info_cols\n limit this null check only to frames with smaller dimensions than\n specified.\n [default: 1690785] [currently: 1690785]\n\ndisplay.max_rows : int\n If max_rows is exceeded, switch to truncate view. Depending on\n `large_repr`, objects are either centrally truncated or printed as\n a summary view. 'None' value means unlimited.\n\n In case python/IPython is running in a terminal and `large_repr`\n equals 'truncate' this can be set to 0 and pandas will auto-detect\n the height of the terminal and print a truncated object which fits\n the screen height. The IPython notebook, IPython qtconsole, or\n IDLE do not run in a terminal and hence it is not possible to do\n correct auto-detection.\n [default: 60] [currently: 60]\n\ndisplay.max_seq_items : int or None\n when pretty-printing a long sequence, no more then `max_seq_items`\n will be printed. If items are omitted, they will be denoted by the\n addition of \"...\" to the resulting string.\n\n If set to None, the number of items to be printed is unlimited.\n [default: 100] [currently: 100]\n\ndisplay.memory_usage : bool, string or None\n This specifies if the memory usage of a DataFrame should be displayed when\n df.info() is called. Valid values True,False,'deep'\n [default: True] [currently: True]\n\ndisplay.multi_sparse : boolean\n \"sparsify\" MultiIndex display (don't display repeated\n elements in outer levels within groups)\n [default: True] [currently: True]\n\ndisplay.notebook_repr_html : boolean\n When True, IPython notebook will use html representation for\n pandas objects (if it is available).\n [default: True] [currently: True]\n\ndisplay.pprint_nest_depth : int\n Controls the number of nested levels to process when pretty-printing\n [default: 3] [currently: 3]\n\ndisplay.precision : int\n Floating point output precision (number of significant digits). This is\n only a suggestion\n [default: 6] [currently: 6]\n\ndisplay.show_dimensions : boolean or 'truncate'\n Whether to print out dimensions at the end of DataFrame repr.\n If 'truncate' is specified, only print out the dimensions if the\n frame is truncated (e.g. not display all rows and/or columns)\n [default: truncate] [currently: truncate]\n\ndisplay.unicode.ambiguous_as_wide : boolean\n Whether to use the Unicode East Asian Width to calculate the display text\n width.\n Enabling this may affect to the performance (default: False)\n [default: False] [currently: False]\n\ndisplay.unicode.east_asian_width : boolean\n Whether to use the Unicode East Asian Width to calculate the display text\n width.\n Enabling this may affect to the performance (default: False)\n [default: False] [currently: False]\n\ndisplay.width : int\n Width of the display in characters. In case python/IPython is running in\n a terminal this can be set to None and pandas will correctly auto-detect\n the width.\n Note that the IPython notebook, IPython qtconsole, or IDLE do not run in a\n terminal and hence it is not possible to correctly detect the width.\n [default: 80] [currently: 80]\n\nhtml.border : int\n A ``border=value`` attribute is inserted in the ``<table>`` tag\n for the DataFrame HTML repr.\n [default: 1] [currently: 1]\n (Deprecated, use `display.html.border` instead.)\n\nio.excel.xls.writer : string\n The default Excel writer engine for 'xls' files. Available options:\n auto, xlwt.\n [default: auto] [currently: auto]\n\nio.excel.xlsm.writer : string\n The default Excel writer engine for 'xlsm' files. Available options:\n auto, openpyxl.\n [default: auto] [currently: auto]\n\nio.excel.xlsx.writer : string\n The default Excel writer engine for 'xlsx' files. Available options:\n auto, openpyxl, xlsxwriter.\n [default: auto] [currently: auto]\n\nio.hdf.default_format : format\n default format writing format, if None, then\n put will default to 'fixed' and append will default to 'table'\n [default: None] [currently: None]\n\nio.hdf.dropna_table : boolean\n drop ALL nan rows when appending to a table\n [default: False] [currently: False]\n\nio.parquet.engine : string\n The default parquet reader/writer engine. Available options:\n 'auto', 'pyarrow', 'fastparquet', the default is 'auto'\n [default: auto] [currently: auto]\n\nmode.chained_assignment : string\n Raise an exception, warn, or no action if trying to use chained assignment,\n The default is warn\n [default: warn] [currently: warn]\n\nmode.sim_interactive : boolean\n Whether to simulate interactive mode for purposes of testing\n [default: False] [currently: False]\n\nmode.use_inf_as_na : boolean\n True means treat None, NaN, INF, -INF as NA (old way),\n False means None and NaN are null, but INF, -INF are not NA\n (new way).\n [default: False] [currently: False]\n\nmode.use_inf_as_null : boolean\n use_inf_as_null had been deprecated and will be removed in a future\n version. Use `use_inf_as_na` instead.\n [default: False] [currently: False]\n (Deprecated, use `mode.use_inf_as_na` instead.)\n\nplotting.matplotlib.register_converters : bool\n Whether to register converters with matplotlib's units registry for\n dates, times, datetimes, and Periods. Toggling to False will remove\n the converters, restoring any converters that pandas overwrote.\n [default: True] [currently: True]", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"], ["PR01", "Parameters {**kwds, *args} not documented"], ["PR02", "Unknown parameters {value :, pat}"], ["PR10", "Parameter \"value \" requires a space before the colon separating the parameter name and type"], ["PR08", "Parameter \"value :\" description should start with a capital letter"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Working with options", "subsection": "", "shared_code_with": "pandas.get_option"}, "pandas.option_context": {"type": "type", "docstring": "Context manager to temporarily set options in the `with` statement context.\n\nYou need to invoke as ``option_context(pat, val, [(pat, val), ...])``.\n\nExamples\n--------\n\n>>> with option_context('display.max_rows', 10, 'display.max_columns', 5):\n... ...", "deprecated": false, "file": "pandas/core/config.py", "file_line": 377, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/config.py#L377", "errors": [["PR01", "Parameters {*args} not documented"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 9, in pandas.option_context\nFailed example:\n with option_context('display.max_rows', 10, 'display.max_columns', 5):\n ...\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.option_context[0]>\", line 1, in <module>\n with option_context('display.max_rows', 10, 'display.max_columns', 5):\n NameError: name 'option_context' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'option_context'"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 9, in pandas.option_context\nFailed example:\n with option_context('display.max_rows', 10, 'display.max_columns', 5):\n ...\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.option_context[0]>\", line 1, in <module>\n with option_context('display.max_rows', 10, 'display.max_columns', 5):\n NameError: name 'option_context' is not defined\n", "in_api": true, "section": "Working with options", "subsection": "", "shared_code_with": ""}, "pandas.testing.assert_frame_equal": {"type": "function", "docstring": "Check that left and right DataFrame are equal.\n\nThis function is intended to compare two DataFrames and output any\ndifferences. Is is mostly intended for use in unit tests.\nAdditional parameters allow varying the strictness of the\nequality checks performed.\n\nParameters\n----------\nleft : DataFrame\n First DataFrame to compare.\nright : DataFrame\n Second DataFrame to compare.\ncheck_dtype : bool, default True\n Whether to check the DataFrame dtype is identical.\ncheck_index_type : bool / string {'equiv'}, default 'equiv'\n Whether to check the Index class, dtype and inferred_type\n are identical.\ncheck_column_type : bool / string {'equiv'}, default 'equiv'\n Whether to check the columns class, dtype and inferred_type\n are identical. Is passed as the ``exact`` argument of\n :func:`assert_index_equal`.\ncheck_frame_type : bool, default True\n Whether to check the DataFrame class is identical.\ncheck_less_precise : bool or int, default False\n Specify comparison precision. Only used when check_exact is False.\n 5 digits (False) or 3 digits (True) after decimal points are compared.\n If int, then specify the digits to compare.\ncheck_names : bool, default True\n Whether to check that the `names` attribute for both the `index`\n and `column` attributes of the DataFrame is identical, i.e.\n\n * left.index.names == right.index.names\n * left.columns.names == right.columns.names\nby_blocks : bool, default False\n Specify how to compare internal data. If False, compare by columns.\n If True, compare by blocks.\ncheck_exact : bool, default False\n Whether to compare number exactly.\ncheck_datetimelike_compat : bool, default False\n Compare datetime-like which is comparable ignoring dtype.\ncheck_categorical : bool, default True\n Whether to compare internal Categorical exactly.\ncheck_like : bool, default False\n If True, ignore the order of index & columns.\n Note: index labels must match their respective rows\n (same as in columns) - same labels must be with the same data.\nobj : str, default 'DataFrame'\n Specify object name being compared, internally used to show appropriate\n assertion message.\n\nSee Also\n--------\nassert_series_equal : Equivalent method for asserting Series equality.\nDataFrame.equals : Check DataFrame equality.\n\nExamples\n--------\nThis example shows comparing two DataFrames that are equal\nbut with columns of differing dtypes.\n\n>>> from pandas.util.testing import assert_frame_equal\n>>> df1 = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})\n>>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]})\n\ndf1 equals itself.\n>>> assert_frame_equal(df1, df1)\n\ndf1 differs from df2 as column 'b' is of a different type.\n>>> assert_frame_equal(df1, df2)\nTraceback (most recent call last):\nAssertionError: Attributes are different\n\nAttribute \"dtype\" are different\n[left]: int64\n[right]: float64\n\nIgnore differing dtypes in columns with check_dtype.\n>>> assert_frame_equal(df1, df2, check_dtype=False)", "deprecated": false, "file": "pandas/util/testing.py", "file_line": 1349, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/util/testing.py#L1349", "errors": [["PR06", "Parameter \"check_index_type\" type should use \"str\" instead of \"string\""], ["PR06", "Parameter \"check_column_type\" type should use \"str\" instead of \"string\""], ["PR09", "Parameter \"check_names\" description should finish with \".\""]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Testing functions", "subsection": "", "shared_code_with": ""}, "pandas.testing.assert_series_equal": {"type": "function", "docstring": "Check that left and right Series are equal.\n\nParameters\n----------\nleft : Series\nright : Series\ncheck_dtype : bool, default True\n Whether to check the Series dtype is identical.\ncheck_index_type : bool / string {'equiv'}, default 'equiv'\n Whether to check the Index class, dtype and inferred_type\n are identical.\ncheck_series_type : bool, default True\n Whether to check the Series class is identical.\ncheck_less_precise : bool or int, default False\n Specify comparison precision. Only used when check_exact is False.\n 5 digits (False) or 3 digits (True) after decimal points are compared.\n If int, then specify the digits to compare.\ncheck_names : bool, default True\n Whether to check the Series and Index names attribute.\ncheck_exact : bool, default False\n Whether to compare number exactly.\ncheck_datetimelike_compat : bool, default False\n Compare datetime-like which is comparable ignoring dtype.\ncheck_categorical : bool, default True\n Whether to compare internal Categorical exactly.\nobj : str, default 'Series'\n Specify object name being compared, internally used to show appropriate\n assertion message.", "deprecated": false, "file": "pandas/util/testing.py", "file_line": 1223, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/util/testing.py#L1223", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["PR07", "Parameter \"left\" has no description"], ["PR07", "Parameter \"right\" has no description"], ["PR06", "Parameter \"check_index_type\" type should use \"str\" instead of \"string\""], ["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Testing functions", "subsection": "", "shared_code_with": ""}, "pandas.testing.assert_index_equal": {"type": "function", "docstring": "Check that left and right Index are equal.\n\nParameters\n----------\nleft : Index\nright : Index\nexact : bool / string {'equiv'}, default 'equiv'\n Whether to check the Index class, dtype and inferred_type\n are identical. If 'equiv', then RangeIndex can be substituted for\n Int64Index as well.\ncheck_names : bool, default True\n Whether to check the names attribute.\ncheck_less_precise : bool or int, default False\n Specify comparison precision. Only used when check_exact is False.\n 5 digits (False) or 3 digits (True) after decimal points are compared.\n If int, then specify the digits to compare\ncheck_exact : bool, default True\n Whether to compare number exactly.\ncheck_categorical : bool, default True\n Whether to compare internal Categorical exactly.\nobj : str, default 'Index'\n Specify object name being compared, internally used to show appropriate\n assertion message", "deprecated": false, "file": "pandas/util/testing.py", "file_line": 766, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/util/testing.py#L766", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["PR07", "Parameter \"left\" has no description"], ["PR07", "Parameter \"right\" has no description"], ["PR06", "Parameter \"exact\" type should use \"str\" instead of \"string\""], ["PR09", "Parameter \"check_less_precise\" description should finish with \".\""], ["PR09", "Parameter \"obj\" description should finish with \".\""], ["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Testing functions", "subsection": "", "shared_code_with": ""}, "pandas.errors.DtypeWarning": {"type": "type", "docstring": "Warning raised when reading different dtypes in a column from a file.\n\nRaised for a dtype incompatibility. This can happen whenever `read_csv`\nor `read_table` encounter non-uniform dtypes in a column(s) of a given\nCSV file.\n\nSee Also\n--------\npandas.read_csv : Read CSV (comma-separated) file into a DataFrame.\npandas.read_table : Read general delimited file into a DataFrame.\n\nNotes\n-----\nThis warning is issued when dealing with larger files because the dtype\nchecking happens per chunk read.\n\nDespite the warning, the CSV file is read with mixed types in a single\ncolumn which will be an object type. See the examples below to better\nunderstand this issue.\n\nExamples\n--------\nThis example creates and reads a large CSV file with a column that contains\n`int` and `str`.\n\n>>> df = pd.DataFrame({'a': (['1'] * 100000 + ['X'] * 100000 +\n... ['1'] * 100000),\n... 'b': ['b'] * 300000})\n>>> df.to_csv('test.csv', index=False)\n>>> df2 = pd.read_csv('test.csv')\n... # DtypeWarning: Columns (0) have mixed types\n\nImportant to notice that ``df2`` will contain both `str` and `int` for the\nsame input, '1'.\n\n>>> df2.iloc[262140, 0]\n'1'\n>>> type(df2.iloc[262140, 0])\n<class 'str'>\n>>> df2.iloc[262150, 0]\n1\n>>> type(df2.iloc[262150, 0])\n<class 'int'>\n\nOne way to solve this issue is using the `dtype` parameter in the\n`read_csv` and `read_table` functions to explicit the conversion:\n\n>>> df2 = pd.read_csv('test.csv', sep=',', dtype={'a': str})\n\nNo warning was issued.\n\n>>> import os\n>>> os.remove('test.csv')", "deprecated": false, "file": "pandas/errors/__init__.py", "file_line": 38, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/errors/__init__.py#L38", "errors": [["SA05", "pandas.read_csv in `See Also` section does not need `pandas` prefix, use read_csv instead."], ["SA05", "pandas.read_table in `See Also` section does not need `pandas` prefix, use read_table instead."]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Exceptions and warnings", "subsection": "", "shared_code_with": ""}, "pandas.errors.EmptyDataError": {"type": "type", "docstring": "Exception that is thrown in `pd.read_csv` (by both the C and\nPython engines) when empty data or header is encountered.", "deprecated": false, "file": "pandas/errors/__init__.py", "file_line": 96, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/errors/__init__.py#L96", "errors": [["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Exceptions and warnings", "subsection": "", "shared_code_with": ""}, "pandas.errors.OutOfBoundsDatetime": {"type": "type", "docstring": "Inappropriate argument value (of correct type).", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Exceptions and warnings", "subsection": "", "shared_code_with": "pandas.set_option"}, "pandas.errors.ParserError": {"type": "type", "docstring": "Exception that is raised by an error encountered in `pd.read_csv`.", "deprecated": false, "file": "pandas/errors/__init__.py", "file_line": 32, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/errors/__init__.py#L32", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Exceptions and warnings", "subsection": "", "shared_code_with": ""}, "pandas.errors.ParserWarning": {"type": "type", "docstring": "Warning raised when reading a file that doesn't use the default 'c' parser.\n\nRaised by `pd.read_csv` and `pd.read_table` when it is necessary to change\nparsers, generally from the default 'c' parser to 'python'.\n\nIt happens due to a lack of support or functionality for parsing a\nparticular attribute of a CSV file with the requested engine.\n\nCurrently, 'c' unsupported options include the following parameters:\n\n1. `sep` other than a single character (e.g. regex separators)\n2. `skipfooter` higher than 0\n3. `sep=None` with `delim_whitespace=False`\n\nThe warning can be avoided by adding `engine='python'` as a parameter in\n`pd.read_csv` and `pd.read_table` methods.\n\nSee Also\n--------\npd.read_csv : Read CSV (comma-separated) file into DataFrame.\npd.read_table : Read general delimited file into DataFrame.\n\nExamples\n--------\nUsing a `sep` in `pd.read_csv` other than a single character:\n\n>>> import io\n>>> csv = u'''a;b;c\n... 1;1,8\n... 1;2,1'''\n>>> df = pd.read_csv(io.StringIO(csv), sep='[;,]') # doctest: +SKIP\n... # ParserWarning: Falling back to the 'python' engine...\n\nAdding `engine='python'` to `pd.read_csv` removes the Warning:\n\n>>> df = pd.read_csv(io.StringIO(csv), sep='[;,]', engine='python')", "deprecated": false, "file": "pandas/errors/__init__.py", "file_line": 103, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/errors/__init__.py#L103", "errors": [], "warnings": [], "examples_errors": "", "in_api": true, "section": "Exceptions and warnings", "subsection": "", "shared_code_with": ""}, "pandas.errors.PerformanceWarning": {"type": "type", "docstring": "Warning raised when there is a possible\nperformance impact.", "deprecated": false, "file": "pandas/errors/__init__.py", "file_line": 10, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/errors/__init__.py#L10", "errors": [["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Exceptions and warnings", "subsection": "", "shared_code_with": ""}, "pandas.errors.UnsortedIndexError": {"type": "type", "docstring": "Error raised when attempting to get a slice of a MultiIndex,\nand the index has not been lexsorted. Subclass of `KeyError`.\n\n.. versionadded:: 0.20.0", "deprecated": false, "file": "pandas/errors/__init__.py", "file_line": 23, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/errors/__init__.py#L23", "errors": [["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Exceptions and warnings", "subsection": "", "shared_code_with": ""}, "pandas.errors.UnsupportedFunctionCall": {"type": "type", "docstring": "Exception raised when attempting to call a numpy function\non a pandas object, but that function is not supported by\nthe object e.g. ``np.cumsum(groupby_object)``.", "deprecated": false, "file": "pandas/errors/__init__.py", "file_line": 16, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/errors/__init__.py#L16", "errors": [["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Exceptions and warnings", "subsection": "", "shared_code_with": ""}, "pandas.api.types.union_categoricals": {"type": "function", "docstring": "Combine list-like of Categorical-like, unioning categories. All\ncategories must have the same dtype.\n\n.. versionadded:: 0.19.0\n\nParameters\n----------\nto_union : list-like of Categorical, CategoricalIndex,\n or Series with dtype='category'\nsort_categories : boolean, default False\n If true, resulting categories will be lexsorted, otherwise\n they will be ordered as they appear in the data.\nignore_order : boolean, default False\n If true, the ordered attribute of the Categoricals will be ignored.\n Results in an unordered categorical.\n\n .. versionadded:: 0.20.0\n\nReturns\n-------\nresult : Categorical\n\nRaises\n------\nTypeError\n - all inputs do not have the same dtype\n - all inputs do not have the same ordered property\n - all inputs are ordered and their categories are not identical\n - sort_categories=True and Categoricals are ordered\nValueError\n Empty list of categoricals passed\n\nNotes\n-----\n\nTo learn more about categories, see `link\n<http://pandas.pydata.org/pandas-docs/stable/categorical.html#unioning>`__\n\nExamples\n--------\n\n>>> from pandas.api.types import union_categoricals\n\nIf you want to combine categoricals that do not necessarily have\nthe same categories, `union_categoricals` will combine a list-like\nof categoricals. The new categories will be the union of the\ncategories being combined.\n\n>>> a = pd.Categorical([\"b\", \"c\"])\n>>> b = pd.Categorical([\"a\", \"b\"])\n>>> union_categoricals([a, b])\n[b, c, a, b]\nCategories (3, object): [b, c, a]\n\nBy default, the resulting categories will be ordered as they appear\nin the `categories` of the data. If you want the categories to be\nlexsorted, use `sort_categories=True` argument.\n\n>>> union_categoricals([a, b], sort_categories=True)\n[b, c, a, b]\nCategories (3, object): [a, b, c]\n\n`union_categoricals` also works with the case of combining two\ncategoricals of the same categories and order information (e.g. what\nyou could also `append` for).\n\n>>> a = pd.Categorical([\"a\", \"b\"], ordered=True)\n>>> b = pd.Categorical([\"a\", \"b\", \"a\"], ordered=True)\n>>> union_categoricals([a, b])\n[a, b, a, b, a]\nCategories (2, object): [a < b]\n\nRaises `TypeError` because the categories are ordered and not identical.\n\n>>> a = pd.Categorical([\"a\", \"b\"], ordered=True)\n>>> b = pd.Categorical([\"a\", \"b\", \"c\"], ordered=True)\n>>> union_categoricals([a, b])\nTypeError: to union ordered Categoricals, all categories must be the same\n\nNew in version 0.20.0\n\nOrdered categoricals with different categories or orderings can be\ncombined by using the `ignore_ordered=True` argument.\n\n>>> a = pd.Categorical([\"a\", \"b\", \"c\"], ordered=True)\n>>> b = pd.Categorical([\"c\", \"b\", \"a\"], ordered=True)\n>>> union_categoricals([a, b], ignore_order=True)\n[a, b, c, c, b, a]\nCategories (3, object): [a, b, c]\n\n`union_categoricals` also works with a `CategoricalIndex`, or `Series`\ncontaining categorical data, but note that the resulting array will\nalways be a plain `Categorical`\n\n>>> a = pd.Series([\"b\", \"c\"], dtype='category')\n>>> b = pd.Series([\"a\", \"b\"], dtype='category')\n>>> union_categoricals([a, b])\n[b, c, a, b]\nCategories (3, object): [b, c, a]", "deprecated": false, "file": "pandas/core/dtypes/concat.py", "file_line": 214, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/concat.py#L214", "errors": [["SS06", "Summary should fit in a single line"], ["PR08", "Parameter \"to_union\" description should start with a capital letter"], ["PR09", "Parameter \"to_union\" description should finish with \".\""], ["PR06", "Parameter \"sort_categories\" type should use \"bool\" instead of \"boolean\""], ["PR06", "Parameter \"ignore_order\" type should use \"bool\" instead of \"boolean\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 78, in pandas.api.types.union_categoricals\nFailed example:\n union_categoricals([a, b])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.union_categoricals[10]>\", line 1, in <module>\n union_categoricals([a, b])\n File \"/home/stijn_vanhoey/githubs/forks/pandas/pandas/core/dtypes/concat.py\", line 378, in union_categoricals\n raise TypeError(msg)\n TypeError: to union ordered Categoricals, all categories must be the same\n"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 78, in pandas.api.types.union_categoricals\nFailed example:\n union_categoricals([a, b])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.union_categoricals[10]>\", line 1, in <module>\n union_categoricals([a, b])\n File \"/home/stijn_vanhoey/githubs/forks/pandas/pandas/core/dtypes/concat.py\", line 378, in union_categoricals\n raise TypeError(msg)\n TypeError: to union ordered Categoricals, all categories must be the same\n", "in_api": true, "section": "Data types related functionality", "subsection": "", "shared_code_with": ""}, "pandas.api.types.infer_dtype": {"type": "builtin_function_or_method", "docstring": "Efficiently infer the type of a passed val, or list-like\narray of values. Return a string describing the type.\n\nParameters\n----------\nvalue : scalar, list, ndarray, or pandas type\nskipna : bool, default False\n Ignore NaN values when inferring the type.\n\n .. versionadded:: 0.21.0\n\nReturns\n-------\nstring describing the common type of the input data.\nResults can include:\n\n- string\n- unicode\n- bytes\n- floating\n- integer\n- mixed-integer\n- mixed-integer-float\n- decimal\n- complex\n- categorical\n- boolean\n- datetime64\n- datetime\n- date\n- timedelta64\n- timedelta\n- time\n- period\n- mixed\n\nRaises\n------\nTypeError if ndarray-like but cannot infer the dtype\n\nNotes\n-----\n- 'mixed' is the catchall for anything that is not otherwise\n specialized\n- 'mixed-integer-float' are floats and integers\n- 'mixed-integer' are integers mixed with non-integers\n\nExamples\n--------\n>>> infer_dtype(['foo', 'bar'])\n'string'\n\n>>> infer_dtype(['a', np.nan, 'b'], skipna=True)\n'string'\n\n>>> infer_dtype(['a', np.nan, 'b'], skipna=False)\n'mixed'\n\n>>> infer_dtype([b'foo', b'bar'])\n'bytes'\n\n>>> infer_dtype([1, 2, 3])\n'integer'\n\n>>> infer_dtype([1, 2, 3.5])\n'mixed-integer-float'\n\n>>> infer_dtype([1.0, 2.0, 3.5])\n'floating'\n\n>>> infer_dtype(['a', 1])\n'mixed-integer'\n\n>>> infer_dtype([Decimal(1), Decimal(2.0)])\n'decimal'\n\n>>> infer_dtype([True, False])\n'boolean'\n\n>>> infer_dtype([True, False, np.nan])\n'mixed'\n\n>>> infer_dtype([pd.Timestamp('20130101')])\n'datetime'\n\n>>> infer_dtype([datetime.date(2013, 1, 1)])\n'date'\n\n>>> infer_dtype([np.datetime64('2013-01-01')])\n'datetime64'\n\n>>> infer_dtype([datetime.timedelta(0, 1, 1)])\n'timedelta'\n\n>>> infer_dtype(pd.Series(list('aabc')).astype('category'))\n'categorical'", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["SS06", "Summary should fit in a single line"], ["PR02", "Unknown parameters {value, skipna}"], ["PR07", "Parameter \"value\" has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 51, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype(['foo', 'bar'])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[0]>\", line 1, in <module>\n infer_dtype(['foo', 'bar'])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 54, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype(['a', np.nan, 'b'], skipna=True)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[1]>\", line 1, in <module>\n infer_dtype(['a', np.nan, 'b'], skipna=True)\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 57, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype(['a', np.nan, 'b'], skipna=False)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[2]>\", line 1, in <module>\n infer_dtype(['a', np.nan, 'b'], skipna=False)\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 60, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([b'foo', b'bar'])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[3]>\", line 1, in <module>\n infer_dtype([b'foo', b'bar'])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 63, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[4]>\", line 1, in <module>\n infer_dtype([1, 2, 3])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 66, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([1, 2, 3.5])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[5]>\", line 1, in <module>\n infer_dtype([1, 2, 3.5])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 69, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([1.0, 2.0, 3.5])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[6]>\", line 1, in <module>\n infer_dtype([1.0, 2.0, 3.5])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 72, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype(['a', 1])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[7]>\", line 1, in <module>\n infer_dtype(['a', 1])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 75, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([Decimal(1), Decimal(2.0)])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[8]>\", line 1, in <module>\n infer_dtype([Decimal(1), Decimal(2.0)])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 78, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([True, False])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[9]>\", line 1, in <module>\n infer_dtype([True, False])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 81, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([True, False, np.nan])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[10]>\", line 1, in <module>\n infer_dtype([True, False, np.nan])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 84, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([pd.Timestamp('20130101')])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[11]>\", line 1, in <module>\n infer_dtype([pd.Timestamp('20130101')])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 87, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([datetime.date(2013, 1, 1)])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[12]>\", line 1, in <module>\n infer_dtype([datetime.date(2013, 1, 1)])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 90, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([np.datetime64('2013-01-01')])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[13]>\", line 1, in <module>\n infer_dtype([np.datetime64('2013-01-01')])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 93, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([datetime.timedelta(0, 1, 1)])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[14]>\", line 1, in <module>\n infer_dtype([datetime.timedelta(0, 1, 1)])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 96, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype(pd.Series(list('aabc')).astype('category'))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[15]>\", line 1, in <module>\n infer_dtype(pd.Series(list('aabc')).astype('category'))\n NameError: name 'infer_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'infer_dtype' (20 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 51, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype(['foo', 'bar'])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[0]>\", line 1, in <module>\n infer_dtype(['foo', 'bar'])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 54, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype(['a', np.nan, 'b'], skipna=True)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[1]>\", line 1, in <module>\n infer_dtype(['a', np.nan, 'b'], skipna=True)\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 57, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype(['a', np.nan, 'b'], skipna=False)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[2]>\", line 1, in <module>\n infer_dtype(['a', np.nan, 'b'], skipna=False)\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 60, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([b'foo', b'bar'])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[3]>\", line 1, in <module>\n infer_dtype([b'foo', b'bar'])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 63, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[4]>\", line 1, in <module>\n infer_dtype([1, 2, 3])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 66, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([1, 2, 3.5])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[5]>\", line 1, in <module>\n infer_dtype([1, 2, 3.5])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 69, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([1.0, 2.0, 3.5])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[6]>\", line 1, in <module>\n infer_dtype([1.0, 2.0, 3.5])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 72, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype(['a', 1])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[7]>\", line 1, in <module>\n infer_dtype(['a', 1])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 75, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([Decimal(1), Decimal(2.0)])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[8]>\", line 1, in <module>\n infer_dtype([Decimal(1), Decimal(2.0)])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 78, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([True, False])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[9]>\", line 1, in <module>\n infer_dtype([True, False])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 81, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([True, False, np.nan])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[10]>\", line 1, in <module>\n infer_dtype([True, False, np.nan])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 84, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([pd.Timestamp('20130101')])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[11]>\", line 1, in <module>\n infer_dtype([pd.Timestamp('20130101')])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 87, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([datetime.date(2013, 1, 1)])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[12]>\", line 1, in <module>\n infer_dtype([datetime.date(2013, 1, 1)])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 90, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([np.datetime64('2013-01-01')])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[13]>\", line 1, in <module>\n infer_dtype([np.datetime64('2013-01-01')])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 93, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype([datetime.timedelta(0, 1, 1)])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[14]>\", line 1, in <module>\n infer_dtype([datetime.timedelta(0, 1, 1)])\n NameError: name 'infer_dtype' is not defined\n**********************************************************************\nLine 96, in pandas.api.types.infer_dtype\nFailed example:\n infer_dtype(pd.Series(list('aabc')).astype('category'))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.infer_dtype[15]>\", line 1, in <module>\n infer_dtype(pd.Series(list('aabc')).astype('category'))\n NameError: name 'infer_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "", "shared_code_with": "pandas.errors.OutOfBoundsDatetime"}, "pandas.api.types.pandas_dtype": {"type": "function", "docstring": "Converts input into a pandas only dtype object or a numpy dtype object.\n\nParameters\n----------\ndtype : object to be converted\n\nReturns\n-------\nnp.dtype or a pandas dtype\n\nRaises\n------\nTypeError if not a dtype", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 1981, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L1981", "errors": [["SS05", "Summary must start with infinitive verb, not third person (e.g. use \"Generate\" instead of \"Generates\")"], ["PR07", "Parameter \"dtype\" has no description"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Data types related functionality", "subsection": "", "shared_code_with": ""}, "pandas.api.types.is_bool_dtype": {"type": "function", "docstring": "Check whether the provided array or dtype is of a boolean dtype.\n\nParameters\n----------\narr_or_dtype : array-like\n The array or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array or dtype is of a boolean dtype.\n\nNotes\n-----\nAn ExtensionArray is considered boolean when the ``_is_boolean``\nattribute is set to True.\n\nExamples\n--------\n>>> is_bool_dtype(str)\nFalse\n>>> is_bool_dtype(int)\nFalse\n>>> is_bool_dtype(bool)\nTrue\n>>> is_bool_dtype(np.bool)\nTrue\n>>> is_bool_dtype(np.array(['a', 'b']))\nFalse\n>>> is_bool_dtype(pd.Series([1, 2]))\nFalse\n>>> is_bool_dtype(np.array([True, False]))\nTrue\n>>> is_bool_dtype(pd.Categorical([True, False]))\nTrue\n>>> is_bool_dtype(pd.SparseArray([True, False]))\nTrue", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 1578, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L1578", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 20, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[0]>\", line 1, in <module>\n is_bool_dtype(str)\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[1]>\", line 1, in <module>\n is_bool_dtype(int)\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 24, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(bool)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[2]>\", line 1, in <module>\n is_bool_dtype(bool)\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 26, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(np.bool)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[3]>\", line 1, in <module>\n is_bool_dtype(np.bool)\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 28, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[4]>\", line 1, in <module>\n is_bool_dtype(np.array(['a', 'b']))\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 30, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(pd.Series([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[5]>\", line 1, in <module>\n is_bool_dtype(pd.Series([1, 2]))\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 32, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(np.array([True, False]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[6]>\", line 1, in <module>\n is_bool_dtype(np.array([True, False]))\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 34, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(pd.Categorical([True, False]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[7]>\", line 1, in <module>\n is_bool_dtype(pd.Categorical([True, False]))\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 36, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(pd.SparseArray([True, False]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[8]>\", line 1, in <module>\n is_bool_dtype(pd.SparseArray([True, False]))\n NameError: name 'is_bool_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_bool_dtype' (9 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 20, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[0]>\", line 1, in <module>\n is_bool_dtype(str)\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[1]>\", line 1, in <module>\n is_bool_dtype(int)\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 24, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(bool)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[2]>\", line 1, in <module>\n is_bool_dtype(bool)\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 26, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(np.bool)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[3]>\", line 1, in <module>\n is_bool_dtype(np.bool)\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 28, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[4]>\", line 1, in <module>\n is_bool_dtype(np.array(['a', 'b']))\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 30, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(pd.Series([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[5]>\", line 1, in <module>\n is_bool_dtype(pd.Series([1, 2]))\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 32, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(np.array([True, False]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[6]>\", line 1, in <module>\n is_bool_dtype(np.array([True, False]))\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 34, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(pd.Categorical([True, False]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[7]>\", line 1, in <module>\n is_bool_dtype(pd.Categorical([True, False]))\n NameError: name 'is_bool_dtype' is not defined\n**********************************************************************\nLine 36, in pandas.api.types.is_bool_dtype\nFailed example:\n is_bool_dtype(pd.SparseArray([True, False]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_bool_dtype[8]>\", line 1, in <module>\n is_bool_dtype(pd.SparseArray([True, False]))\n NameError: name 'is_bool_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_categorical_dtype": {"type": "function", "docstring": "Check whether an array-like or dtype is of the Categorical dtype.\n\nParameters\n----------\narr_or_dtype : array-like\n The array-like or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array-like or dtype is\n of the Categorical dtype.\n\nExamples\n--------\n>>> is_categorical_dtype(object)\nFalse\n>>> is_categorical_dtype(CategoricalDtype())\nTrue\n>>> is_categorical_dtype([1, 2, 3])\nFalse\n>>> is_categorical_dtype(pd.Categorical([1, 2, 3]))\nTrue\n>>> is_categorical_dtype(pd.CategoricalIndex([1, 2, 3]))\nTrue", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 572, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L572", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT04", "Return value description should start with a capital letter"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 16, in pandas.api.types.is_categorical_dtype\nFailed example:\n is_categorical_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical_dtype[0]>\", line 1, in <module>\n is_categorical_dtype(object)\n NameError: name 'is_categorical_dtype' is not defined\n**********************************************************************\nLine 18, in pandas.api.types.is_categorical_dtype\nFailed example:\n is_categorical_dtype(CategoricalDtype())\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical_dtype[1]>\", line 1, in <module>\n is_categorical_dtype(CategoricalDtype())\n NameError: name 'is_categorical_dtype' is not defined\n**********************************************************************\nLine 20, in pandas.api.types.is_categorical_dtype\nFailed example:\n is_categorical_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical_dtype[2]>\", line 1, in <module>\n is_categorical_dtype([1, 2, 3])\n NameError: name 'is_categorical_dtype' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_categorical_dtype\nFailed example:\n is_categorical_dtype(pd.Categorical([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical_dtype[3]>\", line 1, in <module>\n is_categorical_dtype(pd.Categorical([1, 2, 3]))\n NameError: name 'is_categorical_dtype' is not defined\n**********************************************************************\nLine 24, in pandas.api.types.is_categorical_dtype\nFailed example:\n is_categorical_dtype(pd.CategoricalIndex([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical_dtype[4]>\", line 1, in <module>\n is_categorical_dtype(pd.CategoricalIndex([1, 2, 3]))\n NameError: name 'is_categorical_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_categorical_dtype' (6 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 16, in pandas.api.types.is_categorical_dtype\nFailed example:\n is_categorical_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical_dtype[0]>\", line 1, in <module>\n is_categorical_dtype(object)\n NameError: name 'is_categorical_dtype' is not defined\n**********************************************************************\nLine 18, in pandas.api.types.is_categorical_dtype\nFailed example:\n is_categorical_dtype(CategoricalDtype())\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical_dtype[1]>\", line 1, in <module>\n is_categorical_dtype(CategoricalDtype())\n NameError: name 'is_categorical_dtype' is not defined\n**********************************************************************\nLine 20, in pandas.api.types.is_categorical_dtype\nFailed example:\n is_categorical_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical_dtype[2]>\", line 1, in <module>\n is_categorical_dtype([1, 2, 3])\n NameError: name 'is_categorical_dtype' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_categorical_dtype\nFailed example:\n is_categorical_dtype(pd.Categorical([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical_dtype[3]>\", line 1, in <module>\n is_categorical_dtype(pd.Categorical([1, 2, 3]))\n NameError: name 'is_categorical_dtype' is not defined\n**********************************************************************\nLine 24, in pandas.api.types.is_categorical_dtype\nFailed example:\n is_categorical_dtype(pd.CategoricalIndex([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical_dtype[4]>\", line 1, in <module>\n is_categorical_dtype(pd.CategoricalIndex([1, 2, 3]))\n NameError: name 'is_categorical_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_complex_dtype": {"type": "function", "docstring": "Check whether the provided array or dtype is of a complex dtype.\n\nParameters\n----------\narr_or_dtype : array-like\n The array or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array or dtype is of a compex dtype.\n\nExamples\n--------\n>>> is_complex_dtype(str)\nFalse\n>>> is_complex_dtype(int)\nFalse\n>>> is_complex_dtype(np.complex)\nTrue\n>>> is_complex_dtype(np.array(['a', 'b']))\nFalse\n>>> is_complex_dtype(pd.Series([1, 2]))\nFalse\n>>> is_complex_dtype(np.array([1 + 1j, 5]))\nTrue", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 1752, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L1752", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 15, in pandas.api.types.is_complex_dtype\nFailed example:\n is_complex_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_complex_dtype[0]>\", line 1, in <module>\n is_complex_dtype(str)\n NameError: name 'is_complex_dtype' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_complex_dtype\nFailed example:\n is_complex_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_complex_dtype[1]>\", line 1, in <module>\n is_complex_dtype(int)\n NameError: name 'is_complex_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_complex_dtype\nFailed example:\n is_complex_dtype(np.complex)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_complex_dtype[2]>\", line 1, in <module>\n is_complex_dtype(np.complex)\n NameError: name 'is_complex_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_complex_dtype\nFailed example:\n is_complex_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_complex_dtype[3]>\", line 1, in <module>\n is_complex_dtype(np.array(['a', 'b']))\n NameError: name 'is_complex_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_complex_dtype\nFailed example:\n is_complex_dtype(pd.Series([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_complex_dtype[4]>\", line 1, in <module>\n is_complex_dtype(pd.Series([1, 2]))\n NameError: name 'is_complex_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_complex_dtype\nFailed example:\n is_complex_dtype(np.array([1 + 1j, 5]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_complex_dtype[5]>\", line 1, in <module>\n is_complex_dtype(np.array([1 + 1j, 5]))\n NameError: name 'is_complex_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_complex_dtype' (6 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 15, in pandas.api.types.is_complex_dtype\nFailed example:\n is_complex_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_complex_dtype[0]>\", line 1, in <module>\n is_complex_dtype(str)\n NameError: name 'is_complex_dtype' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_complex_dtype\nFailed example:\n is_complex_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_complex_dtype[1]>\", line 1, in <module>\n is_complex_dtype(int)\n NameError: name 'is_complex_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_complex_dtype\nFailed example:\n is_complex_dtype(np.complex)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_complex_dtype[2]>\", line 1, in <module>\n is_complex_dtype(np.complex)\n NameError: name 'is_complex_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_complex_dtype\nFailed example:\n is_complex_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_complex_dtype[3]>\", line 1, in <module>\n is_complex_dtype(np.array(['a', 'b']))\n NameError: name 'is_complex_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_complex_dtype\nFailed example:\n is_complex_dtype(pd.Series([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_complex_dtype[4]>\", line 1, in <module>\n is_complex_dtype(pd.Series([1, 2]))\n NameError: name 'is_complex_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_complex_dtype\nFailed example:\n is_complex_dtype(np.array([1 + 1j, 5]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_complex_dtype[5]>\", line 1, in <module>\n is_complex_dtype(np.array([1 + 1j, 5]))\n NameError: name 'is_complex_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_datetime64_any_dtype": {"type": "function", "docstring": "Check whether the provided array or dtype is of the datetime64 dtype.\n\nParameters\n----------\narr_or_dtype : array-like\n The array or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array or dtype is of the datetime64 dtype.\n\nExamples\n--------\n>>> is_datetime64_any_dtype(str)\nFalse\n>>> is_datetime64_any_dtype(int)\nFalse\n>>> is_datetime64_any_dtype(np.datetime64) # can be tz-naive\nTrue\n>>> is_datetime64_any_dtype(DatetimeTZDtype(\"ns\", \"US/Eastern\"))\nTrue\n>>> is_datetime64_any_dtype(np.array(['a', 'b']))\nFalse\n>>> is_datetime64_any_dtype(np.array([1, 2]))\nFalse\n>>> is_datetime64_any_dtype(np.array([], dtype=np.datetime64))\nTrue\n>>> is_datetime64_any_dtype(pd.DatetimeIndex([1, 2, 3],\n dtype=np.datetime64))\nTrue", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 1078, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L1078", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 15, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[0]>\", line 1, in <module>\n is_datetime64_any_dtype(str)\n NameError: name 'is_datetime64_any_dtype' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[1]>\", line 1, in <module>\n is_datetime64_any_dtype(int)\n NameError: name 'is_datetime64_any_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(np.datetime64) # can be tz-naive\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[2]>\", line 1, in <module>\n is_datetime64_any_dtype(np.datetime64) # can be tz-naive\n NameError: name 'is_datetime64_any_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(DatetimeTZDtype(\"ns\", \"US/Eastern\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[3]>\", line 1, in <module>\n is_datetime64_any_dtype(DatetimeTZDtype(\"ns\", \"US/Eastern\"))\n NameError: name 'is_datetime64_any_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[4]>\", line 1, in <module>\n is_datetime64_any_dtype(np.array(['a', 'b']))\n NameError: name 'is_datetime64_any_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(np.array([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[5]>\", line 1, in <module>\n is_datetime64_any_dtype(np.array([1, 2]))\n NameError: name 'is_datetime64_any_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(np.array([], dtype=np.datetime64))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[6]>\", line 1, in <module>\n is_datetime64_any_dtype(np.array([], dtype=np.datetime64))\n NameError: name 'is_datetime64_any_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(pd.DatetimeIndex([1, 2, 3],\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[7]>\", line 1\n is_datetime64_any_dtype(pd.DatetimeIndex([1, 2, 3],\n ^\n SyntaxError: unexpected EOF while parsing\n"], ["EX03", "flake8 error: E902 TokenError: EOF in multi-line statement"], ["EX03", "flake8 error: E999 SyntaxError: unexpected EOF while parsing"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 15, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[0]>\", line 1, in <module>\n is_datetime64_any_dtype(str)\n NameError: name 'is_datetime64_any_dtype' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[1]>\", line 1, in <module>\n is_datetime64_any_dtype(int)\n NameError: name 'is_datetime64_any_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(np.datetime64) # can be tz-naive\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[2]>\", line 1, in <module>\n is_datetime64_any_dtype(np.datetime64) # can be tz-naive\n NameError: name 'is_datetime64_any_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(DatetimeTZDtype(\"ns\", \"US/Eastern\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[3]>\", line 1, in <module>\n is_datetime64_any_dtype(DatetimeTZDtype(\"ns\", \"US/Eastern\"))\n NameError: name 'is_datetime64_any_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[4]>\", line 1, in <module>\n is_datetime64_any_dtype(np.array(['a', 'b']))\n NameError: name 'is_datetime64_any_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(np.array([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[5]>\", line 1, in <module>\n is_datetime64_any_dtype(np.array([1, 2]))\n NameError: name 'is_datetime64_any_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(np.array([], dtype=np.datetime64))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[6]>\", line 1, in <module>\n is_datetime64_any_dtype(np.array([], dtype=np.datetime64))\n NameError: name 'is_datetime64_any_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_datetime64_any_dtype\nFailed example:\n is_datetime64_any_dtype(pd.DatetimeIndex([1, 2, 3],\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_any_dtype[7]>\", line 1\n is_datetime64_any_dtype(pd.DatetimeIndex([1, 2, 3],\n ^\n SyntaxError: unexpected EOF while parsing\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_datetime64_dtype": {"type": "function", "docstring": "Check whether an array-like or dtype is of the datetime64 dtype.\n\nParameters\n----------\narr_or_dtype : array-like\n The array-like or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array-like or dtype is of\n the datetime64 dtype.\n\nExamples\n--------\n>>> is_datetime64_dtype(object)\nFalse\n>>> is_datetime64_dtype(np.datetime64)\nTrue\n>>> is_datetime64_dtype(np.array([], dtype=int))\nFalse\n>>> is_datetime64_dtype(np.array([], dtype=np.datetime64))\nTrue\n>>> is_datetime64_dtype([1, 2, 3])\nFalse", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 403, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L403", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT04", "Return value description should start with a capital letter"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 16, in pandas.api.types.is_datetime64_dtype\nFailed example:\n is_datetime64_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_dtype[0]>\", line 1, in <module>\n is_datetime64_dtype(object)\n NameError: name 'is_datetime64_dtype' is not defined\n**********************************************************************\nLine 18, in pandas.api.types.is_datetime64_dtype\nFailed example:\n is_datetime64_dtype(np.datetime64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_dtype[1]>\", line 1, in <module>\n is_datetime64_dtype(np.datetime64)\n NameError: name 'is_datetime64_dtype' is not defined\n**********************************************************************\nLine 20, in pandas.api.types.is_datetime64_dtype\nFailed example:\n is_datetime64_dtype(np.array([], dtype=int))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_dtype[2]>\", line 1, in <module>\n is_datetime64_dtype(np.array([], dtype=int))\n NameError: name 'is_datetime64_dtype' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_datetime64_dtype\nFailed example:\n is_datetime64_dtype(np.array([], dtype=np.datetime64))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_dtype[3]>\", line 1, in <module>\n is_datetime64_dtype(np.array([], dtype=np.datetime64))\n NameError: name 'is_datetime64_dtype' is not defined\n**********************************************************************\nLine 24, in pandas.api.types.is_datetime64_dtype\nFailed example:\n is_datetime64_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_dtype[4]>\", line 1, in <module>\n is_datetime64_dtype([1, 2, 3])\n NameError: name 'is_datetime64_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_datetime64_dtype' (5 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 16, in pandas.api.types.is_datetime64_dtype\nFailed example:\n is_datetime64_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_dtype[0]>\", line 1, in <module>\n is_datetime64_dtype(object)\n NameError: name 'is_datetime64_dtype' is not defined\n**********************************************************************\nLine 18, in pandas.api.types.is_datetime64_dtype\nFailed example:\n is_datetime64_dtype(np.datetime64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_dtype[1]>\", line 1, in <module>\n is_datetime64_dtype(np.datetime64)\n NameError: name 'is_datetime64_dtype' is not defined\n**********************************************************************\nLine 20, in pandas.api.types.is_datetime64_dtype\nFailed example:\n is_datetime64_dtype(np.array([], dtype=int))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_dtype[2]>\", line 1, in <module>\n is_datetime64_dtype(np.array([], dtype=int))\n NameError: name 'is_datetime64_dtype' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_datetime64_dtype\nFailed example:\n is_datetime64_dtype(np.array([], dtype=np.datetime64))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_dtype[3]>\", line 1, in <module>\n is_datetime64_dtype(np.array([], dtype=np.datetime64))\n NameError: name 'is_datetime64_dtype' is not defined\n**********************************************************************\nLine 24, in pandas.api.types.is_datetime64_dtype\nFailed example:\n is_datetime64_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_dtype[4]>\", line 1, in <module>\n is_datetime64_dtype([1, 2, 3])\n NameError: name 'is_datetime64_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_datetime64_ns_dtype": {"type": "function", "docstring": "Check whether the provided array or dtype is of the datetime64[ns] dtype.\n\nParameters\n----------\narr_or_dtype : array-like\n The array or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array or dtype is of the datetime64[ns] dtype.\n\nExamples\n--------\n>>> is_datetime64_ns_dtype(str)\nFalse\n>>> is_datetime64_ns_dtype(int)\nFalse\n>>> is_datetime64_ns_dtype(np.datetime64) # no unit\nFalse\n>>> is_datetime64_ns_dtype(DatetimeTZDtype(\"ns\", \"US/Eastern\"))\nTrue\n>>> is_datetime64_ns_dtype(np.array(['a', 'b']))\nFalse\n>>> is_datetime64_ns_dtype(np.array([1, 2]))\nFalse\n>>> is_datetime64_ns_dtype(np.array([], dtype=np.datetime64)) # no unit\nFalse\n>>> is_datetime64_ns_dtype(np.array([],\n dtype=\"datetime64[ps]\")) # wrong unit\nFalse\n>>> is_datetime64_ns_dtype(pd.DatetimeIndex([1, 2, 3],\n dtype=np.datetime64)) # has 'ns' unit\nTrue", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 1118, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L1118", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 15, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[0]>\", line 1, in <module>\n is_datetime64_ns_dtype(str)\n NameError: name 'is_datetime64_ns_dtype' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[1]>\", line 1, in <module>\n is_datetime64_ns_dtype(int)\n NameError: name 'is_datetime64_ns_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(np.datetime64) # no unit\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[2]>\", line 1, in <module>\n is_datetime64_ns_dtype(np.datetime64) # no unit\n NameError: name 'is_datetime64_ns_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(DatetimeTZDtype(\"ns\", \"US/Eastern\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[3]>\", line 1, in <module>\n is_datetime64_ns_dtype(DatetimeTZDtype(\"ns\", \"US/Eastern\"))\n NameError: name 'is_datetime64_ns_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[4]>\", line 1, in <module>\n is_datetime64_ns_dtype(np.array(['a', 'b']))\n NameError: name 'is_datetime64_ns_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(np.array([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[5]>\", line 1, in <module>\n is_datetime64_ns_dtype(np.array([1, 2]))\n NameError: name 'is_datetime64_ns_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(np.array([], dtype=np.datetime64)) # no unit\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[6]>\", line 1, in <module>\n is_datetime64_ns_dtype(np.array([], dtype=np.datetime64)) # no unit\n NameError: name 'is_datetime64_ns_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(np.array([],\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[7]>\", line 1\n is_datetime64_ns_dtype(np.array([],\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 32, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(pd.DatetimeIndex([1, 2, 3],\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[8]>\", line 1\n is_datetime64_ns_dtype(pd.DatetimeIndex([1, 2, 3],\n ^\n SyntaxError: unexpected EOF while parsing\n"], ["EX03", "flake8 error: E902 TokenError: EOF in multi-line statement"], ["EX03", "flake8 error: E999 SyntaxError: unexpected EOF while parsing"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 15, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[0]>\", line 1, in <module>\n is_datetime64_ns_dtype(str)\n NameError: name 'is_datetime64_ns_dtype' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[1]>\", line 1, in <module>\n is_datetime64_ns_dtype(int)\n NameError: name 'is_datetime64_ns_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(np.datetime64) # no unit\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[2]>\", line 1, in <module>\n is_datetime64_ns_dtype(np.datetime64) # no unit\n NameError: name 'is_datetime64_ns_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(DatetimeTZDtype(\"ns\", \"US/Eastern\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[3]>\", line 1, in <module>\n is_datetime64_ns_dtype(DatetimeTZDtype(\"ns\", \"US/Eastern\"))\n NameError: name 'is_datetime64_ns_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[4]>\", line 1, in <module>\n is_datetime64_ns_dtype(np.array(['a', 'b']))\n NameError: name 'is_datetime64_ns_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(np.array([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[5]>\", line 1, in <module>\n is_datetime64_ns_dtype(np.array([1, 2]))\n NameError: name 'is_datetime64_ns_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(np.array([], dtype=np.datetime64)) # no unit\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[6]>\", line 1, in <module>\n is_datetime64_ns_dtype(np.array([], dtype=np.datetime64)) # no unit\n NameError: name 'is_datetime64_ns_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(np.array([],\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[7]>\", line 1\n is_datetime64_ns_dtype(np.array([],\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 32, in pandas.api.types.is_datetime64_ns_dtype\nFailed example:\n is_datetime64_ns_dtype(pd.DatetimeIndex([1, 2, 3],\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64_ns_dtype[8]>\", line 1\n is_datetime64_ns_dtype(pd.DatetimeIndex([1, 2, 3],\n ^\n SyntaxError: unexpected EOF while parsing\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_datetime64tz_dtype": {"type": "function", "docstring": "Check whether an array-like or dtype is of a DatetimeTZDtype dtype.\n\nParameters\n----------\narr_or_dtype : array-like\n The array-like or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array-like or dtype is of\n a DatetimeTZDtype dtype.\n\nExamples\n--------\n>>> is_datetime64tz_dtype(object)\nFalse\n>>> is_datetime64tz_dtype([1, 2, 3])\nFalse\n>>> is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3])) # tz-naive\nFalse\n>>> is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\nTrue\n\n>>> dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\n>>> s = pd.Series([], dtype=dtype)\n>>> is_datetime64tz_dtype(dtype)\nTrue\n>>> is_datetime64tz_dtype(s)\nTrue", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 434, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L434", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT04", "Return value description should start with a capital letter"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 16, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n is_datetime64tz_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[0]>\", line 1, in <module>\n is_datetime64tz_dtype(object)\n NameError: name 'is_datetime64tz_dtype' is not defined\n**********************************************************************\nLine 18, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n is_datetime64tz_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[1]>\", line 1, in <module>\n is_datetime64tz_dtype([1, 2, 3])\n NameError: name 'is_datetime64tz_dtype' is not defined\n**********************************************************************\nLine 20, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3])) # tz-naive\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[2]>\", line 1, in <module>\n is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3])) # tz-naive\n NameError: name 'is_datetime64tz_dtype' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[3]>\", line 1, in <module>\n is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\n NameError: name 'is_datetime64tz_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[4]>\", line 1, in <module>\n dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\n NameError: name 'DatetimeTZDtype' is not defined\n**********************************************************************\nLine 26, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n s = pd.Series([], dtype=dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[5]>\", line 1, in <module>\n s = pd.Series([], dtype=dtype)\n NameError: name 'dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n is_datetime64tz_dtype(dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[6]>\", line 1, in <module>\n is_datetime64tz_dtype(dtype)\n NameError: name 'is_datetime64tz_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n is_datetime64tz_dtype(s)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[7]>\", line 1, in <module>\n is_datetime64tz_dtype(s)\n NameError: name 'is_datetime64tz_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_datetime64tz_dtype' (7 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 16, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n is_datetime64tz_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[0]>\", line 1, in <module>\n is_datetime64tz_dtype(object)\n NameError: name 'is_datetime64tz_dtype' is not defined\n**********************************************************************\nLine 18, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n is_datetime64tz_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[1]>\", line 1, in <module>\n is_datetime64tz_dtype([1, 2, 3])\n NameError: name 'is_datetime64tz_dtype' is not defined\n**********************************************************************\nLine 20, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3])) # tz-naive\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[2]>\", line 1, in <module>\n is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3])) # tz-naive\n NameError: name 'is_datetime64tz_dtype' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[3]>\", line 1, in <module>\n is_datetime64tz_dtype(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\n NameError: name 'is_datetime64tz_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[4]>\", line 1, in <module>\n dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\n NameError: name 'DatetimeTZDtype' is not defined\n**********************************************************************\nLine 26, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n s = pd.Series([], dtype=dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[5]>\", line 1, in <module>\n s = pd.Series([], dtype=dtype)\n NameError: name 'dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n is_datetime64tz_dtype(dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[6]>\", line 1, in <module>\n is_datetime64tz_dtype(dtype)\n NameError: name 'is_datetime64tz_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_datetime64tz_dtype\nFailed example:\n is_datetime64tz_dtype(s)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetime64tz_dtype[7]>\", line 1, in <module>\n is_datetime64tz_dtype(s)\n NameError: name 'is_datetime64tz_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_extension_type": {"type": "function", "docstring": "Check whether an array-like is of a pandas extension class instance.\n\nExtension classes include categoricals, pandas sparse objects (i.e.\nclasses represented within the pandas library and not ones external\nto it like scipy sparse matrices), and datetime-like arrays.\n\nParameters\n----------\narr : array-like\n The array-like to check.\n\nReturns\n-------\nboolean : Whether or not the array-like is of a pandas\n extension class instance.\n\nExamples\n--------\n>>> is_extension_type([1, 2, 3])\nFalse\n>>> is_extension_type(np.array([1, 2, 3]))\nFalse\n>>>\n>>> cat = pd.Categorical([1, 2, 3])\n>>>\n>>> is_extension_type(cat)\nTrue\n>>> is_extension_type(pd.Series(cat))\nTrue\n>>> is_extension_type(pd.SparseArray([1, 2, 3]))\nTrue\n>>> is_extension_type(pd.SparseSeries([1, 2, 3]))\nTrue\n>>>\n>>> from scipy.sparse import bsr_matrix\n>>> is_extension_type(bsr_matrix([1, 2, 3]))\nFalse\n>>> is_extension_type(pd.DatetimeIndex([1, 2, 3]))\nFalse\n>>> is_extension_type(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\nTrue\n>>>\n>>> dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\n>>> s = pd.Series([], dtype=dtype)\n>>> is_extension_type(s)\nTrue", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 1643, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L1643", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT04", "Return value description should start with a capital letter"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 20, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[0]>\", line 1, in <module>\n is_extension_type([1, 2, 3])\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(np.array([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[1]>\", line 1, in <module>\n is_extension_type(np.array([1, 2, 3]))\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(cat)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[3]>\", line 1, in <module>\n is_extension_type(cat)\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(pd.Series(cat))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[4]>\", line 1, in <module>\n is_extension_type(pd.Series(cat))\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 31, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(pd.SparseArray([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[5]>\", line 1, in <module>\n is_extension_type(pd.SparseArray([1, 2, 3]))\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 33, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(pd.SparseSeries([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[6]>\", line 1, in <module>\n is_extension_type(pd.SparseSeries([1, 2, 3]))\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 37, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(bsr_matrix([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[8]>\", line 1, in <module>\n is_extension_type(bsr_matrix([1, 2, 3]))\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 39, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(pd.DatetimeIndex([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[9]>\", line 1, in <module>\n is_extension_type(pd.DatetimeIndex([1, 2, 3]))\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 41, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[10]>\", line 1, in <module>\n is_extension_type(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 44, in pandas.api.types.is_extension_type\nFailed example:\n dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[11]>\", line 1, in <module>\n dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\n NameError: name 'DatetimeTZDtype' is not defined\n**********************************************************************\nLine 45, in pandas.api.types.is_extension_type\nFailed example:\n s = pd.Series([], dtype=dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[12]>\", line 1, in <module>\n s = pd.Series([], dtype=dtype)\n NameError: name 'dtype' is not defined\n**********************************************************************\nLine 46, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(s)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[13]>\", line 1, in <module>\n is_extension_type(s)\n NameError: name 'is_extension_type' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_extension_type' (11 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 20, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[0]>\", line 1, in <module>\n is_extension_type([1, 2, 3])\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(np.array([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[1]>\", line 1, in <module>\n is_extension_type(np.array([1, 2, 3]))\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(cat)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[3]>\", line 1, in <module>\n is_extension_type(cat)\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(pd.Series(cat))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[4]>\", line 1, in <module>\n is_extension_type(pd.Series(cat))\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 31, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(pd.SparseArray([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[5]>\", line 1, in <module>\n is_extension_type(pd.SparseArray([1, 2, 3]))\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 33, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(pd.SparseSeries([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[6]>\", line 1, in <module>\n is_extension_type(pd.SparseSeries([1, 2, 3]))\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 37, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(bsr_matrix([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[8]>\", line 1, in <module>\n is_extension_type(bsr_matrix([1, 2, 3]))\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 39, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(pd.DatetimeIndex([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[9]>\", line 1, in <module>\n is_extension_type(pd.DatetimeIndex([1, 2, 3]))\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 41, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[10]>\", line 1, in <module>\n is_extension_type(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\n NameError: name 'is_extension_type' is not defined\n**********************************************************************\nLine 44, in pandas.api.types.is_extension_type\nFailed example:\n dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[11]>\", line 1, in <module>\n dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\n NameError: name 'DatetimeTZDtype' is not defined\n**********************************************************************\nLine 45, in pandas.api.types.is_extension_type\nFailed example:\n s = pd.Series([], dtype=dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[12]>\", line 1, in <module>\n s = pd.Series([], dtype=dtype)\n NameError: name 'dtype' is not defined\n**********************************************************************\nLine 46, in pandas.api.types.is_extension_type\nFailed example:\n is_extension_type(s)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_extension_type[13]>\", line 1, in <module>\n is_extension_type(s)\n NameError: name 'is_extension_type' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_extension_array_dtype": {"type": "function", "docstring": "Check if an object is a pandas extension array type.\n\nSee the :ref:`Use Guide <extending.extension-types>` for more.\n\nParameters\n----------\narr_or_dtype : object\n For array-like input, the ``.dtype`` attribute will\n be extracted.\n\nReturns\n-------\nbool\n Whether the `arr_or_dtype` is an extension array type.\n\nNotes\n-----\nThis checks whether an object implements the pandas extension\narray interface. In pandas, this includes:\n\n* Categorical\n* Sparse\n* Interval\n* Period\n* DatetimeArray\n* TimedeltaArray\n\nThird-party libraries may implement arrays or types satisfying\nthis interface as well.\n\nExamples\n--------\n>>> from pandas.api.types import is_extension_array_dtype\n>>> arr = pd.Categorical(['a', 'b'])\n>>> is_extension_array_dtype(arr)\nTrue\n>>> is_extension_array_dtype(arr.dtype)\nTrue\n\n>>> arr = np.array(['a', 'b'])\n>>> is_extension_array_dtype(arr.dtype)\nFalse", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 1702, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L1702", "errors": [], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_float_dtype": {"type": "function", "docstring": "Check whether the provided array or dtype is of a float dtype.\n\nThis function is internal and should not be exposed in the public API.\n\nParameters\n----------\narr_or_dtype : array-like\n The array or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array or dtype is of a float dtype.\n\nExamples\n--------\n>>> is_float_dtype(str)\nFalse\n>>> is_float_dtype(int)\nFalse\n>>> is_float_dtype(float)\nTrue\n>>> is_float_dtype(np.array(['a', 'b']))\nFalse\n>>> is_float_dtype(pd.Series([1, 2]))\nFalse\n>>> is_float_dtype(pd.Index([1, 2.]))\nTrue", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 1545, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L1545", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 17, in pandas.api.types.is_float_dtype\nFailed example:\n is_float_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_float_dtype[0]>\", line 1, in <module>\n is_float_dtype(str)\n NameError: name 'is_float_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_float_dtype\nFailed example:\n is_float_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_float_dtype[1]>\", line 1, in <module>\n is_float_dtype(int)\n NameError: name 'is_float_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_float_dtype\nFailed example:\n is_float_dtype(float)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_float_dtype[2]>\", line 1, in <module>\n is_float_dtype(float)\n NameError: name 'is_float_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_float_dtype\nFailed example:\n is_float_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_float_dtype[3]>\", line 1, in <module>\n is_float_dtype(np.array(['a', 'b']))\n NameError: name 'is_float_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_float_dtype\nFailed example:\n is_float_dtype(pd.Series([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_float_dtype[4]>\", line 1, in <module>\n is_float_dtype(pd.Series([1, 2]))\n NameError: name 'is_float_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_float_dtype\nFailed example:\n is_float_dtype(pd.Index([1, 2.]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_float_dtype[5]>\", line 1, in <module>\n is_float_dtype(pd.Index([1, 2.]))\n NameError: name 'is_float_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_float_dtype' (6 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 17, in pandas.api.types.is_float_dtype\nFailed example:\n is_float_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_float_dtype[0]>\", line 1, in <module>\n is_float_dtype(str)\n NameError: name 'is_float_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_float_dtype\nFailed example:\n is_float_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_float_dtype[1]>\", line 1, in <module>\n is_float_dtype(int)\n NameError: name 'is_float_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_float_dtype\nFailed example:\n is_float_dtype(float)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_float_dtype[2]>\", line 1, in <module>\n is_float_dtype(float)\n NameError: name 'is_float_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_float_dtype\nFailed example:\n is_float_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_float_dtype[3]>\", line 1, in <module>\n is_float_dtype(np.array(['a', 'b']))\n NameError: name 'is_float_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_float_dtype\nFailed example:\n is_float_dtype(pd.Series([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_float_dtype[4]>\", line 1, in <module>\n is_float_dtype(pd.Series([1, 2]))\n NameError: name 'is_float_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_float_dtype\nFailed example:\n is_float_dtype(pd.Index([1, 2.]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_float_dtype[5]>\", line 1, in <module>\n is_float_dtype(pd.Index([1, 2.]))\n NameError: name 'is_float_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_int64_dtype": {"type": "function", "docstring": "Check whether the provided array or dtype is of the int64 dtype.\n\nParameters\n----------\narr_or_dtype : array-like\n The array or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array or dtype is of the int64 dtype.\n\nNotes\n-----\nDepending on system architecture, the return value of `is_int64_dtype(\nint)` will be True if the OS uses 64-bit integers and False if the OS\nuses 32-bit integers.\n\nExamples\n--------\n>>> is_int64_dtype(str)\nFalse\n>>> is_int64_dtype(np.int32)\nFalse\n>>> is_int64_dtype(np.int64)\nTrue\n>>> is_int64_dtype('int8')\nFalse\n>>> is_int64_dtype('Int8')\nFalse\n>>> is_int64_dtype(pd.Int64Dtype)\nTrue\n>>> is_int64_dtype(float)\nFalse\n>>> is_int64_dtype(np.uint64) # unsigned\nFalse\n>>> is_int64_dtype(np.array(['a', 'b']))\nFalse\n>>> is_int64_dtype(np.array([1, 2], dtype=np.int64))\nTrue\n>>> is_int64_dtype(pd.Index([1, 2.])) # float\nFalse\n>>> is_int64_dtype(np.array([1, 2], dtype=np.uint32)) # unsigned\nFalse", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 1028, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L1028", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 21, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[0]>\", line 1, in <module>\n is_int64_dtype(str)\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(np.int32)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[1]>\", line 1, in <module>\n is_int64_dtype(np.int32)\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(np.int64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[2]>\", line 1, in <module>\n is_int64_dtype(np.int64)\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype('int8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[3]>\", line 1, in <module>\n is_int64_dtype('int8')\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype('Int8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[4]>\", line 1, in <module>\n is_int64_dtype('Int8')\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 31, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(pd.Int64Dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[5]>\", line 1, in <module>\n is_int64_dtype(pd.Int64Dtype)\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 33, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(float)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[6]>\", line 1, in <module>\n is_int64_dtype(float)\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 35, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(np.uint64) # unsigned\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[7]>\", line 1, in <module>\n is_int64_dtype(np.uint64) # unsigned\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 37, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[8]>\", line 1, in <module>\n is_int64_dtype(np.array(['a', 'b']))\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 39, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(np.array([1, 2], dtype=np.int64))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[9]>\", line 1, in <module>\n is_int64_dtype(np.array([1, 2], dtype=np.int64))\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 41, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(pd.Index([1, 2.])) # float\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[10]>\", line 1, in <module>\n is_int64_dtype(pd.Index([1, 2.])) # float\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 43, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(np.array([1, 2], dtype=np.uint32)) # unsigned\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[11]>\", line 1, in <module>\n is_int64_dtype(np.array([1, 2], dtype=np.uint32)) # unsigned\n NameError: name 'is_int64_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_int64_dtype' (12 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 21, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[0]>\", line 1, in <module>\n is_int64_dtype(str)\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(np.int32)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[1]>\", line 1, in <module>\n is_int64_dtype(np.int32)\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(np.int64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[2]>\", line 1, in <module>\n is_int64_dtype(np.int64)\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype('int8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[3]>\", line 1, in <module>\n is_int64_dtype('int8')\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype('Int8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[4]>\", line 1, in <module>\n is_int64_dtype('Int8')\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 31, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(pd.Int64Dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[5]>\", line 1, in <module>\n is_int64_dtype(pd.Int64Dtype)\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 33, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(float)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[6]>\", line 1, in <module>\n is_int64_dtype(float)\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 35, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(np.uint64) # unsigned\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[7]>\", line 1, in <module>\n is_int64_dtype(np.uint64) # unsigned\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 37, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[8]>\", line 1, in <module>\n is_int64_dtype(np.array(['a', 'b']))\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 39, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(np.array([1, 2], dtype=np.int64))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[9]>\", line 1, in <module>\n is_int64_dtype(np.array([1, 2], dtype=np.int64))\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 41, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(pd.Index([1, 2.])) # float\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[10]>\", line 1, in <module>\n is_int64_dtype(pd.Index([1, 2.])) # float\n NameError: name 'is_int64_dtype' is not defined\n**********************************************************************\nLine 43, in pandas.api.types.is_int64_dtype\nFailed example:\n is_int64_dtype(np.array([1, 2], dtype=np.uint32)) # unsigned\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_int64_dtype[11]>\", line 1, in <module>\n is_int64_dtype(np.array([1, 2], dtype=np.uint32)) # unsigned\n NameError: name 'is_int64_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_integer_dtype": {"type": "function", "docstring": "Check whether the provided array or dtype is of an integer dtype.\n\nUnlike in `in_any_int_dtype`, timedelta64 instances will return False.\n\n.. versionchanged:: 0.24.0\n\n The nullable Integer dtypes (e.g. pandas.Int64Dtype) are also considered\n as integer by this function.\n\nParameters\n----------\narr_or_dtype : array-like\n The array or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array or dtype is of an integer dtype\n and not an instance of timedelta64.\n\nExamples\n--------\n>>> is_integer_dtype(str)\nFalse\n>>> is_integer_dtype(int)\nTrue\n>>> is_integer_dtype(float)\nFalse\n>>> is_integer_dtype(np.uint64)\nTrue\n>>> is_integer_dtype('int8')\nTrue\n>>> is_integer_dtype('Int8')\nTrue\n>>> is_integer_dtype(pd.Int8Dtype)\nTrue\n>>> is_integer_dtype(np.datetime64)\nFalse\n>>> is_integer_dtype(np.timedelta64)\nFalse\n>>> is_integer_dtype(np.array(['a', 'b']))\nFalse\n>>> is_integer_dtype(pd.Series([1, 2]))\nTrue\n>>> is_integer_dtype(np.array([], dtype=np.timedelta64))\nFalse\n>>> is_integer_dtype(pd.Index([1, 2.])) # float\nFalse", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 868, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L868", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT04", "Return value description should start with a capital letter"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 23, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[0]>\", line 1, in <module>\n is_integer_dtype(str)\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[1]>\", line 1, in <module>\n is_integer_dtype(int)\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(float)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[2]>\", line 1, in <module>\n is_integer_dtype(float)\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(np.uint64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[3]>\", line 1, in <module>\n is_integer_dtype(np.uint64)\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 31, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype('int8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[4]>\", line 1, in <module>\n is_integer_dtype('int8')\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 33, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype('Int8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[5]>\", line 1, in <module>\n is_integer_dtype('Int8')\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 35, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(pd.Int8Dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[6]>\", line 1, in <module>\n is_integer_dtype(pd.Int8Dtype)\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 37, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(np.datetime64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[7]>\", line 1, in <module>\n is_integer_dtype(np.datetime64)\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 39, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(np.timedelta64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[8]>\", line 1, in <module>\n is_integer_dtype(np.timedelta64)\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 41, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[9]>\", line 1, in <module>\n is_integer_dtype(np.array(['a', 'b']))\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 43, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(pd.Series([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[10]>\", line 1, in <module>\n is_integer_dtype(pd.Series([1, 2]))\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 45, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(np.array([], dtype=np.timedelta64))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[11]>\", line 1, in <module>\n is_integer_dtype(np.array([], dtype=np.timedelta64))\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 47, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(pd.Index([1, 2.])) # float\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[12]>\", line 1, in <module>\n is_integer_dtype(pd.Index([1, 2.])) # float\n NameError: name 'is_integer_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_integer_dtype' (13 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 23, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[0]>\", line 1, in <module>\n is_integer_dtype(str)\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[1]>\", line 1, in <module>\n is_integer_dtype(int)\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(float)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[2]>\", line 1, in <module>\n is_integer_dtype(float)\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(np.uint64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[3]>\", line 1, in <module>\n is_integer_dtype(np.uint64)\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 31, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype('int8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[4]>\", line 1, in <module>\n is_integer_dtype('int8')\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 33, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype('Int8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[5]>\", line 1, in <module>\n is_integer_dtype('Int8')\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 35, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(pd.Int8Dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[6]>\", line 1, in <module>\n is_integer_dtype(pd.Int8Dtype)\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 37, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(np.datetime64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[7]>\", line 1, in <module>\n is_integer_dtype(np.datetime64)\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 39, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(np.timedelta64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[8]>\", line 1, in <module>\n is_integer_dtype(np.timedelta64)\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 41, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[9]>\", line 1, in <module>\n is_integer_dtype(np.array(['a', 'b']))\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 43, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(pd.Series([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[10]>\", line 1, in <module>\n is_integer_dtype(pd.Series([1, 2]))\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 45, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(np.array([], dtype=np.timedelta64))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[11]>\", line 1, in <module>\n is_integer_dtype(np.array([], dtype=np.timedelta64))\n NameError: name 'is_integer_dtype' is not defined\n**********************************************************************\nLine 47, in pandas.api.types.is_integer_dtype\nFailed example:\n is_integer_dtype(pd.Index([1, 2.])) # float\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_integer_dtype[12]>\", line 1, in <module>\n is_integer_dtype(pd.Index([1, 2.])) # float\n NameError: name 'is_integer_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_interval_dtype": {"type": "function", "docstring": "Check whether an array-like or dtype is of the Interval dtype.\n\nParameters\n----------\narr_or_dtype : array-like\n The array-like or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array-like or dtype is\n of the Interval dtype.\n\nExamples\n--------\n>>> is_interval_dtype(object)\nFalse\n>>> is_interval_dtype(IntervalDtype())\nTrue\n>>> is_interval_dtype([1, 2, 3])\nFalse\n>>>\n>>> interval = pd.Interval(1, 2, closed=\"right\")\n>>> is_interval_dtype(interval)\nFalse\n>>> is_interval_dtype(pd.IntervalIndex([interval]))\nTrue", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 536, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L536", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT04", "Return value description should start with a capital letter"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 16, in pandas.api.types.is_interval_dtype\nFailed example:\n is_interval_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_interval_dtype[0]>\", line 1, in <module>\n is_interval_dtype(object)\n NameError: name 'is_interval_dtype' is not defined\n**********************************************************************\nLine 18, in pandas.api.types.is_interval_dtype\nFailed example:\n is_interval_dtype(IntervalDtype())\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_interval_dtype[1]>\", line 1, in <module>\n is_interval_dtype(IntervalDtype())\n NameError: name 'is_interval_dtype' is not defined\n**********************************************************************\nLine 20, in pandas.api.types.is_interval_dtype\nFailed example:\n is_interval_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_interval_dtype[2]>\", line 1, in <module>\n is_interval_dtype([1, 2, 3])\n NameError: name 'is_interval_dtype' is not defined\n**********************************************************************\nLine 24, in pandas.api.types.is_interval_dtype\nFailed example:\n is_interval_dtype(interval)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_interval_dtype[4]>\", line 1, in <module>\n is_interval_dtype(interval)\n NameError: name 'is_interval_dtype' is not defined\n**********************************************************************\nLine 26, in pandas.api.types.is_interval_dtype\nFailed example:\n is_interval_dtype(pd.IntervalIndex([interval]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_interval_dtype[5]>\", line 1, in <module>\n is_interval_dtype(pd.IntervalIndex([interval]))\n NameError: name 'is_interval_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_interval_dtype' (6 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 16, in pandas.api.types.is_interval_dtype\nFailed example:\n is_interval_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_interval_dtype[0]>\", line 1, in <module>\n is_interval_dtype(object)\n NameError: name 'is_interval_dtype' is not defined\n**********************************************************************\nLine 18, in pandas.api.types.is_interval_dtype\nFailed example:\n is_interval_dtype(IntervalDtype())\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_interval_dtype[1]>\", line 1, in <module>\n is_interval_dtype(IntervalDtype())\n NameError: name 'is_interval_dtype' is not defined\n**********************************************************************\nLine 20, in pandas.api.types.is_interval_dtype\nFailed example:\n is_interval_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_interval_dtype[2]>\", line 1, in <module>\n is_interval_dtype([1, 2, 3])\n NameError: name 'is_interval_dtype' is not defined\n**********************************************************************\nLine 24, in pandas.api.types.is_interval_dtype\nFailed example:\n is_interval_dtype(interval)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_interval_dtype[4]>\", line 1, in <module>\n is_interval_dtype(interval)\n NameError: name 'is_interval_dtype' is not defined\n**********************************************************************\nLine 26, in pandas.api.types.is_interval_dtype\nFailed example:\n is_interval_dtype(pd.IntervalIndex([interval]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_interval_dtype[5]>\", line 1, in <module>\n is_interval_dtype(pd.IntervalIndex([interval]))\n NameError: name 'is_interval_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_numeric_dtype": {"type": "function", "docstring": "Check whether the provided array or dtype is of a numeric dtype.\n\nParameters\n----------\narr_or_dtype : array-like\n The array or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array or dtype is of a numeric dtype.\n\nExamples\n--------\n>>> is_numeric_dtype(str)\nFalse\n>>> is_numeric_dtype(int)\nTrue\n>>> is_numeric_dtype(float)\nTrue\n>>> is_numeric_dtype(np.uint64)\nTrue\n>>> is_numeric_dtype(np.datetime64)\nFalse\n>>> is_numeric_dtype(np.timedelta64)\nFalse\n>>> is_numeric_dtype(np.array(['a', 'b']))\nFalse\n>>> is_numeric_dtype(pd.Series([1, 2]))\nTrue\n>>> is_numeric_dtype(pd.Index([1, 2.]))\nTrue\n>>> is_numeric_dtype(np.array([], dtype=np.timedelta64))\nFalse", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 1472, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L1472", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 15, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[0]>\", line 1, in <module>\n is_numeric_dtype(str)\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[1]>\", line 1, in <module>\n is_numeric_dtype(int)\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(float)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[2]>\", line 1, in <module>\n is_numeric_dtype(float)\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(np.uint64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[3]>\", line 1, in <module>\n is_numeric_dtype(np.uint64)\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(np.datetime64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[4]>\", line 1, in <module>\n is_numeric_dtype(np.datetime64)\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(np.timedelta64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[5]>\", line 1, in <module>\n is_numeric_dtype(np.timedelta64)\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[6]>\", line 1, in <module>\n is_numeric_dtype(np.array(['a', 'b']))\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(pd.Series([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[7]>\", line 1, in <module>\n is_numeric_dtype(pd.Series([1, 2]))\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 31, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(pd.Index([1, 2.]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[8]>\", line 1, in <module>\n is_numeric_dtype(pd.Index([1, 2.]))\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 33, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(np.array([], dtype=np.timedelta64))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[9]>\", line 1, in <module>\n is_numeric_dtype(np.array([], dtype=np.timedelta64))\n NameError: name 'is_numeric_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_numeric_dtype' (10 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 15, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[0]>\", line 1, in <module>\n is_numeric_dtype(str)\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[1]>\", line 1, in <module>\n is_numeric_dtype(int)\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(float)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[2]>\", line 1, in <module>\n is_numeric_dtype(float)\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(np.uint64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[3]>\", line 1, in <module>\n is_numeric_dtype(np.uint64)\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(np.datetime64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[4]>\", line 1, in <module>\n is_numeric_dtype(np.datetime64)\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(np.timedelta64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[5]>\", line 1, in <module>\n is_numeric_dtype(np.timedelta64)\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[6]>\", line 1, in <module>\n is_numeric_dtype(np.array(['a', 'b']))\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(pd.Series([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[7]>\", line 1, in <module>\n is_numeric_dtype(pd.Series([1, 2]))\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 31, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(pd.Index([1, 2.]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[8]>\", line 1, in <module>\n is_numeric_dtype(pd.Index([1, 2.]))\n NameError: name 'is_numeric_dtype' is not defined\n**********************************************************************\nLine 33, in pandas.api.types.is_numeric_dtype\nFailed example:\n is_numeric_dtype(np.array([], dtype=np.timedelta64))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_numeric_dtype[9]>\", line 1, in <module>\n is_numeric_dtype(np.array([], dtype=np.timedelta64))\n NameError: name 'is_numeric_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_object_dtype": {"type": "function", "docstring": "Check whether an array-like or dtype is of the object dtype.\n\nParameters\n----------\narr_or_dtype : array-like\n The array-like or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array-like or dtype is of the object dtype.\n\nExamples\n--------\n>>> is_object_dtype(object)\nTrue\n>>> is_object_dtype(int)\nFalse\n>>> is_object_dtype(np.array([], dtype=object))\nTrue\n>>> is_object_dtype(np.array([], dtype=int))\nFalse\n>>> is_object_dtype([1, 2, 3])\nFalse", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 131, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L131", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 15, in pandas.api.types.is_object_dtype\nFailed example:\n is_object_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_object_dtype[0]>\", line 1, in <module>\n is_object_dtype(object)\n NameError: name 'is_object_dtype' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_object_dtype\nFailed example:\n is_object_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_object_dtype[1]>\", line 1, in <module>\n is_object_dtype(int)\n NameError: name 'is_object_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_object_dtype\nFailed example:\n is_object_dtype(np.array([], dtype=object))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_object_dtype[2]>\", line 1, in <module>\n is_object_dtype(np.array([], dtype=object))\n NameError: name 'is_object_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_object_dtype\nFailed example:\n is_object_dtype(np.array([], dtype=int))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_object_dtype[3]>\", line 1, in <module>\n is_object_dtype(np.array([], dtype=int))\n NameError: name 'is_object_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_object_dtype\nFailed example:\n is_object_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_object_dtype[4]>\", line 1, in <module>\n is_object_dtype([1, 2, 3])\n NameError: name 'is_object_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_object_dtype' (5 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 15, in pandas.api.types.is_object_dtype\nFailed example:\n is_object_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_object_dtype[0]>\", line 1, in <module>\n is_object_dtype(object)\n NameError: name 'is_object_dtype' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_object_dtype\nFailed example:\n is_object_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_object_dtype[1]>\", line 1, in <module>\n is_object_dtype(int)\n NameError: name 'is_object_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_object_dtype\nFailed example:\n is_object_dtype(np.array([], dtype=object))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_object_dtype[2]>\", line 1, in <module>\n is_object_dtype(np.array([], dtype=object))\n NameError: name 'is_object_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_object_dtype\nFailed example:\n is_object_dtype(np.array([], dtype=int))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_object_dtype[3]>\", line 1, in <module>\n is_object_dtype(np.array([], dtype=int))\n NameError: name 'is_object_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_object_dtype\nFailed example:\n is_object_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_object_dtype[4]>\", line 1, in <module>\n is_object_dtype([1, 2, 3])\n NameError: name 'is_object_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_period_dtype": {"type": "function", "docstring": "Check whether an array-like or dtype is of the Period dtype.\n\nParameters\n----------\narr_or_dtype : array-like\n The array-like or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array-like or dtype is of the Period dtype.\n\nExamples\n--------\n>>> is_period_dtype(object)\nFalse\n>>> is_period_dtype(PeriodDtype(freq=\"D\"))\nTrue\n>>> is_period_dtype([1, 2, 3])\nFalse\n>>> is_period_dtype(pd.Period(\"2017-01-01\"))\nFalse\n>>> is_period_dtype(pd.PeriodIndex([], freq=\"A\"))\nTrue", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 503, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L503", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 15, in pandas.api.types.is_period_dtype\nFailed example:\n is_period_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period_dtype[0]>\", line 1, in <module>\n is_period_dtype(object)\n NameError: name 'is_period_dtype' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_period_dtype\nFailed example:\n is_period_dtype(PeriodDtype(freq=\"D\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period_dtype[1]>\", line 1, in <module>\n is_period_dtype(PeriodDtype(freq=\"D\"))\n NameError: name 'is_period_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_period_dtype\nFailed example:\n is_period_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period_dtype[2]>\", line 1, in <module>\n is_period_dtype([1, 2, 3])\n NameError: name 'is_period_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_period_dtype\nFailed example:\n is_period_dtype(pd.Period(\"2017-01-01\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period_dtype[3]>\", line 1, in <module>\n is_period_dtype(pd.Period(\"2017-01-01\"))\n NameError: name 'is_period_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_period_dtype\nFailed example:\n is_period_dtype(pd.PeriodIndex([], freq=\"A\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period_dtype[4]>\", line 1, in <module>\n is_period_dtype(pd.PeriodIndex([], freq=\"A\"))\n NameError: name 'is_period_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_period_dtype' (6 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 15, in pandas.api.types.is_period_dtype\nFailed example:\n is_period_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period_dtype[0]>\", line 1, in <module>\n is_period_dtype(object)\n NameError: name 'is_period_dtype' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_period_dtype\nFailed example:\n is_period_dtype(PeriodDtype(freq=\"D\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period_dtype[1]>\", line 1, in <module>\n is_period_dtype(PeriodDtype(freq=\"D\"))\n NameError: name 'is_period_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_period_dtype\nFailed example:\n is_period_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period_dtype[2]>\", line 1, in <module>\n is_period_dtype([1, 2, 3])\n NameError: name 'is_period_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_period_dtype\nFailed example:\n is_period_dtype(pd.Period(\"2017-01-01\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period_dtype[3]>\", line 1, in <module>\n is_period_dtype(pd.Period(\"2017-01-01\"))\n NameError: name 'is_period_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_period_dtype\nFailed example:\n is_period_dtype(pd.PeriodIndex([], freq=\"A\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period_dtype[4]>\", line 1, in <module>\n is_period_dtype(pd.PeriodIndex([], freq=\"A\"))\n NameError: name 'is_period_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_signed_integer_dtype": {"type": "function", "docstring": "Check whether the provided array or dtype is of a signed integer dtype.\n\nUnlike in `in_any_int_dtype`, timedelta64 instances will return False.\n\n.. versionchanged:: 0.24.0\n\n The nullable Integer dtypes (e.g. pandas.Int64Dtype) are also considered\n as integer by this function.\n\nParameters\n----------\narr_or_dtype : array-like\n The array or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array or dtype is of a signed integer dtype\n and not an instance of timedelta64.\n\nExamples\n--------\n>>> is_signed_integer_dtype(str)\nFalse\n>>> is_signed_integer_dtype(int)\nTrue\n>>> is_signed_integer_dtype(float)\nFalse\n>>> is_signed_integer_dtype(np.uint64) # unsigned\nFalse\n>>> is_signed_integer_dtype('int8')\nTrue\n>>> is_signed_integer_dtype('Int8')\nTrue\n>>> is_signed_dtype(pd.Int8Dtype)\nTrue\n>>> is_signed_integer_dtype(np.datetime64)\nFalse\n>>> is_signed_integer_dtype(np.timedelta64)\nFalse\n>>> is_signed_integer_dtype(np.array(['a', 'b']))\nFalse\n>>> is_signed_integer_dtype(pd.Series([1, 2]))\nTrue\n>>> is_signed_integer_dtype(np.array([], dtype=np.timedelta64))\nFalse\n>>> is_signed_integer_dtype(pd.Index([1, 2.])) # float\nFalse\n>>> is_signed_integer_dtype(np.array([1, 2], dtype=np.uint32)) # unsigned\nFalse", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 923, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L923", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT04", "Return value description should start with a capital letter"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 23, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[0]>\", line 1, in <module>\n is_signed_integer_dtype(str)\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[1]>\", line 1, in <module>\n is_signed_integer_dtype(int)\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(float)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[2]>\", line 1, in <module>\n is_signed_integer_dtype(float)\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(np.uint64) # unsigned\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[3]>\", line 1, in <module>\n is_signed_integer_dtype(np.uint64) # unsigned\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 31, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype('int8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[4]>\", line 1, in <module>\n is_signed_integer_dtype('int8')\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 33, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype('Int8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[5]>\", line 1, in <module>\n is_signed_integer_dtype('Int8')\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 35, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_dtype(pd.Int8Dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[6]>\", line 1, in <module>\n is_signed_dtype(pd.Int8Dtype)\n NameError: name 'is_signed_dtype' is not defined\n**********************************************************************\nLine 37, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(np.datetime64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[7]>\", line 1, in <module>\n is_signed_integer_dtype(np.datetime64)\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 39, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(np.timedelta64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[8]>\", line 1, in <module>\n is_signed_integer_dtype(np.timedelta64)\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 41, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[9]>\", line 1, in <module>\n is_signed_integer_dtype(np.array(['a', 'b']))\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 43, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(pd.Series([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[10]>\", line 1, in <module>\n is_signed_integer_dtype(pd.Series([1, 2]))\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 45, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(np.array([], dtype=np.timedelta64))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[11]>\", line 1, in <module>\n is_signed_integer_dtype(np.array([], dtype=np.timedelta64))\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 47, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(pd.Index([1, 2.])) # float\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[12]>\", line 1, in <module>\n is_signed_integer_dtype(pd.Index([1, 2.])) # float\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 49, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(np.array([1, 2], dtype=np.uint32)) # unsigned\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[13]>\", line 1, in <module>\n is_signed_integer_dtype(np.array([1, 2], dtype=np.uint32)) # unsigned\n NameError: name 'is_signed_integer_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_signed_integer_dtype' (14 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 23, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[0]>\", line 1, in <module>\n is_signed_integer_dtype(str)\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[1]>\", line 1, in <module>\n is_signed_integer_dtype(int)\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(float)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[2]>\", line 1, in <module>\n is_signed_integer_dtype(float)\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(np.uint64) # unsigned\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[3]>\", line 1, in <module>\n is_signed_integer_dtype(np.uint64) # unsigned\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 31, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype('int8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[4]>\", line 1, in <module>\n is_signed_integer_dtype('int8')\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 33, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype('Int8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[5]>\", line 1, in <module>\n is_signed_integer_dtype('Int8')\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 35, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_dtype(pd.Int8Dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[6]>\", line 1, in <module>\n is_signed_dtype(pd.Int8Dtype)\n NameError: name 'is_signed_dtype' is not defined\n**********************************************************************\nLine 37, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(np.datetime64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[7]>\", line 1, in <module>\n is_signed_integer_dtype(np.datetime64)\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 39, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(np.timedelta64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[8]>\", line 1, in <module>\n is_signed_integer_dtype(np.timedelta64)\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 41, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[9]>\", line 1, in <module>\n is_signed_integer_dtype(np.array(['a', 'b']))\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 43, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(pd.Series([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[10]>\", line 1, in <module>\n is_signed_integer_dtype(pd.Series([1, 2]))\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 45, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(np.array([], dtype=np.timedelta64))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[11]>\", line 1, in <module>\n is_signed_integer_dtype(np.array([], dtype=np.timedelta64))\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 47, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(pd.Index([1, 2.])) # float\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[12]>\", line 1, in <module>\n is_signed_integer_dtype(pd.Index([1, 2.])) # float\n NameError: name 'is_signed_integer_dtype' is not defined\n**********************************************************************\nLine 49, in pandas.api.types.is_signed_integer_dtype\nFailed example:\n is_signed_integer_dtype(np.array([1, 2], dtype=np.uint32)) # unsigned\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_signed_integer_dtype[13]>\", line 1, in <module>\n is_signed_integer_dtype(np.array([1, 2], dtype=np.uint32)) # unsigned\n NameError: name 'is_signed_integer_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_string_dtype": {"type": "function", "docstring": "Check whether the provided array or dtype is of the string dtype.\n\nParameters\n----------\narr_or_dtype : array-like\n The array or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array or dtype is of the string dtype.\n\nExamples\n--------\n>>> is_string_dtype(str)\nTrue\n>>> is_string_dtype(object)\nTrue\n>>> is_string_dtype(int)\nFalse\n>>>\n>>> is_string_dtype(np.array(['a', 'b']))\nTrue\n>>> is_string_dtype(pd.Series([1, 2]))\nFalse", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 605, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L605", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 15, in pandas.api.types.is_string_dtype\nFailed example:\n is_string_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_string_dtype[0]>\", line 1, in <module>\n is_string_dtype(str)\n NameError: name 'is_string_dtype' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_string_dtype\nFailed example:\n is_string_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_string_dtype[1]>\", line 1, in <module>\n is_string_dtype(object)\n NameError: name 'is_string_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_string_dtype\nFailed example:\n is_string_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_string_dtype[2]>\", line 1, in <module>\n is_string_dtype(int)\n NameError: name 'is_string_dtype' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_string_dtype\nFailed example:\n is_string_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_string_dtype[3]>\", line 1, in <module>\n is_string_dtype(np.array(['a', 'b']))\n NameError: name 'is_string_dtype' is not defined\n**********************************************************************\nLine 24, in pandas.api.types.is_string_dtype\nFailed example:\n is_string_dtype(pd.Series([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_string_dtype[4]>\", line 1, in <module>\n is_string_dtype(pd.Series([1, 2]))\n NameError: name 'is_string_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_string_dtype' (5 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 15, in pandas.api.types.is_string_dtype\nFailed example:\n is_string_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_string_dtype[0]>\", line 1, in <module>\n is_string_dtype(str)\n NameError: name 'is_string_dtype' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_string_dtype\nFailed example:\n is_string_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_string_dtype[1]>\", line 1, in <module>\n is_string_dtype(object)\n NameError: name 'is_string_dtype' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_string_dtype\nFailed example:\n is_string_dtype(int)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_string_dtype[2]>\", line 1, in <module>\n is_string_dtype(int)\n NameError: name 'is_string_dtype' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_string_dtype\nFailed example:\n is_string_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_string_dtype[3]>\", line 1, in <module>\n is_string_dtype(np.array(['a', 'b']))\n NameError: name 'is_string_dtype' is not defined\n**********************************************************************\nLine 24, in pandas.api.types.is_string_dtype\nFailed example:\n is_string_dtype(pd.Series([1, 2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_string_dtype[4]>\", line 1, in <module>\n is_string_dtype(pd.Series([1, 2]))\n NameError: name 'is_string_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_timedelta64_dtype": {"type": "function", "docstring": "Check whether an array-like or dtype is of the timedelta64 dtype.\n\nParameters\n----------\narr_or_dtype : array-like\n The array-like or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array-like or dtype is\n of the timedelta64 dtype.\n\nExamples\n--------\n>>> is_timedelta64_dtype(object)\nFalse\n>>> is_timedelta64_dtype(np.timedelta64)\nTrue\n>>> is_timedelta64_dtype([1, 2, 3])\nFalse\n>>> is_timedelta64_dtype(pd.Series([], dtype=\"timedelta64[ns]\"))\nTrue\n>>> is_timedelta64_dtype('0 days')\nFalse", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 472, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L472", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT04", "Return value description should start with a capital letter"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 16, in pandas.api.types.is_timedelta64_dtype\nFailed example:\n is_timedelta64_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_dtype[0]>\", line 1, in <module>\n is_timedelta64_dtype(object)\n NameError: name 'is_timedelta64_dtype' is not defined\n**********************************************************************\nLine 18, in pandas.api.types.is_timedelta64_dtype\nFailed example:\n is_timedelta64_dtype(np.timedelta64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_dtype[1]>\", line 1, in <module>\n is_timedelta64_dtype(np.timedelta64)\n NameError: name 'is_timedelta64_dtype' is not defined\n**********************************************************************\nLine 20, in pandas.api.types.is_timedelta64_dtype\nFailed example:\n is_timedelta64_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_dtype[2]>\", line 1, in <module>\n is_timedelta64_dtype([1, 2, 3])\n NameError: name 'is_timedelta64_dtype' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_timedelta64_dtype\nFailed example:\n is_timedelta64_dtype(pd.Series([], dtype=\"timedelta64[ns]\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_dtype[3]>\", line 1, in <module>\n is_timedelta64_dtype(pd.Series([], dtype=\"timedelta64[ns]\"))\n NameError: name 'is_timedelta64_dtype' is not defined\n**********************************************************************\nLine 24, in pandas.api.types.is_timedelta64_dtype\nFailed example:\n is_timedelta64_dtype('0 days')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_dtype[4]>\", line 1, in <module>\n is_timedelta64_dtype('0 days')\n NameError: name 'is_timedelta64_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_timedelta64_dtype' (5 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 16, in pandas.api.types.is_timedelta64_dtype\nFailed example:\n is_timedelta64_dtype(object)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_dtype[0]>\", line 1, in <module>\n is_timedelta64_dtype(object)\n NameError: name 'is_timedelta64_dtype' is not defined\n**********************************************************************\nLine 18, in pandas.api.types.is_timedelta64_dtype\nFailed example:\n is_timedelta64_dtype(np.timedelta64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_dtype[1]>\", line 1, in <module>\n is_timedelta64_dtype(np.timedelta64)\n NameError: name 'is_timedelta64_dtype' is not defined\n**********************************************************************\nLine 20, in pandas.api.types.is_timedelta64_dtype\nFailed example:\n is_timedelta64_dtype([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_dtype[2]>\", line 1, in <module>\n is_timedelta64_dtype([1, 2, 3])\n NameError: name 'is_timedelta64_dtype' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_timedelta64_dtype\nFailed example:\n is_timedelta64_dtype(pd.Series([], dtype=\"timedelta64[ns]\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_dtype[3]>\", line 1, in <module>\n is_timedelta64_dtype(pd.Series([], dtype=\"timedelta64[ns]\"))\n NameError: name 'is_timedelta64_dtype' is not defined\n**********************************************************************\nLine 24, in pandas.api.types.is_timedelta64_dtype\nFailed example:\n is_timedelta64_dtype('0 days')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_dtype[4]>\", line 1, in <module>\n is_timedelta64_dtype('0 days')\n NameError: name 'is_timedelta64_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_timedelta64_ns_dtype": {"type": "function", "docstring": "Check whether the provided array or dtype is of the timedelta64[ns] dtype.\n\nThis is a very specific dtype, so generic ones like `np.timedelta64`\nwill return False if passed into this function.\n\nParameters\n----------\narr_or_dtype : array-like\n The array or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array or dtype is of the\n timedelta64[ns] dtype.\n\nExamples\n--------\n>>> is_timedelta64_ns_dtype(np.dtype('m8[ns]'))\nTrue\n>>> is_timedelta64_ns_dtype(np.dtype('m8[ps]')) # Wrong frequency\nFalse\n>>> is_timedelta64_ns_dtype(np.array([1, 2], dtype='m8[ns]'))\nTrue\n>>> is_timedelta64_ns_dtype(np.array([1, 2], dtype=np.timedelta64))\nFalse", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 1167, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L1167", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT04", "Return value description should start with a capital letter"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 19, in pandas.api.types.is_timedelta64_ns_dtype\nFailed example:\n is_timedelta64_ns_dtype(np.dtype('m8[ns]'))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_ns_dtype[0]>\", line 1, in <module>\n is_timedelta64_ns_dtype(np.dtype('m8[ns]'))\n NameError: name 'is_timedelta64_ns_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_timedelta64_ns_dtype\nFailed example:\n is_timedelta64_ns_dtype(np.dtype('m8[ps]')) # Wrong frequency\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_ns_dtype[1]>\", line 1, in <module>\n is_timedelta64_ns_dtype(np.dtype('m8[ps]')) # Wrong frequency\n NameError: name 'is_timedelta64_ns_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_timedelta64_ns_dtype\nFailed example:\n is_timedelta64_ns_dtype(np.array([1, 2], dtype='m8[ns]'))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_ns_dtype[2]>\", line 1, in <module>\n is_timedelta64_ns_dtype(np.array([1, 2], dtype='m8[ns]'))\n NameError: name 'is_timedelta64_ns_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_timedelta64_ns_dtype\nFailed example:\n is_timedelta64_ns_dtype(np.array([1, 2], dtype=np.timedelta64))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_ns_dtype[3]>\", line 1, in <module>\n is_timedelta64_ns_dtype(np.array([1, 2], dtype=np.timedelta64))\n NameError: name 'is_timedelta64_ns_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_timedelta64_ns_dtype' (4 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 19, in pandas.api.types.is_timedelta64_ns_dtype\nFailed example:\n is_timedelta64_ns_dtype(np.dtype('m8[ns]'))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_ns_dtype[0]>\", line 1, in <module>\n is_timedelta64_ns_dtype(np.dtype('m8[ns]'))\n NameError: name 'is_timedelta64_ns_dtype' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_timedelta64_ns_dtype\nFailed example:\n is_timedelta64_ns_dtype(np.dtype('m8[ps]')) # Wrong frequency\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_ns_dtype[1]>\", line 1, in <module>\n is_timedelta64_ns_dtype(np.dtype('m8[ps]')) # Wrong frequency\n NameError: name 'is_timedelta64_ns_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_timedelta64_ns_dtype\nFailed example:\n is_timedelta64_ns_dtype(np.array([1, 2], dtype='m8[ns]'))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_ns_dtype[2]>\", line 1, in <module>\n is_timedelta64_ns_dtype(np.array([1, 2], dtype='m8[ns]'))\n NameError: name 'is_timedelta64_ns_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_timedelta64_ns_dtype\nFailed example:\n is_timedelta64_ns_dtype(np.array([1, 2], dtype=np.timedelta64))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_timedelta64_ns_dtype[3]>\", line 1, in <module>\n is_timedelta64_ns_dtype(np.array([1, 2], dtype=np.timedelta64))\n NameError: name 'is_timedelta64_ns_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_unsigned_integer_dtype": {"type": "function", "docstring": "Check whether the provided array or dtype is of an unsigned integer dtype.\n\n.. versionchanged:: 0.24.0\n\n The nullable Integer dtypes (e.g. pandas.UInt64Dtype) are also\n considered as integer by this function.\n\nParameters\n----------\narr_or_dtype : array-like\n The array or dtype to check.\n\nReturns\n-------\nboolean : Whether or not the array or dtype is of an\n unsigned integer dtype.\n\nExamples\n--------\n>>> is_unsigned_integer_dtype(str)\nFalse\n>>> is_unsigned_integer_dtype(int) # signed\nFalse\n>>> is_unsigned_integer_dtype(float)\nFalse\n>>> is_unsigned_integer_dtype(np.uint64)\nTrue\n>>> is_unsigned_integer_dtype('uint8')\nTrue\n>>> is_unsigned_integer_dtype('UInt8')\nTrue\n>>> is_unsigned_integer_dtype(pd.UInt8Dtype)\nTrue\n>>> is_unsigned_integer_dtype(np.array(['a', 'b']))\nFalse\n>>> is_unsigned_integer_dtype(pd.Series([1, 2])) # signed\nFalse\n>>> is_unsigned_integer_dtype(pd.Index([1, 2.])) # float\nFalse\n>>> is_unsigned_integer_dtype(np.array([1, 2], dtype=np.uint32))\nTrue", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 980, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L980", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT04", "Return value description should start with a capital letter"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 21, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[0]>\", line 1, in <module>\n is_unsigned_integer_dtype(str)\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(int) # signed\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[1]>\", line 1, in <module>\n is_unsigned_integer_dtype(int) # signed\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(float)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[2]>\", line 1, in <module>\n is_unsigned_integer_dtype(float)\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(np.uint64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[3]>\", line 1, in <module>\n is_unsigned_integer_dtype(np.uint64)\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype('uint8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[4]>\", line 1, in <module>\n is_unsigned_integer_dtype('uint8')\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 31, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype('UInt8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[5]>\", line 1, in <module>\n is_unsigned_integer_dtype('UInt8')\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 33, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(pd.UInt8Dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[6]>\", line 1, in <module>\n is_unsigned_integer_dtype(pd.UInt8Dtype)\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 35, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[7]>\", line 1, in <module>\n is_unsigned_integer_dtype(np.array(['a', 'b']))\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 37, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(pd.Series([1, 2])) # signed\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[8]>\", line 1, in <module>\n is_unsigned_integer_dtype(pd.Series([1, 2])) # signed\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 39, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(pd.Index([1, 2.])) # float\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[9]>\", line 1, in <module>\n is_unsigned_integer_dtype(pd.Index([1, 2.])) # float\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 41, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(np.array([1, 2], dtype=np.uint32))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[10]>\", line 1, in <module>\n is_unsigned_integer_dtype(np.array([1, 2], dtype=np.uint32))\n NameError: name 'is_unsigned_integer_dtype' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_unsigned_integer_dtype' (11 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 21, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(str)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[0]>\", line 1, in <module>\n is_unsigned_integer_dtype(str)\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(int) # signed\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[1]>\", line 1, in <module>\n is_unsigned_integer_dtype(int) # signed\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(float)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[2]>\", line 1, in <module>\n is_unsigned_integer_dtype(float)\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(np.uint64)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[3]>\", line 1, in <module>\n is_unsigned_integer_dtype(np.uint64)\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 29, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype('uint8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[4]>\", line 1, in <module>\n is_unsigned_integer_dtype('uint8')\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 31, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype('UInt8')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[5]>\", line 1, in <module>\n is_unsigned_integer_dtype('UInt8')\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 33, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(pd.UInt8Dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[6]>\", line 1, in <module>\n is_unsigned_integer_dtype(pd.UInt8Dtype)\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 35, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(np.array(['a', 'b']))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[7]>\", line 1, in <module>\n is_unsigned_integer_dtype(np.array(['a', 'b']))\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 37, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(pd.Series([1, 2])) # signed\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[8]>\", line 1, in <module>\n is_unsigned_integer_dtype(pd.Series([1, 2])) # signed\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 39, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(pd.Index([1, 2.])) # float\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[9]>\", line 1, in <module>\n is_unsigned_integer_dtype(pd.Index([1, 2.])) # float\n NameError: name 'is_unsigned_integer_dtype' is not defined\n**********************************************************************\nLine 41, in pandas.api.types.is_unsigned_integer_dtype\nFailed example:\n is_unsigned_integer_dtype(np.array([1, 2], dtype=np.uint32))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_unsigned_integer_dtype[10]>\", line 1, in <module>\n is_unsigned_integer_dtype(np.array([1, 2], dtype=np.uint32))\n NameError: name 'is_unsigned_integer_dtype' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_sparse": {"type": "function", "docstring": "Check whether an array-like is a 1-D pandas sparse array.\n\nCheck that the one-dimensional array-like is a pandas sparse array.\nReturns True if it is a pandas sparse array, not another type of\nsparse array.\n\nParameters\n----------\narr : array-like\n Array-like to check.\n\nReturns\n-------\nbool\n Whether or not the array-like is a pandas sparse array.\n\nSee Also\n--------\nDataFrame.to_sparse : Convert DataFrame to a SparseDataFrame.\nSeries.to_sparse : Convert Series to SparseSeries.\nSeries.to_dense : Return dense representation of a Series.\n\nExamples\n--------\nReturns `True` if the parameter is a 1-D pandas sparse array.\n\n>>> is_sparse(pd.SparseArray([0, 0, 1, 0]))\nTrue\n>>> is_sparse(pd.SparseSeries([0, 0, 1, 0]))\nTrue\n\nReturns `False` if the parameter is not sparse.\n\n>>> is_sparse(np.array([0, 0, 1, 0]))\nFalse\n>>> is_sparse(pd.Series([0, 1, 0, 0]))\nFalse\n\nReturns `False` if the parameter is not a pandas sparse array.\n\n>>> from scipy.sparse import bsr_matrix\n>>> is_sparse(bsr_matrix([0, 1, 0, 0]))\nFalse\n\nReturns `False` if the parameter has more than one dimension.\n\n>>> df = pd.SparseDataFrame([389., 24., 80.5, np.nan],\n columns=['max_speed'],\n index=['falcon', 'parrot', 'lion', 'monkey'])\n>>> is_sparse(df)\nFalse\n>>> is_sparse(df.max_speed)\nTrue", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 160, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L160", "errors": [["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 28, in pandas.api.types.is_sparse\nFailed example:\n is_sparse(pd.SparseArray([0, 0, 1, 0]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[0]>\", line 1, in <module>\n is_sparse(pd.SparseArray([0, 0, 1, 0]))\n NameError: name 'is_sparse' is not defined\n**********************************************************************\nLine 30, in pandas.api.types.is_sparse\nFailed example:\n is_sparse(pd.SparseSeries([0, 0, 1, 0]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[1]>\", line 1, in <module>\n is_sparse(pd.SparseSeries([0, 0, 1, 0]))\n NameError: name 'is_sparse' is not defined\n**********************************************************************\nLine 35, in pandas.api.types.is_sparse\nFailed example:\n is_sparse(np.array([0, 0, 1, 0]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[2]>\", line 1, in <module>\n is_sparse(np.array([0, 0, 1, 0]))\n NameError: name 'is_sparse' is not defined\n**********************************************************************\nLine 37, in pandas.api.types.is_sparse\nFailed example:\n is_sparse(pd.Series([0, 1, 0, 0]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[3]>\", line 1, in <module>\n is_sparse(pd.Series([0, 1, 0, 0]))\n NameError: name 'is_sparse' is not defined\n**********************************************************************\nLine 43, in pandas.api.types.is_sparse\nFailed example:\n is_sparse(bsr_matrix([0, 1, 0, 0]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[5]>\", line 1, in <module>\n is_sparse(bsr_matrix([0, 1, 0, 0]))\n NameError: name 'is_sparse' is not defined\n**********************************************************************\nLine 48, in pandas.api.types.is_sparse\nFailed example:\n df = pd.SparseDataFrame([389., 24., 80.5, np.nan],\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[6]>\", line 1\n df = pd.SparseDataFrame([389., 24., 80.5, np.nan],\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 51, in pandas.api.types.is_sparse\nFailed example:\n is_sparse(df)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[7]>\", line 1, in <module>\n is_sparse(df)\n NameError: name 'is_sparse' is not defined\n**********************************************************************\nLine 53, in pandas.api.types.is_sparse\nFailed example:\n is_sparse(df.max_speed)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[8]>\", line 1, in <module>\n is_sparse(df.max_speed)\n NameError: name 'is_sparse' is not defined\n"], ["EX03", "flake8 error: E902 TokenError: EOF in multi-line statement"], ["EX03", "flake8 error: E999 SyntaxError: invalid syntax"]], "warnings": [], "examples_errors": "**********************************************************************\nLine 28, in pandas.api.types.is_sparse\nFailed example:\n is_sparse(pd.SparseArray([0, 0, 1, 0]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[0]>\", line 1, in <module>\n is_sparse(pd.SparseArray([0, 0, 1, 0]))\n NameError: name 'is_sparse' is not defined\n**********************************************************************\nLine 30, in pandas.api.types.is_sparse\nFailed example:\n is_sparse(pd.SparseSeries([0, 0, 1, 0]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[1]>\", line 1, in <module>\n is_sparse(pd.SparseSeries([0, 0, 1, 0]))\n NameError: name 'is_sparse' is not defined\n**********************************************************************\nLine 35, in pandas.api.types.is_sparse\nFailed example:\n is_sparse(np.array([0, 0, 1, 0]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[2]>\", line 1, in <module>\n is_sparse(np.array([0, 0, 1, 0]))\n NameError: name 'is_sparse' is not defined\n**********************************************************************\nLine 37, in pandas.api.types.is_sparse\nFailed example:\n is_sparse(pd.Series([0, 1, 0, 0]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[3]>\", line 1, in <module>\n is_sparse(pd.Series([0, 1, 0, 0]))\n NameError: name 'is_sparse' is not defined\n**********************************************************************\nLine 43, in pandas.api.types.is_sparse\nFailed example:\n is_sparse(bsr_matrix([0, 1, 0, 0]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[5]>\", line 1, in <module>\n is_sparse(bsr_matrix([0, 1, 0, 0]))\n NameError: name 'is_sparse' is not defined\n**********************************************************************\nLine 48, in pandas.api.types.is_sparse\nFailed example:\n df = pd.SparseDataFrame([389., 24., 80.5, np.nan],\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[6]>\", line 1\n df = pd.SparseDataFrame([389., 24., 80.5, np.nan],\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 51, in pandas.api.types.is_sparse\nFailed example:\n is_sparse(df)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[7]>\", line 1, in <module>\n is_sparse(df)\n NameError: name 'is_sparse' is not defined\n**********************************************************************\nLine 53, in pandas.api.types.is_sparse\nFailed example:\n is_sparse(df.max_speed)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_sparse[8]>\", line 1, in <module>\n is_sparse(df.max_speed)\n NameError: name 'is_sparse' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Dtype introspection", "shared_code_with": ""}, "pandas.api.types.is_dict_like": {"type": "function", "docstring": "Check if the object is dict-like.\n\nParameters\n----------\nobj : The object to check\n\nReturns\n-------\nis_dict_like : bool\n Whether `obj` has dict-like properties.\n\nExamples\n--------\n>>> is_dict_like({1: 2})\nTrue\n>>> is_dict_like([1, 2, 3])\nFalse", "deprecated": false, "file": "pandas/core/dtypes/inference.py", "file_line": 381, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/inference.py#L381", "errors": [["PR07", "Parameter \"obj\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 15, in pandas.api.types.is_dict_like\nFailed example:\n is_dict_like({1: 2})\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_dict_like[0]>\", line 1, in <module>\n is_dict_like({1: 2})\n NameError: name 'is_dict_like' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_dict_like\nFailed example:\n is_dict_like([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_dict_like[1]>\", line 1, in <module>\n is_dict_like([1, 2, 3])\n NameError: name 'is_dict_like' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_dict_like' (2 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 15, in pandas.api.types.is_dict_like\nFailed example:\n is_dict_like({1: 2})\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_dict_like[0]>\", line 1, in <module>\n is_dict_like({1: 2})\n NameError: name 'is_dict_like' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_dict_like\nFailed example:\n is_dict_like([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_dict_like[1]>\", line 1, in <module>\n is_dict_like([1, 2, 3])\n NameError: name 'is_dict_like' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Iterable introspection", "shared_code_with": ""}, "pandas.api.types.is_file_like": {"type": "function", "docstring": "Check if the object is a file-like object.\n\nFor objects to be considered file-like, they must\nbe an iterator AND have either a `read` and/or `write`\nmethod as an attribute.\n\nNote: file-like objects must be iterable, but\niterable objects need not be file-like.\n\n.. versionadded:: 0.20.0\n\nParameters\n----------\nobj : The object to check\n\nReturns\n-------\nis_file_like : bool\n Whether `obj` has file-like properties.\n\nExamples\n--------\n>>> buffer(StringIO(\"data\"))\n>>> is_file_like(buffer)\nTrue\n>>> is_file_like([1, 2, 3])\nFalse", "deprecated": false, "file": "pandas/core/dtypes/inference.py", "file_line": 160, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/inference.py#L160", "errors": [["PR07", "Parameter \"obj\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 24, in pandas.api.types.is_file_like\nFailed example:\n buffer(StringIO(\"data\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_file_like[0]>\", line 1, in <module>\n buffer(StringIO(\"data\"))\n NameError: name 'buffer' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_file_like\nFailed example:\n is_file_like(buffer)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_file_like[1]>\", line 1, in <module>\n is_file_like(buffer)\n NameError: name 'is_file_like' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_file_like\nFailed example:\n is_file_like([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_file_like[2]>\", line 1, in <module>\n is_file_like([1, 2, 3])\n NameError: name 'is_file_like' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'buffer' (5 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 24, in pandas.api.types.is_file_like\nFailed example:\n buffer(StringIO(\"data\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_file_like[0]>\", line 1, in <module>\n buffer(StringIO(\"data\"))\n NameError: name 'buffer' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_file_like\nFailed example:\n is_file_like(buffer)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_file_like[1]>\", line 1, in <module>\n is_file_like(buffer)\n NameError: name 'is_file_like' is not defined\n**********************************************************************\nLine 27, in pandas.api.types.is_file_like\nFailed example:\n is_file_like([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_file_like[2]>\", line 1, in <module>\n is_file_like([1, 2, 3])\n NameError: name 'is_file_like' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Iterable introspection", "shared_code_with": ""}, "pandas.api.types.is_list_like": {"type": "function", "docstring": "Check if the object is list-like.\n\nObjects that are considered list-like are for example Python\nlists, tuples, sets, NumPy arrays, and Pandas Series.\n\nStrings and datetime objects, however, are not considered list-like.\n\nParameters\n----------\nobj : The object to check\nallow_sets : boolean, default True\n If this parameter is False, sets will not be considered list-like\n\n .. versionadded:: 0.24.0\n\nReturns\n-------\nis_list_like : bool\n Whether `obj` has list-like properties.\n\nExamples\n--------\n>>> is_list_like([1, 2, 3])\nTrue\n>>> is_list_like({1, 2, 3})\nTrue\n>>> is_list_like(datetime(2017, 1, 1))\nFalse\n>>> is_list_like(\"foo\")\nFalse\n>>> is_list_like(1)\nFalse\n>>> is_list_like(np.array([2]))\nTrue\n>>> is_list_like(np.array(2)))\nFalse", "deprecated": false, "file": "pandas/core/dtypes/inference.py", "file_line": 253, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/inference.py#L253", "errors": [["PR07", "Parameter \"obj\" has no description"], ["PR06", "Parameter \"allow_sets\" type should use \"bool\" instead of \"boolean\""], ["PR09", "Parameter \"allow_sets\" description should finish with \".\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 24, in pandas.api.types.is_list_like\nFailed example:\n is_list_like([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_list_like[0]>\", line 1, in <module>\n is_list_like([1, 2, 3])\n NameError: name 'is_list_like' is not defined\n**********************************************************************\nLine 26, in pandas.api.types.is_list_like\nFailed example:\n is_list_like({1, 2, 3})\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_list_like[1]>\", line 1, in <module>\n is_list_like({1, 2, 3})\n NameError: name 'is_list_like' is not defined\n**********************************************************************\nLine 28, in pandas.api.types.is_list_like\nFailed example:\n is_list_like(datetime(2017, 1, 1))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_list_like[2]>\", line 1, in <module>\n is_list_like(datetime(2017, 1, 1))\n NameError: name 'is_list_like' is not defined\n**********************************************************************\nLine 30, in pandas.api.types.is_list_like\nFailed example:\n is_list_like(\"foo\")\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_list_like[3]>\", line 1, in <module>\n is_list_like(\"foo\")\n NameError: name 'is_list_like' is not defined\n**********************************************************************\nLine 32, in pandas.api.types.is_list_like\nFailed example:\n is_list_like(1)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_list_like[4]>\", line 1, in <module>\n is_list_like(1)\n NameError: name 'is_list_like' is not defined\n**********************************************************************\nLine 34, in pandas.api.types.is_list_like\nFailed example:\n is_list_like(np.array([2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_list_like[5]>\", line 1, in <module>\n is_list_like(np.array([2]))\n NameError: name 'is_list_like' is not defined\n**********************************************************************\nLine 36, in pandas.api.types.is_list_like\nFailed example:\n is_list_like(np.array(2)))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_list_like[6]>\", line 1\n is_list_like(np.array(2)))\n ^\n SyntaxError: invalid syntax\n"], ["EX03", "flake8 error: E902 TokenError: EOF in multi-line statement"], ["EX03", "flake8 error: E999 SyntaxError: invalid syntax"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 24, in pandas.api.types.is_list_like\nFailed example:\n is_list_like([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_list_like[0]>\", line 1, in <module>\n is_list_like([1, 2, 3])\n NameError: name 'is_list_like' is not defined\n**********************************************************************\nLine 26, in pandas.api.types.is_list_like\nFailed example:\n is_list_like({1, 2, 3})\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_list_like[1]>\", line 1, in <module>\n is_list_like({1, 2, 3})\n NameError: name 'is_list_like' is not defined\n**********************************************************************\nLine 28, in pandas.api.types.is_list_like\nFailed example:\n is_list_like(datetime(2017, 1, 1))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_list_like[2]>\", line 1, in <module>\n is_list_like(datetime(2017, 1, 1))\n NameError: name 'is_list_like' is not defined\n**********************************************************************\nLine 30, in pandas.api.types.is_list_like\nFailed example:\n is_list_like(\"foo\")\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_list_like[3]>\", line 1, in <module>\n is_list_like(\"foo\")\n NameError: name 'is_list_like' is not defined\n**********************************************************************\nLine 32, in pandas.api.types.is_list_like\nFailed example:\n is_list_like(1)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_list_like[4]>\", line 1, in <module>\n is_list_like(1)\n NameError: name 'is_list_like' is not defined\n**********************************************************************\nLine 34, in pandas.api.types.is_list_like\nFailed example:\n is_list_like(np.array([2]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_list_like[5]>\", line 1, in <module>\n is_list_like(np.array([2]))\n NameError: name 'is_list_like' is not defined\n**********************************************************************\nLine 36, in pandas.api.types.is_list_like\nFailed example:\n is_list_like(np.array(2)))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_list_like[6]>\", line 1\n is_list_like(np.array(2)))\n ^\n SyntaxError: invalid syntax\n", "in_api": true, "section": "Data types related functionality", "subsection": "Iterable introspection", "shared_code_with": ""}, "pandas.api.types.is_named_tuple": {"type": "function", "docstring": "Check if the object is a named tuple.\n\nParameters\n----------\nobj : The object to check\n\nReturns\n-------\nis_named_tuple : bool\n Whether `obj` is a named tuple.\n\nExamples\n--------\n>>> Point = namedtuple(\"Point\", [\"x\", \"y\"])\n>>> p = Point(1, 2)\n>>>\n>>> is_named_tuple(p)\nTrue\n>>> is_named_tuple((1, 2))\nFalse", "deprecated": false, "file": "pandas/core/dtypes/inference.py", "file_line": 408, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/inference.py#L408", "errors": [["PR07", "Parameter \"obj\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 15, in pandas.api.types.is_named_tuple\nFailed example:\n Point = namedtuple(\"Point\", [\"x\", \"y\"])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_named_tuple[0]>\", line 1, in <module>\n Point = namedtuple(\"Point\", [\"x\", \"y\"])\n NameError: name 'namedtuple' is not defined\n**********************************************************************\nLine 16, in pandas.api.types.is_named_tuple\nFailed example:\n p = Point(1, 2)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_named_tuple[1]>\", line 1, in <module>\n p = Point(1, 2)\n NameError: name 'Point' is not defined\n**********************************************************************\nLine 18, in pandas.api.types.is_named_tuple\nFailed example:\n is_named_tuple(p)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_named_tuple[2]>\", line 1, in <module>\n is_named_tuple(p)\n NameError: name 'is_named_tuple' is not defined\n**********************************************************************\nLine 20, in pandas.api.types.is_named_tuple\nFailed example:\n is_named_tuple((1, 2))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_named_tuple[3]>\", line 1, in <module>\n is_named_tuple((1, 2))\n NameError: name 'is_named_tuple' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'namedtuple' (3 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 15, in pandas.api.types.is_named_tuple\nFailed example:\n Point = namedtuple(\"Point\", [\"x\", \"y\"])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_named_tuple[0]>\", line 1, in <module>\n Point = namedtuple(\"Point\", [\"x\", \"y\"])\n NameError: name 'namedtuple' is not defined\n**********************************************************************\nLine 16, in pandas.api.types.is_named_tuple\nFailed example:\n p = Point(1, 2)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_named_tuple[1]>\", line 1, in <module>\n p = Point(1, 2)\n NameError: name 'Point' is not defined\n**********************************************************************\nLine 18, in pandas.api.types.is_named_tuple\nFailed example:\n is_named_tuple(p)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_named_tuple[2]>\", line 1, in <module>\n is_named_tuple(p)\n NameError: name 'is_named_tuple' is not defined\n**********************************************************************\nLine 20, in pandas.api.types.is_named_tuple\nFailed example:\n is_named_tuple((1, 2))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_named_tuple[3]>\", line 1, in <module>\n is_named_tuple((1, 2))\n NameError: name 'is_named_tuple' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Iterable introspection", "shared_code_with": ""}, "pandas.api.types.is_iterator": {"type": "function", "docstring": "Check if the object is an iterator.\n\nFor example, lists are considered iterators\nbut not strings or datetime objects.\n\nParameters\n----------\nobj : The object to check\n\nReturns\n-------\nis_iter : bool\n Whether `obj` is an iterator.\n\nExamples\n--------\n>>> is_iterator([1, 2, 3])\nTrue\n>>> is_iterator(datetime(2017, 1, 1))\nFalse\n>>> is_iterator(\"foo\")\nFalse\n>>> is_iterator(1)\nFalse", "deprecated": false, "file": "pandas/core/dtypes/inference.py", "file_line": 121, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/inference.py#L121", "errors": [["PR07", "Parameter \"obj\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 18, in pandas.api.types.is_iterator\nFailed example:\n is_iterator([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_iterator[0]>\", line 1, in <module>\n is_iterator([1, 2, 3])\n NameError: name 'is_iterator' is not defined\n**********************************************************************\nLine 20, in pandas.api.types.is_iterator\nFailed example:\n is_iterator(datetime(2017, 1, 1))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_iterator[1]>\", line 1, in <module>\n is_iterator(datetime(2017, 1, 1))\n NameError: name 'is_iterator' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_iterator\nFailed example:\n is_iterator(\"foo\")\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_iterator[2]>\", line 1, in <module>\n is_iterator(\"foo\")\n NameError: name 'is_iterator' is not defined\n**********************************************************************\nLine 24, in pandas.api.types.is_iterator\nFailed example:\n is_iterator(1)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_iterator[3]>\", line 1, in <module>\n is_iterator(1)\n NameError: name 'is_iterator' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_iterator' (5 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 18, in pandas.api.types.is_iterator\nFailed example:\n is_iterator([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_iterator[0]>\", line 1, in <module>\n is_iterator([1, 2, 3])\n NameError: name 'is_iterator' is not defined\n**********************************************************************\nLine 20, in pandas.api.types.is_iterator\nFailed example:\n is_iterator(datetime(2017, 1, 1))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_iterator[1]>\", line 1, in <module>\n is_iterator(datetime(2017, 1, 1))\n NameError: name 'is_iterator' is not defined\n**********************************************************************\nLine 22, in pandas.api.types.is_iterator\nFailed example:\n is_iterator(\"foo\")\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_iterator[2]>\", line 1, in <module>\n is_iterator(\"foo\")\n NameError: name 'is_iterator' is not defined\n**********************************************************************\nLine 24, in pandas.api.types.is_iterator\nFailed example:\n is_iterator(1)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_iterator[3]>\", line 1, in <module>\n is_iterator(1)\n NameError: name 'is_iterator' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Iterable introspection", "shared_code_with": ""}, "pandas.api.types.is_bool": {"type": "builtin_function_or_method", "docstring": "", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Data types related functionality", "subsection": "Scalar introspection", "shared_code_with": "pandas.api.types.infer_dtype"}, "pandas.api.types.is_categorical": {"type": "function", "docstring": "Check whether an array-like is a Categorical instance.\n\nParameters\n----------\narr : array-like\n The array-like to check.\n\nReturns\n-------\nboolean : Whether or not the array-like is of a Categorical instance.\n\nExamples\n--------\n>>> is_categorical([1, 2, 3])\nFalse\n\nCategoricals, Series Categoricals, and CategoricalIndex will return True.\n\n>>> cat = pd.Categorical([1, 2, 3])\n>>> is_categorical(cat)\nTrue\n>>> is_categorical(pd.Series(cat))\nTrue\n>>> is_categorical(pd.CategoricalIndex([1, 2, 3]))\nTrue", "deprecated": false, "file": "pandas/core/dtypes/common.py", "file_line": 262, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L262", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 15, in pandas.api.types.is_categorical\nFailed example:\n is_categorical([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical[0]>\", line 1, in <module>\n is_categorical([1, 2, 3])\n NameError: name 'is_categorical' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_categorical\nFailed example:\n is_categorical(cat)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical[2]>\", line 1, in <module>\n is_categorical(cat)\n NameError: name 'is_categorical' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_categorical\nFailed example:\n is_categorical(pd.Series(cat))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical[3]>\", line 1, in <module>\n is_categorical(pd.Series(cat))\n NameError: name 'is_categorical' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_categorical\nFailed example:\n is_categorical(pd.CategoricalIndex([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical[4]>\", line 1, in <module>\n is_categorical(pd.CategoricalIndex([1, 2, 3]))\n NameError: name 'is_categorical' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_categorical' (4 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 15, in pandas.api.types.is_categorical\nFailed example:\n is_categorical([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical[0]>\", line 1, in <module>\n is_categorical([1, 2, 3])\n NameError: name 'is_categorical' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_categorical\nFailed example:\n is_categorical(cat)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical[2]>\", line 1, in <module>\n is_categorical(cat)\n NameError: name 'is_categorical' is not defined\n**********************************************************************\nLine 23, in pandas.api.types.is_categorical\nFailed example:\n is_categorical(pd.Series(cat))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical[3]>\", line 1, in <module>\n is_categorical(pd.Series(cat))\n NameError: name 'is_categorical' is not defined\n**********************************************************************\nLine 25, in pandas.api.types.is_categorical\nFailed example:\n is_categorical(pd.CategoricalIndex([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_categorical[4]>\", line 1, in <module>\n is_categorical(pd.CategoricalIndex([1, 2, 3]))\n NameError: name 'is_categorical' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Scalar introspection", "shared_code_with": ""}, "pandas.api.types.is_complex": {"type": "builtin_function_or_method", "docstring": "", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Data types related functionality", "subsection": "Scalar introspection", "shared_code_with": "pandas.api.types.is_bool"}, "pandas.api.types.is_datetimetz": {"type": "function", "docstring": "Check whether an array-like is a datetime array-like with a timezone\ncomponent in its dtype.\n\n.. deprecated:: 0.24.0\n\nParameters\n----------\narr : array-like\n The array-like to check.\n\nReturns\n-------\nboolean : Whether or not the array-like is a datetime array-like with\n a timezone component in its dtype.\n\nExamples\n--------\n>>> is_datetimetz([1, 2, 3])\nFalse\n\nAlthough the following examples are both DatetimeIndex objects,\nthe first one returns False because it has no timezone component\nunlike the second one, which returns True.\n\n>>> is_datetimetz(pd.DatetimeIndex([1, 2, 3]))\nFalse\n>>> is_datetimetz(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\nTrue\n\nThe object need not be a DatetimeIndex object. It just needs to have\na dtype which has a timezone component.\n\n>>> dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\n>>> s = pd.Series([], dtype=dtype)\n>>> is_datetimetz(s)\nTrue", "deprecated": true, "file": "pandas/core/dtypes/common.py", "file_line": 294, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L294", "errors": [["SS06", "Summary should fit in a single line"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT04", "Return value description should start with a capital letter"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 19, in pandas.api.types.is_datetimetz\nFailed example:\n is_datetimetz([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetimetz[0]>\", line 1, in <module>\n is_datetimetz([1, 2, 3])\n NameError: name 'is_datetimetz' is not defined\n**********************************************************************\nLine 26, in pandas.api.types.is_datetimetz\nFailed example:\n is_datetimetz(pd.DatetimeIndex([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetimetz[1]>\", line 1, in <module>\n is_datetimetz(pd.DatetimeIndex([1, 2, 3]))\n NameError: name 'is_datetimetz' is not defined\n**********************************************************************\nLine 28, in pandas.api.types.is_datetimetz\nFailed example:\n is_datetimetz(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetimetz[2]>\", line 1, in <module>\n is_datetimetz(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\n NameError: name 'is_datetimetz' is not defined\n**********************************************************************\nLine 34, in pandas.api.types.is_datetimetz\nFailed example:\n dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetimetz[3]>\", line 1, in <module>\n dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\n NameError: name 'DatetimeTZDtype' is not defined\n**********************************************************************\nLine 35, in pandas.api.types.is_datetimetz\nFailed example:\n s = pd.Series([], dtype=dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetimetz[4]>\", line 1, in <module>\n s = pd.Series([], dtype=dtype)\n NameError: name 'dtype' is not defined\n**********************************************************************\nLine 36, in pandas.api.types.is_datetimetz\nFailed example:\n is_datetimetz(s)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetimetz[5]>\", line 1, in <module>\n is_datetimetz(s)\n NameError: name 'is_datetimetz' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_datetimetz' (5 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 19, in pandas.api.types.is_datetimetz\nFailed example:\n is_datetimetz([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetimetz[0]>\", line 1, in <module>\n is_datetimetz([1, 2, 3])\n NameError: name 'is_datetimetz' is not defined\n**********************************************************************\nLine 26, in pandas.api.types.is_datetimetz\nFailed example:\n is_datetimetz(pd.DatetimeIndex([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetimetz[1]>\", line 1, in <module>\n is_datetimetz(pd.DatetimeIndex([1, 2, 3]))\n NameError: name 'is_datetimetz' is not defined\n**********************************************************************\nLine 28, in pandas.api.types.is_datetimetz\nFailed example:\n is_datetimetz(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetimetz[2]>\", line 1, in <module>\n is_datetimetz(pd.DatetimeIndex([1, 2, 3], tz=\"US/Eastern\"))\n NameError: name 'is_datetimetz' is not defined\n**********************************************************************\nLine 34, in pandas.api.types.is_datetimetz\nFailed example:\n dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetimetz[3]>\", line 1, in <module>\n dtype = DatetimeTZDtype(\"ns\", tz=\"US/Eastern\")\n NameError: name 'DatetimeTZDtype' is not defined\n**********************************************************************\nLine 35, in pandas.api.types.is_datetimetz\nFailed example:\n s = pd.Series([], dtype=dtype)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetimetz[4]>\", line 1, in <module>\n s = pd.Series([], dtype=dtype)\n NameError: name 'dtype' is not defined\n**********************************************************************\nLine 36, in pandas.api.types.is_datetimetz\nFailed example:\n is_datetimetz(s)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_datetimetz[5]>\", line 1, in <module>\n is_datetimetz(s)\n NameError: name 'is_datetimetz' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Scalar introspection", "shared_code_with": ""}, "pandas.api.types.is_float": {"type": "builtin_function_or_method", "docstring": "", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Data types related functionality", "subsection": "Scalar introspection", "shared_code_with": "pandas.api.types.is_complex"}, "pandas.api.types.is_hashable": {"type": "function", "docstring": "Return True if hash(obj) will succeed, False otherwise.\n\nSome types will pass a test against collections.Hashable but fail when they\nare actually hashed with hash().\n\nDistinguish between these and other types by trying the call to hash() and\nseeing if they raise TypeError.\n\nExamples\n--------\n>>> a = ([],)\n>>> isinstance(a, collections.Hashable)\nTrue\n>>> is_hashable(a)\nFalse", "deprecated": false, "file": "pandas/core/dtypes/inference.py", "file_line": 435, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/inference.py#L435", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["PR01", "Parameters {obj} not documented"], ["RT01", "No Returns section found"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 12, in pandas.api.types.is_hashable\nFailed example:\n isinstance(a, collections.Hashable)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_hashable[1]>\", line 1, in <module>\n isinstance(a, collections.Hashable)\n NameError: name 'collections' is not defined\n**********************************************************************\nLine 14, in pandas.api.types.is_hashable\nFailed example:\n is_hashable(a)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_hashable[2]>\", line 1, in <module>\n is_hashable(a)\n NameError: name 'is_hashable' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'collections' (2 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 12, in pandas.api.types.is_hashable\nFailed example:\n isinstance(a, collections.Hashable)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_hashable[1]>\", line 1, in <module>\n isinstance(a, collections.Hashable)\n NameError: name 'collections' is not defined\n**********************************************************************\nLine 14, in pandas.api.types.is_hashable\nFailed example:\n is_hashable(a)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_hashable[2]>\", line 1, in <module>\n is_hashable(a)\n NameError: name 'is_hashable' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Scalar introspection", "shared_code_with": ""}, "pandas.api.types.is_integer": {"type": "builtin_function_or_method", "docstring": "", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Data types related functionality", "subsection": "Scalar introspection", "shared_code_with": "pandas.api.types.is_float"}, "pandas.api.types.is_interval": {"type": "builtin_function_or_method", "docstring": "", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Data types related functionality", "subsection": "Scalar introspection", "shared_code_with": "pandas.api.types.is_integer"}, "pandas.api.types.is_number": {"type": "function", "docstring": "Check if the object is a number.\n\nReturns True when the object is a number, and False if is not.\n\nParameters\n----------\nobj : any type\n The object to check if is a number.\n\nReturns\n-------\nis_number : bool\n Whether `obj` is a number or not.\n\nSee Also\n--------\npandas.api.types.is_integer: Checks a subgroup of numbers.\n\nExamples\n--------\n>>> pd.api.types.is_number(1)\nTrue\n>>> pd.api.types.is_number(7.15)\nTrue\n\nBooleans are valid because they are int subclass.\n\n>>> pd.api.types.is_number(False)\nTrue\n\n>>> pd.api.types.is_number(\"foo\")\nFalse\n>>> pd.api.types.is_number(\"5\")\nFalse", "deprecated": false, "file": "pandas/core/dtypes/inference.py", "file_line": 29, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/inference.py#L29", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["SA05", "pandas.api.types.is_integer in `See Also` section does not need `pandas` prefix, use api.types.is_integer instead."]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Data types related functionality", "subsection": "Scalar introspection", "shared_code_with": ""}, "pandas.api.types.is_period": {"type": "function", "docstring": "Check whether an array-like is a periodical index.\n\n.. deprecated:: 0.24.0\n\nParameters\n----------\narr : array-like\n The array-like to check.\n\nReturns\n-------\nboolean : Whether or not the array-like is a periodical index.\n\nExamples\n--------\n>>> is_period([1, 2, 3])\nFalse\n>>> is_period(pd.Index([1, 2, 3]))\nFalse\n>>> is_period(pd.PeriodIndex([\"2017-01-01\"], freq=\"D\"))\nTrue", "deprecated": true, "file": "pandas/core/dtypes/common.py", "file_line": 371, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/common.py#L371", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 17, in pandas.api.types.is_period\nFailed example:\n is_period([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period[0]>\", line 1, in <module>\n is_period([1, 2, 3])\n NameError: name 'is_period' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_period\nFailed example:\n is_period(pd.Index([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period[1]>\", line 1, in <module>\n is_period(pd.Index([1, 2, 3]))\n NameError: name 'is_period' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_period\nFailed example:\n is_period(pd.PeriodIndex([\"2017-01-01\"], freq=\"D\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period[2]>\", line 1, in <module>\n is_period(pd.PeriodIndex([\"2017-01-01\"], freq=\"D\"))\n NameError: name 'is_period' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_period' (3 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 17, in pandas.api.types.is_period\nFailed example:\n is_period([1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period[0]>\", line 1, in <module>\n is_period([1, 2, 3])\n NameError: name 'is_period' is not defined\n**********************************************************************\nLine 19, in pandas.api.types.is_period\nFailed example:\n is_period(pd.Index([1, 2, 3]))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period[1]>\", line 1, in <module>\n is_period(pd.Index([1, 2, 3]))\n NameError: name 'is_period' is not defined\n**********************************************************************\nLine 21, in pandas.api.types.is_period\nFailed example:\n is_period(pd.PeriodIndex([\"2017-01-01\"], freq=\"D\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_period[2]>\", line 1, in <module>\n is_period(pd.PeriodIndex([\"2017-01-01\"], freq=\"D\"))\n NameError: name 'is_period' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Scalar introspection", "shared_code_with": ""}, "pandas.api.types.is_re": {"type": "function", "docstring": "Check if the object is a regex pattern instance.\n\nParameters\n----------\nobj : The object to check\n\nReturns\n-------\nis_regex : bool\n Whether `obj` is a regex pattern.\n\nExamples\n--------\n>>> is_re(re.compile(\".*\"))\nTrue\n>>> is_re(\"foo\")\nFalse", "deprecated": false, "file": "pandas/core/dtypes/inference.py", "file_line": 200, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/inference.py#L200", "errors": [["PR07", "Parameter \"obj\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 15, in pandas.api.types.is_re\nFailed example:\n is_re(re.compile(\".*\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_re[0]>\", line 1, in <module>\n is_re(re.compile(\".*\"))\n NameError: name 'is_re' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_re\nFailed example:\n is_re(\"foo\")\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_re[1]>\", line 1, in <module>\n is_re(\"foo\")\n NameError: name 'is_re' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_re' (3 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 15, in pandas.api.types.is_re\nFailed example:\n is_re(re.compile(\".*\"))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_re[0]>\", line 1, in <module>\n is_re(re.compile(\".*\"))\n NameError: name 'is_re' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_re\nFailed example:\n is_re(\"foo\")\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_re[1]>\", line 1, in <module>\n is_re(\"foo\")\n NameError: name 'is_re' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Scalar introspection", "shared_code_with": ""}, "pandas.api.types.is_re_compilable": {"type": "function", "docstring": "Check if the object can be compiled into a regex pattern instance.\n\nParameters\n----------\nobj : The object to check\n\nReturns\n-------\nis_regex_compilable : bool\n Whether `obj` can be compiled as a regex pattern.\n\nExamples\n--------\n>>> is_re_compilable(\".*\")\nTrue\n>>> is_re_compilable(1)\nFalse", "deprecated": false, "file": "pandas/core/dtypes/inference.py", "file_line": 224, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/dtypes/inference.py#L224", "errors": [["PR07", "Parameter \"obj\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 15, in pandas.api.types.is_re_compilable\nFailed example:\n is_re_compilable(\".*\")\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_re_compilable[0]>\", line 1, in <module>\n is_re_compilable(\".*\")\n NameError: name 'is_re_compilable' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_re_compilable\nFailed example:\n is_re_compilable(1)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_re_compilable[1]>\", line 1, in <module>\n is_re_compilable(1)\n NameError: name 'is_re_compilable' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'is_re_compilable' (2 times)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 15, in pandas.api.types.is_re_compilable\nFailed example:\n is_re_compilable(\".*\")\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_re_compilable[0]>\", line 1, in <module>\n is_re_compilable(\".*\")\n NameError: name 'is_re_compilable' is not defined\n**********************************************************************\nLine 17, in pandas.api.types.is_re_compilable\nFailed example:\n is_re_compilable(1)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_re_compilable[1]>\", line 1, in <module>\n is_re_compilable(1)\n NameError: name 'is_re_compilable' is not defined\n", "in_api": true, "section": "Data types related functionality", "subsection": "Scalar introspection", "shared_code_with": ""}, "pandas.api.types.is_scalar": {"type": "builtin_function_or_method", "docstring": "Return True if given value is scalar.\n\nParameters\n----------\nval : object\n This includes:\n\n - numpy array scalar (e.g. np.int64)\n - Python builtin numerics\n - Python builtin byte arrays and strings\n - None\n - datetime.datetime\n - datetime.timedelta\n - Period\n - decimal.Decimal\n - Interval\n - DateOffset\n - Fraction\n - Number\n\nReturns\n-------\nbool\n Return True if given object is scalar, False otherwise\n\nExamples\n--------\n>>> dt = pd.datetime.datetime(2018, 10, 3)\n>>> pd.is_scalar(dt)\nTrue\n\n>>> pd.api.types.is_scalar([2, 3])\nFalse\n\n>>> pd.api.types.is_scalar({0: 1, 2: 3})\nFalse\n\n>>> pd.api.types.is_scalar((0, 2))\nFalse\n\npandas supports PEP 3141 numbers:\n\n>>> from fractions import Fraction\n>>> pd.api.types.is_scalar(Fraction(3, 5))\nTrue", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["PR02", "Unknown parameters {val}"], ["PR09", "Parameter \"val\" description should finish with \".\""], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 29, in pandas.api.types.is_scalar\nFailed example:\n dt = pd.datetime.datetime(2018, 10, 3)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_scalar[0]>\", line 1, in <module>\n dt = pd.datetime.datetime(2018, 10, 3)\n AttributeError: type object 'datetime.datetime' has no attribute 'datetime'\n**********************************************************************\nLine 30, in pandas.api.types.is_scalar\nFailed example:\n pd.is_scalar(dt)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_scalar[1]>\", line 1, in <module>\n pd.is_scalar(dt)\n AttributeError: module 'pandas' has no attribute 'is_scalar'\n"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 29, in pandas.api.types.is_scalar\nFailed example:\n dt = pd.datetime.datetime(2018, 10, 3)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_scalar[0]>\", line 1, in <module>\n dt = pd.datetime.datetime(2018, 10, 3)\n AttributeError: type object 'datetime.datetime' has no attribute 'datetime'\n**********************************************************************\nLine 30, in pandas.api.types.is_scalar\nFailed example:\n pd.is_scalar(dt)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.api.types.is_scalar[1]>\", line 1, in <module>\n pd.is_scalar(dt)\n AttributeError: module 'pandas' has no attribute 'is_scalar'\n", "in_api": true, "section": "Data types related functionality", "subsection": "Scalar introspection", "shared_code_with": "pandas.api.types.is_interval"}, "pandas.Index": {"type": "type", "docstring": "Immutable ndarray implementing an ordered, sliceable set. The basic object\nstoring axis labels for all pandas objects.\n\nParameters\n----------\ndata : array-like (1-dimensional)\ndtype : NumPy dtype (default: object)\n If dtype is None, we find the dtype that best fits the data.\n If an actual dtype is provided, we coerce to that dtype if it's safe.\n Otherwise, an error will be raised.\ncopy : bool\n Make a copy of input ndarray\nname : object\n Name to be stored in the index\ntupleize_cols : bool (default: True)\n When True, attempt to create a MultiIndex if possible\n\nSee Also\n---------\nRangeIndex : Index implementing a monotonic integer range.\nCategoricalIndex : Index of :class:`Categorical` s.\nMultiIndex : A multi-level, or hierarchical, Index.\nIntervalIndex : An Index of :class:`Interval` s.\nDatetimeIndex, TimedeltaIndex, PeriodIndex\nInt64Index, UInt64Index, Float64Index\n\nNotes\n-----\nAn Index instance can **only** contain hashable objects\n\nExamples\n--------\n>>> pd.Index([1, 2, 3])\nInt64Index([1, 2, 3], dtype='int64')\n\n>>> pd.Index(list('abc'))\nIndex(['a', 'b', 'c'], dtype='object')", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 165, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L165", "errors": [["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {**kwargs, fastpath} not documented"], ["PR07", "Parameter \"data\" has no description"], ["PR09", "Parameter \"copy\" description should finish with \".\""], ["PR09", "Parameter \"name\" description should finish with \".\""], ["PR09", "Parameter \"tupleize_cols\" description should finish with \".\""], ["SA04", "Missing description for See Also \"DatetimeIndex\" reference"], ["SA04", "Missing description for See Also \"TimedeltaIndex\" reference"], ["SA04", "Missing description for See Also \"PeriodIndex\" reference"], ["SA04", "Missing description for See Also \"Int64Index\" reference"], ["SA04", "Missing description for See Also \"UInt64Index\" reference"], ["SA04", "Missing description for See Also \"Float64Index\" reference"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "", "shared_code_with": ""}, "pandas.Index.values": {"type": "property", "docstring": "Return an array representing the data in the Index.\n\n.. warning::\n\n We recommend using :attr:`Index.array` or\n :meth:`Index.to_numpy`, depending on whether you need\n a reference to the underlying data or a NumPy array.\n\nReturns\n-------\narray: numpy.ndarray or ExtensionArray\n\nSee Also\n--------\nIndex.array : Reference to the underlying data.\nIndex.to_numpy : A NumPy array representing the underlying data.\n\nReturn the underlying data as an ndarray.", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 3607, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L3607", "errors": [["SA04", "Missing description for See Also \"Return\" reference"]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.is_monotonic": {"type": "property", "docstring": "Alias for is_monotonic_increasing.", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1579, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1579", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.is_monotonic_increasing": {"type": "property", "docstring": "Return if the index is monotonic increasing (only equal or\nincreasing) values.\n\nExamples\n--------\n>>> Index([1, 2, 3]).is_monotonic_increasing\nTrue\n>>> Index([1, 2, 2]).is_monotonic_increasing\nTrue\n>>> Index([1, 3, 2]).is_monotonic_increasing\nFalse", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1586, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1586", "errors": [["SS06", "Summary should fit in a single line"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 7, in pandas.Index.is_monotonic_increasing\nFailed example:\n Index([1, 2, 3]).is_monotonic_increasing\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.is_monotonic_increasing[0]>\", line 1, in <module>\n Index([1, 2, 3]).is_monotonic_increasing\n NameError: name 'Index' is not defined\n**********************************************************************\nLine 9, in pandas.Index.is_monotonic_increasing\nFailed example:\n Index([1, 2, 2]).is_monotonic_increasing\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.is_monotonic_increasing[1]>\", line 1, in <module>\n Index([1, 2, 2]).is_monotonic_increasing\n NameError: name 'Index' is not defined\n**********************************************************************\nLine 11, in pandas.Index.is_monotonic_increasing\nFailed example:\n Index([1, 3, 2]).is_monotonic_increasing\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.is_monotonic_increasing[2]>\", line 1, in <module>\n Index([1, 3, 2]).is_monotonic_increasing\n NameError: name 'Index' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'Index' (3 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 7, in pandas.Index.is_monotonic_increasing\nFailed example:\n Index([1, 2, 3]).is_monotonic_increasing\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.is_monotonic_increasing[0]>\", line 1, in <module>\n Index([1, 2, 3]).is_monotonic_increasing\n NameError: name 'Index' is not defined\n**********************************************************************\nLine 9, in pandas.Index.is_monotonic_increasing\nFailed example:\n Index([1, 2, 2]).is_monotonic_increasing\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.is_monotonic_increasing[1]>\", line 1, in <module>\n Index([1, 2, 2]).is_monotonic_increasing\n NameError: name 'Index' is not defined\n**********************************************************************\nLine 11, in pandas.Index.is_monotonic_increasing\nFailed example:\n Index([1, 3, 2]).is_monotonic_increasing\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.is_monotonic_increasing[2]>\", line 1, in <module>\n Index([1, 3, 2]).is_monotonic_increasing\n NameError: name 'Index' is not defined\n", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.is_monotonic_decreasing": {"type": "property", "docstring": "Return if the index is monotonic decreasing (only equal or\ndecreasing) values.\n\nExamples\n--------\n>>> Index([3, 2, 1]).is_monotonic_decreasing\nTrue\n>>> Index([3, 2, 2]).is_monotonic_decreasing\nTrue\n>>> Index([3, 1, 2]).is_monotonic_decreasing\nFalse", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1603, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1603", "errors": [["SS06", "Summary should fit in a single line"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 7, in pandas.Index.is_monotonic_decreasing\nFailed example:\n Index([3, 2, 1]).is_monotonic_decreasing\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.is_monotonic_decreasing[0]>\", line 1, in <module>\n Index([3, 2, 1]).is_monotonic_decreasing\n NameError: name 'Index' is not defined\n**********************************************************************\nLine 9, in pandas.Index.is_monotonic_decreasing\nFailed example:\n Index([3, 2, 2]).is_monotonic_decreasing\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.is_monotonic_decreasing[1]>\", line 1, in <module>\n Index([3, 2, 2]).is_monotonic_decreasing\n NameError: name 'Index' is not defined\n**********************************************************************\nLine 11, in pandas.Index.is_monotonic_decreasing\nFailed example:\n Index([3, 1, 2]).is_monotonic_decreasing\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.is_monotonic_decreasing[2]>\", line 1, in <module>\n Index([3, 1, 2]).is_monotonic_decreasing\n NameError: name 'Index' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'Index' (3 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 7, in pandas.Index.is_monotonic_decreasing\nFailed example:\n Index([3, 2, 1]).is_monotonic_decreasing\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.is_monotonic_decreasing[0]>\", line 1, in <module>\n Index([3, 2, 1]).is_monotonic_decreasing\n NameError: name 'Index' is not defined\n**********************************************************************\nLine 9, in pandas.Index.is_monotonic_decreasing\nFailed example:\n Index([3, 2, 2]).is_monotonic_decreasing\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.is_monotonic_decreasing[1]>\", line 1, in <module>\n Index([3, 2, 2]).is_monotonic_decreasing\n NameError: name 'Index' is not defined\n**********************************************************************\nLine 11, in pandas.Index.is_monotonic_decreasing\nFailed example:\n Index([3, 1, 2]).is_monotonic_decreasing\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.is_monotonic_decreasing[2]>\", line 1, in <module>\n Index([3, 1, 2]).is_monotonic_decreasing\n NameError: name 'Index' is not defined\n", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.is_unique": {"type": "CachedProperty", "docstring": "Return if the index has unique values.", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": "pandas.api.types.is_scalar"}, "pandas.Index.has_duplicates": {"type": "property", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1664, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1664", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.hasnans": {"type": "CachedProperty", "docstring": "Return if I have any nans; enables various perf speedups.", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": "pandas.Index.is_unique"}, "pandas.Index.dtype": {"type": "CachedProperty", "docstring": "Return the dtype object of the underlying data.", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": "pandas.Index.hasnans"}, "pandas.Index.dtype_str": {"type": "CachedProperty", "docstring": "Return the dtype str of the underlying data.", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": "pandas.Index.dtype"}, "pandas.Index.inferred_type": {"type": "CachedProperty", "docstring": "Return a string of the type inferred from the values.", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": "pandas.Index.dtype_str"}, "pandas.Index.is_all_dates": {"type": "CachedProperty", "docstring": "", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": "pandas.Index.inferred_type"}, "pandas.Index.shape": {"type": "property", "docstring": "Return a tuple of the shape of the underlying data.", "deprecated": false, "file": "pandas/core/base.py", "file_line": 697, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L697", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.name": {"type": "NoneType", "docstring": "", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": "pandas.Index.is_all_dates"}, "pandas.Index.names": {"type": "property", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1236, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1236", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.nbytes": {"type": "property", "docstring": "Return the number of bytes in the underlying data.", "deprecated": false, "file": "pandas/core/base.py", "file_line": 742, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L742", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.ndim": {"type": "property", "docstring": "Number of dimensions of the underlying data, by definition 1.", "deprecated": false, "file": "pandas/core/base.py", "file_line": 704, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L704", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.size": {"type": "property", "docstring": "Return the number of elements in the underlying data.", "deprecated": false, "file": "pandas/core/base.py", "file_line": 759, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L759", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.empty": {"type": "property", "docstring": "", "deprecated": false, "file": "pandas/core/base.py", "file_line": 978, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L978", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.strides": {"type": "property", "docstring": "Return the strides of the underlying data.", "deprecated": false, "file": "pandas/core/base.py", "file_line": 749, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L749", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.itemsize": {"type": "property", "docstring": "Return the size of the dtype of the item of the underlying data.", "deprecated": false, "file": "pandas/core/base.py", "file_line": 732, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L732", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.base": {"type": "property", "docstring": "Return the base object if the memory of the underlying data is shared.", "deprecated": false, "file": "pandas/core/base.py", "file_line": 776, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L776", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.T": {"type": "property", "docstring": "Return the transpose, which is by definition self.", "deprecated": false, "file": "pandas/core/base.py", "file_line": 671, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L671", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.memory_usage": {"type": "function", "docstring": "Memory usage of the values\n\nParameters\n----------\ndeep : bool\n Introspect the data deeply, interrogate\n `object` dtypes for system-level memory consumption\n\nReturns\n-------\nbytes used\n\nSee Also\n--------\nnumpy.ndarray.nbytes\n\nNotes\n-----\nMemory usage does not include memory consumed by elements that\nare not components of the array if deep=False or if used on PyPy", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 3706, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L3706", "errors": [["SS03", "Summary does not end with a period"], ["PR09", "Parameter \"deep\" description should finish with \".\""], ["RT03", "Return value has no description"], ["SA04", "Missing description for See Also \"numpy.ndarray.nbytes\" reference"]], "warnings": [["ES01", "No extended summary found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Properties", "shared_code_with": ""}, "pandas.Index.all": {"type": "function", "docstring": "Return whether all elements are True.\n\nParameters\n----------\n*args\n These parameters will be passed to numpy.all.\n**kwargs\n These parameters will be passed to numpy.all.\n\nReturns\n-------\nall : bool or array_like (if axis is specified)\n A single element array_like may be converted to bool.\n\nSee Also\n--------\npandas.Index.any : Return whether any element in an Index is True.\npandas.Series.any : Return whether any element in a Series is True.\npandas.Series.all : Return whether all elements in a Series are True.\n\nNotes\n-----\nNot a Number (NaN), positive infinity and negative infinity\nevaluate to True because these are not equal to zero.\n\nExamples\n--------\n**all**\n\nTrue, because nonzero integers are considered True.\n\n>>> pd.Index([1, 2, 3]).all()\nTrue\n\nFalse, because ``0`` is considered False.\n\n>>> pd.Index([0, 1, 2]).all()\nFalse\n\n**any**\n\nTrue, because ``1`` is considered True.\n\n>>> pd.Index([0, 0, 1]).any()\nTrue\n\nFalse, because ``0`` is considered False.\n\n>>> pd.Index([0, 0, 0]).any()\nFalse", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 5240, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L5240", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["SA05", "pandas.Index.any in `See Also` section does not need `pandas` prefix, use Index.any instead."], ["SA05", "pandas.Series.any in `See Also` section does not need `pandas` prefix, use Series.any instead."], ["SA05", "pandas.Series.all in `See Also` section does not need `pandas` prefix, use Series.all instead."]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.any": {"type": "function", "docstring": "Return whether any element is True.\n\nParameters\n----------\n*args\n These parameters will be passed to numpy.any.\n**kwargs\n These parameters will be passed to numpy.any.\n\nReturns\n-------\nany : bool or array_like (if axis is specified)\n A single element array_like may be converted to bool.\n\nSee Also\n--------\npandas.Index.all : Return whether all elements are True.\npandas.Series.all : Return whether all elements are True.\n\nNotes\n-----\nNot a Number (NaN), positive infinity and negative infinity\nevaluate to True because these are not equal to zero.\n\nExamples\n--------\n>>> index = pd.Index([0, 1, 2])\n>>> index.any()\nTrue\n\n>>> index = pd.Index([0, 0, 0])\n>>> index.any()\nFalse", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 5240, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L5240", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["SA05", "pandas.Index.all in `See Also` section does not need `pandas` prefix, use Index.all instead."], ["SA05", "pandas.Series.all in `See Also` section does not need `pandas` prefix, use Series.all instead."]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": "pandas.Index.all"}, "pandas.Index.argmin": {"type": "function", "docstring": "Return a ndarray of the minimum argument indexer.\n\nParameters\n----------\naxis : {None}\n Dummy argument for consistency with Series\nskipna : bool, default True\n\nSee Also\n--------\nnumpy.ndarray.argmin", "deprecated": false, "file": "pandas/core/base.py", "file_line": 1079, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L1079", "errors": [["PR09", "Parameter \"axis\" description should finish with \".\""], ["PR07", "Parameter \"skipna\" has no description"], ["RT01", "No Returns section found"], ["SA04", "Missing description for See Also \"numpy.ndarray.argmin\" reference"]], "warnings": [["ES01", "No extended summary found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.argmax": {"type": "function", "docstring": "Return a ndarray of the maximum argument indexer.\n\nParameters\n----------\naxis : {None}\n Dummy argument for consistency with Series\nskipna : bool, default True\n\nSee Also\n--------\nnumpy.ndarray.argmax", "deprecated": false, "file": "pandas/core/base.py", "file_line": 1022, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L1022", "errors": [["PR09", "Parameter \"axis\" description should finish with \".\""], ["PR07", "Parameter \"skipna\" has no description"], ["RT01", "No Returns section found"], ["SA04", "Missing description for See Also \"numpy.ndarray.argmax\" reference"]], "warnings": [["ES01", "No extended summary found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.copy": {"type": "function", "docstring": "Make a copy of this object. Name and dtype sets those attributes on\nthe new object.\n\nParameters\n----------\nname : string, optional\ndeep : boolean, default False\ndtype : numpy dtype or pandas type\n\nReturns\n-------\ncopy : Index\n\nNotes\n-----\nIn most cases, there should be no functional difference from using\n``deep``, but if ``deep`` is passed it will attempt to deepcopy.", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 887, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L887", "errors": [["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {**kwargs} not documented"], ["PR06", "Parameter \"name\" type should use \"str\" instead of \"string\""], ["PR07", "Parameter \"name\" has no description"], ["PR06", "Parameter \"deep\" type should use \"bool\" instead of \"boolean\""], ["PR07", "Parameter \"deep\" has no description"], ["PR07", "Parameter \"dtype\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.delete": {"type": "function", "docstring": "Make new Index with passed location(-s) deleted.\n\nReturns\n-------\nnew_index : Index", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4908, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4908", "errors": [["PR01", "Parameters {loc} not documented"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.drop": {"type": "function", "docstring": "Make new Index with passed list of labels deleted.\n\nParameters\n----------\nlabels : array-like\nerrors : {'ignore', 'raise'}, default 'raise'\n If 'ignore', suppress error and existing labels are dropped.\n\nReturns\n-------\ndropped : Index\n\nRaises\n------\nKeyError\n If not all of the labels are found in the selected axis", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4938, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4938", "errors": [["PR07", "Parameter \"labels\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.drop_duplicates": {"type": "function", "docstring": "Return Index with duplicate values removed.\n\nParameters\n----------\nkeep : {'first', 'last', ``False``}, default 'first'\n - 'first' : Drop duplicates except for the first occurrence.\n - 'last' : Drop duplicates except for the last occurrence.\n - ``False`` : Drop all duplicates.\n\nReturns\n-------\ndeduplicated : Index\n\nSee Also\n--------\nSeries.drop_duplicates : Equivalent method on Series.\nDataFrame.drop_duplicates : Equivalent method on DataFrame.\nIndex.duplicated : Related method on Index, indicating duplicate\n Index values.\n\nExamples\n--------\nGenerate an pandas.Index with duplicate values.\n\n>>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'])\n\nThe `keep` parameter controls which duplicate values are removed.\nThe value 'first' keeps the first occurrence for each\nset of duplicated entries. The default value of keep is 'first'.\n\n>>> idx.drop_duplicates(keep='first')\nIndex(['lama', 'cow', 'beetle', 'hippo'], dtype='object')\n\nThe value 'last' keeps the last occurrence for each set of duplicated\nentries.\n\n>>> idx.drop_duplicates(keep='last')\nIndex(['cow', 'beetle', 'lama', 'hippo'], dtype='object')\n\nThe value ``False`` discards all sets of duplicated entries.\n\n>>> idx.drop_duplicates(keep=False)\nIndex(['cow', 'beetle', 'hippo'], dtype='object')", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 2004, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L2004", "errors": [["PR08", "Parameter \"keep\" description should start with a capital letter"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.duplicated": {"type": "function", "docstring": "Indicate duplicate index values.\n\nDuplicated values are indicated as ``True`` values in the resulting\narray. Either all duplicates, all except the first, or all except the\nlast occurrence of duplicates can be indicated.\n\nParameters\n----------\nkeep : {'first', 'last', False}, default 'first'\n The value or values in a set of duplicates to mark as missing.\n\n - 'first' : Mark duplicates as ``True`` except for the first\n occurrence.\n - 'last' : Mark duplicates as ``True`` except for the last\n occurrence.\n - ``False`` : Mark all duplicates as ``True``.\n\nReturns\n-------\nnumpy.ndarray\n\nSee Also\n--------\npandas.Series.duplicated : Equivalent method on pandas.Series.\npandas.DataFrame.duplicated : Equivalent method on pandas.DataFrame.\npandas.Index.drop_duplicates : Remove duplicate values from Index.\n\nExamples\n--------\nBy default, for each set of duplicated values, the first occurrence is\nset to False and all others to True:\n\n>>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama'])\n>>> idx.duplicated()\narray([False, False, True, False, True])\n\nwhich is equivalent to\n\n>>> idx.duplicated(keep='first')\narray([False, False, True, False, True])\n\nBy using 'last', the last occurrence of each set of duplicated values\nis set on False and all others on True:\n\n>>> idx.duplicated(keep='last')\narray([ True, False, True, False, False])\n\nBy setting keep on ``False``, all duplicates are True:\n\n>>> idx.duplicated(keep=False)\narray([ True, False, True, False, True])", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 2052, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L2052", "errors": [["RT03", "Return value has no description"], ["SA05", "pandas.Series.duplicated in `See Also` section does not need `pandas` prefix, use Series.duplicated instead."], ["SA05", "pandas.DataFrame.duplicated in `See Also` section does not need `pandas` prefix, use DataFrame.duplicated instead."], ["SA05", "pandas.Index.drop_duplicates in `See Also` section does not need `pandas` prefix, use Index.drop_duplicates instead."]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.equals": {"type": "function", "docstring": "Determines if two Index objects contain the same elements.", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4050, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4050", "errors": [["SS05", "Summary must start with infinitive verb, not third person (e.g. use \"Generate\" instead of \"Generates\")"], ["PR01", "Parameters {other} not documented"], ["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.factorize": {"type": "function", "docstring": "Encode the object as an enumerated type or categorical variable.\n\nThis method is useful for obtaining a numeric representation of an\narray when all that matters is identifying distinct values. `factorize`\nis available as both a top-level function :func:`pandas.factorize`,\nand as a method :meth:`Series.factorize` and :meth:`Index.factorize`.\n\nParameters\n----------\nsort : boolean, default False\n Sort `uniques` and shuffle `labels` to maintain the\n relationship.\n\nna_sentinel : int, default -1\n Value to mark \"not found\".\n\nReturns\n-------\nlabels : ndarray\n An integer ndarray that's an indexer into `uniques`.\n ``uniques.take(labels)`` will have the same values as `values`.\nuniques : ndarray, Index, or Categorical\n The unique valid values. When `values` is Categorical, `uniques`\n is a Categorical. When `values` is some other pandas object, an\n `Index` is returned. Otherwise, a 1-D ndarray is returned.\n\n .. note ::\n\n Even if there's a missing value in `values`, `uniques` will\n *not* contain an entry for it.\n\nSee Also\n--------\ncut : Discretize continuous-valued array.\nunique : Find the unique value in an array.\n\nExamples\n--------\nThese examples all show factorize as a top-level method like\n``pd.factorize(values)``. The results are identical for methods like\n:meth:`Series.factorize`.\n\n>>> labels, uniques = pd.factorize(['b', 'b', 'a', 'c', 'b'])\n>>> labels\narray([0, 0, 1, 2, 0])\n>>> uniques\narray(['b', 'a', 'c'], dtype=object)\n\nWith ``sort=True``, the `uniques` will be sorted, and `labels` will be\nshuffled so that the relationship is the maintained.\n\n>>> labels, uniques = pd.factorize(['b', 'b', 'a', 'c', 'b'], sort=True)\n>>> labels\narray([1, 1, 0, 2, 1])\n>>> uniques\narray(['a', 'b', 'c'], dtype=object)\n\nMissing values are indicated in `labels` with `na_sentinel`\n(``-1`` by default). Note that missing values are never\nincluded in `uniques`.\n\n>>> labels, uniques = pd.factorize(['b', None, 'a', 'c', 'b'])\n>>> labels\narray([ 0, -1, 1, 2, 0])\n>>> uniques\narray(['b', 'a', 'c'], dtype=object)\n\nThus far, we've only factorized lists (which are internally coerced to\nNumPy arrays). When factorizing pandas objects, the type of `uniques`\nwill differ. For Categoricals, a `Categorical` is returned.\n\n>>> cat = pd.Categorical(['a', 'a', 'c'], categories=['a', 'b', 'c'])\n>>> labels, uniques = pd.factorize(cat)\n>>> labels\narray([0, 0, 1])\n>>> uniques\n[a, c]\nCategories (3, object): [a, b, c]\n\nNotice that ``'b'`` is in ``uniques.categories``, despite not being\npresent in ``cat.values``.\n\nFor all other pandas objects, an Index of the appropriate type is\nreturned.\n\n>>> cat = pd.Series(['a', 'a', 'c'])\n>>> labels, uniques = pd.factorize(cat)\n>>> labels\narray([0, 0, 1])\n>>> uniques\nIndex(['a', 'c'], dtype='object')", "deprecated": false, "file": "pandas/core/base.py", "file_line": 1413, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L1413", "errors": [["PR06", "Parameter \"sort\" type should use \"bool\" instead of \"boolean\""]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.identical": {"type": "function", "docstring": "Similar to equals, but check that other comparable attributes are\nalso equal.", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4070, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4070", "errors": [["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {other} not documented"], ["RT01", "No Returns section found"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.insert": {"type": "function", "docstring": "Make new Index inserting new item at location.\n\nFollows Python list.append semantics for negative values.\n\nParameters\n----------\nloc : int\nitem : object\n\nReturns\n-------\nnew_index : Index", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4918, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4918", "errors": [["PR07", "Parameter \"loc\" has no description"], ["PR07", "Parameter \"item\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.is_": {"type": "function", "docstring": "More flexible, faster check like ``is`` but that works through views.\n\nNote: this is *not* the same as ``Index.identical()``, which checks\nthat metadata is also the same.\n\nParameters\n----------\nother : object\n other object to compare against.\n\nReturns\n-------\nTrue if both have same underlying data, False otherwise : bool", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 613, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L613", "errors": [["PR08", "Parameter \"other\" description should start with a capital letter"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.is_boolean": {"type": "function", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1668, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1668", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.is_categorical": {"type": "function", "docstring": "Check if the Index holds categorical data.\n\nReturns\n-------\nboolean\n True if the Index is categorical.\n\nSee Also\n--------\nCategoricalIndex : Index for categorical data.\n\nExamples\n--------\n>>> idx = pd.Index([\"Watermelon\", \"Orange\", \"Apple\",\n... \"Watermelon\"]).astype(\"category\")\n>>> idx.is_categorical()\nTrue\n\n>>> idx = pd.Index([1, 3, 5, 7])\n>>> idx.is_categorical()\nFalse\n\n>>> s = pd.Series([\"Peter\", \"Victor\", \"Elisabeth\", \"Mar\"])\n>>> s\n0 Peter\n1 Victor\n2 Elisabeth\n3 Mar\ndtype: object\n>>> s.index.is_categorical()\nFalse", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1683, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1683", "errors": [], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.is_floating": {"type": "function", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1674, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1674", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.is_integer": {"type": "function", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1671, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1671", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.is_interval": {"type": "function", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1719, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1719", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.is_mixed": {"type": "function", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1722, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1722", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.is_numeric": {"type": "function", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1677, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1677", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.is_object": {"type": "function", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1680, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1680", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.min": {"type": "function", "docstring": "Return the minimum value of the Index.\n\nParameters\n----------\naxis : {None}\n Dummy argument for consistency with Series\nskipna : bool, default True\n\nReturns\n-------\nscalar\n Minimum value.\n\nSee Also\n--------\nIndex.max : Return the maximum value of the object.\nSeries.min : Return the minimum value in a Series.\nDataFrame.min : Return the minimum values in a DataFrame.\n\nExamples\n--------\n>>> idx = pd.Index([3, 2, 1])\n>>> idx.min()\n1\n\n>>> idx = pd.Index(['c', 'b', 'a'])\n>>> idx.min()\n'a'\n\nFor a MultiIndex, the minimum is determined lexicographically.\n\n>>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])\n>>> idx.min()\n('a', 1)", "deprecated": false, "file": "pandas/core/base.py", "file_line": 1039, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L1039", "errors": [["PR09", "Parameter \"axis\" description should finish with \".\""], ["PR07", "Parameter \"skipna\" has no description"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.max": {"type": "function", "docstring": "Return the maximum value of the Index.\n\nParameters\n----------\naxis : int, optional\n For compatibility with NumPy. Only 0 or None are allowed.\nskipna : bool, default True\n\nReturns\n-------\nscalar\n Maximum value.\n\nSee Also\n--------\nIndex.min : Return the minimum value in an Index.\nSeries.max : Return the maximum value in a Series.\nDataFrame.max : Return the maximum values in a DataFrame.\n\nExamples\n--------\n>>> idx = pd.Index([3, 2, 1])\n>>> idx.max()\n3\n\n>>> idx = pd.Index(['c', 'b', 'a'])\n>>> idx.max()\n'c'\n\nFor a MultiIndex, the maximum is determined lexicographically.\n\n>>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])\n>>> idx.max()\n('b', 2)", "deprecated": false, "file": "pandas/core/base.py", "file_line": 982, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L982", "errors": [["PR07", "Parameter \"skipna\" has no description"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.reindex": {"type": "function", "docstring": "Create index with target's values (move/add/delete values\nas necessary).\n\nParameters\n----------\ntarget : an iterable\n\nReturns\n-------\nnew_index : pd.Index\n Resulting index\nindexer : np.ndarray or None\n Indices of output values in original index", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 3088, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L3088", "errors": [["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"], ["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {level, tolerance, limit, method} not documented"], ["PR07", "Parameter \"target\" has no description"], ["RT05", "Return value description should finish with \".\""], ["RT05", "Return value description should finish with \".\""]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.rename": {"type": "function", "docstring": "Alter Index or MultiIndex name.\n\nAble to set new names without level. Defaults to returning new index.\nLength of names must match number of levels in MultiIndex.\n\nParameters\n----------\nname : label or list of labels\n Name(s) to set.\ninplace : boolean, default False\n Modifies the object directly, instead of creating a new Index or\n MultiIndex.\n\nReturns\n-------\nIndex\n The same type as the caller or None if inplace is True.\n\nSee Also\n--------\nIndex.set_names : Able to set new names partially and by level.\n\nExamples\n--------\n>>> idx = pd.Index(['A', 'C', 'A', 'B'], name='score')\n>>> idx.rename('grade')\nIndex(['A', 'C', 'A', 'B'], dtype='object', name='grade')\n\n>>> idx = pd.MultiIndex.from_product([['python', 'cobra'],\n... [2018, 2019]],\n... names=['kind', 'year'])\n>>> idx\nMultiIndex(levels=[['cobra', 'python'], [2018, 2019]],\n codes=[[1, 1, 0, 0], [0, 1, 0, 1]],\n names=['kind', 'year'])\n>>> idx.rename(['species', 'year'])\nMultiIndex(levels=[['cobra', 'python'], [2018, 2019]],\n codes=[[1, 1, 0, 0], [0, 1, 0, 1]],\n names=['species', 'year'])\n>>> idx.rename('species')\nTraceback (most recent call last):\nTypeError: Must pass list-like as `names`.", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1345, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1345", "errors": [["PR06", "Parameter \"inplace\" type should use \"bool\" instead of \"boolean\""], ["EX03", "flake8 error: E127 continuation line over-indented for visual indent"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.repeat": {"type": "function", "docstring": "Repeat elements of a Index.\n\nReturns a new Index where each element of the current Index\nis repeated consecutively a given number of times.\n\nParameters\n----------\nrepeats : int or array of ints\n The number of repetitions for each element. This should be a\n non-negative integer. Repeating 0 times will return an empty\n Index.\naxis : None\n Must be ``None``. Has no effect but is accepted for compatibility\n with numpy.\n\nReturns\n-------\nrepeated_index : Index\n Newly created Index with repeated elements.\n\nSee Also\n--------\nSeries.repeat : Equivalent function for Series.\nnumpy.repeat : Similar method for :class:`numpy.ndarray`.\n\nExamples\n--------\n>>> idx = pd.Index(['a', 'b', 'c'])\n>>> idx\nIndex(['a', 'b', 'c'], dtype='object')\n>>> idx.repeat(2)\nIndex(['a', 'a', 'b', 'b', 'c', 'c'], dtype='object')\n>>> idx.repeat([1, 2, 3])\nIndex(['a', 'b', 'b', 'c', 'c', 'c'], dtype='object')", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 859, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L859", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.where": {"type": "function", "docstring": "Return an Index of same shape as self and whose corresponding\nentries are from self where cond is True and otherwise are from\nother.\n\n.. versionadded:: 0.19.0\n\nParameters\n----------\ncond : boolean array-like with the same length as self\nother : scalar, or array-like", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 3727, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L3727", "errors": [["SS06", "Summary should fit in a single line"], ["PR06", "Parameter \"cond\" type should use \"bool\" instead of \"boolean\""], ["PR07", "Parameter \"cond\" has no description"], ["PR07", "Parameter \"other\" has no description"], ["RT01", "No Returns section found"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.take": {"type": "function", "docstring": "Return a new Index of the values selected by the indices.\n\nFor internal compatibility with numpy arrays.\n\nParameters\n----------\nindices : list\n Indices to be taken\naxis : int, optional\n The axis over which to select values, always 0.\nallow_fill : bool, default True\nfill_value : bool, default None\n If allow_fill=True and fill_value is not None, indices specified by\n -1 is regarded as NA. If Index doesn't hold NA, raise ValueError\n\nSee Also\n--------\nnumpy.ndarray.take", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 783, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L783", "errors": [["PR01", "Parameters {**kwargs} not documented"], ["PR09", "Parameter \"indices\" description should finish with \".\""], ["PR07", "Parameter \"allow_fill\" has no description"], ["PR09", "Parameter \"fill_value\" description should finish with \".\""], ["RT01", "No Returns section found"], ["SA04", "Missing description for See Also \"numpy.ndarray.take\" reference"]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.putmask": {"type": "function", "docstring": "Return a new Index of the values set with the mask.\n\nSee Also\n--------\nnumpy.ndarray.putmask", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4031, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4031", "errors": [["PR01", "Parameters {mask, value} not documented"], ["RT01", "No Returns section found"], ["SA04", "Missing description for See Also \"numpy.ndarray.putmask\" reference"]], "warnings": [["ES01", "No extended summary found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.unique": {"type": "function", "docstring": "Return unique values in the index. Uniques are returned in order\nof appearance, this does NOT sort.\n\nParameters\n----------\nlevel : int or str, optional, default None\n Only return values from specified level (for MultiIndex)\n\n .. versionadded:: 0.23.0\n\nReturns\n-------\nIndex without duplicates\n\nSee Also\n--------\nunique\nSeries.unique", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1997, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1997", "errors": [["SS06", "Summary should fit in a single line"], ["PR09", "Parameter \"level\" description should finish with \".\""], ["RT03", "Return value has no description"], ["SA04", "Missing description for See Also \"unique\" reference"], ["SA04", "Missing description for See Also \"Series.unique\" reference"]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.nunique": {"type": "function", "docstring": "Return number of unique elements in the object.\n\nExcludes NA values by default.\n\nParameters\n----------\ndropna : boolean, default True\n Don't include NaN in the count.\n\nReturns\n-------\nnunique : int", "deprecated": false, "file": "pandas/core/base.py", "file_line": 1318, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L1318", "errors": [["PR06", "Parameter \"dropna\" type should use \"bool\" instead of \"boolean\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.value_counts": {"type": "function", "docstring": "Return a Series containing counts of unique values.\n\nThe resulting object will be in descending order so that the\nfirst element is the most frequently-occurring element.\nExcludes NA values by default.\n\nParameters\n----------\nnormalize : boolean, default False\n If True then the object returned will contain the relative\n frequencies of the unique values.\nsort : boolean, default True\n Sort by frequencies.\nascending : boolean, default False\n Sort in ascending order.\nbins : integer, optional\n Rather than count values, group them into half-open bins,\n a convenience for ``pd.cut``, only works with numeric data.\ndropna : boolean, default True\n Don't include counts of NaN.\n\nReturns\n-------\ncounts : Series\n\nSee Also\n--------\nSeries.count: Number of non-NA elements in a Series.\nDataFrame.count: Number of non-NA elements in a DataFrame.\n\nExamples\n--------\n>>> index = pd.Index([3, 1, 2, 3, 4, np.nan])\n>>> index.value_counts()\n3.0 2\n4.0 1\n2.0 1\n1.0 1\ndtype: int64\n\nWith `normalize` set to `True`, returns the relative frequency by\ndividing all values by the sum of values.\n\n>>> s = pd.Series([3, 1, 2, 3, 4, np.nan])\n>>> s.value_counts(normalize=True)\n3.0 0.4\n4.0 0.2\n2.0 0.2\n1.0 0.2\ndtype: float64\n\n**bins**\n\nBins can be useful for going from a continuous variable to a\ncategorical variable; instead of counting unique\napparitions of values, divide the index in the specified\nnumber of half-open bins.\n\n>>> s.value_counts(bins=3)\n(2.0, 3.0] 2\n(0.996, 2.0] 2\n(3.0, 4.0] 1\ndtype: int64\n\n**dropna**\n\nWith `dropna` set to `False` we can also see NaN index values.\n\n>>> s.value_counts(dropna=False)\n3.0 2\nNaN 1\n4.0 1\n2.0 1\n1.0 1\ndtype: int64", "deprecated": false, "file": "pandas/core/base.py", "file_line": 1222, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L1222", "errors": [["PR06", "Parameter \"normalize\" type should use \"bool\" instead of \"boolean\""], ["PR06", "Parameter \"sort\" type should use \"bool\" instead of \"boolean\""], ["PR06", "Parameter \"ascending\" type should use \"bool\" instead of \"boolean\""], ["PR06", "Parameter \"bins\" type should use \"int\" instead of \"integer\""], ["PR06", "Parameter \"dropna\" type should use \"bool\" instead of \"boolean\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.Index.set_names": {"type": "function", "docstring": "Set Index or MultiIndex name.\n\nAble to set new names partially and by level.\n\nParameters\n----------\nnames : label or list of label\n Name(s) to set.\nlevel : int, label or list of int or label, optional\n If the index is a MultiIndex, level(s) to set (None for all\n levels). Otherwise level must be None.\ninplace : bool, default False\n Modifies the object directly, instead of creating a new Index or\n MultiIndex.\n\nReturns\n-------\nIndex\n The same type as the caller or None if inplace is True.\n\nSee Also\n--------\nIndex.rename : Able to set new names without level.\n\nExamples\n--------\n>>> idx = pd.Index([1, 2, 3, 4])\n>>> idx\nInt64Index([1, 2, 3, 4], dtype='int64')\n>>> idx.set_names('quarter')\nInt64Index([1, 2, 3, 4], dtype='int64', name='quarter')\n\n>>> idx = pd.MultiIndex.from_product([['python', 'cobra'],\n... [2018, 2019]])\n>>> idx\nMultiIndex(levels=[['cobra', 'python'], [2018, 2019]],\n codes=[[1, 1, 0, 0], [0, 1, 0, 1]])\n>>> idx.set_names(['kind', 'year'], inplace=True)\n>>> idx\nMultiIndex(levels=[['cobra', 'python'], [2018, 2019]],\n codes=[[1, 1, 0, 0], [0, 1, 0, 1]],\n names=['kind', 'year'])\n>>> idx.set_names('species', level=0)\nMultiIndex(levels=[['cobra', 'python'], [2018, 2019]],\n codes=[[1, 1, 0, 0], [0, 1, 0, 1]],\n names=['species', 'year'])", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1271, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1271", "errors": [], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Compatibility with MultiIndex", "shared_code_with": ""}, "pandas.Index.is_lexsorted_for_tuple": {"type": "function", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1654, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1654", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Compatibility with MultiIndex", "shared_code_with": ""}, "pandas.Index.droplevel": {"type": "function", "docstring": "Return index with requested level(s) removed.\n\nIf resulting index has only 1 level left, the result will be\nof Index type, not MultiIndex.\n\n.. versionadded:: 0.23.1 (support for non-MultiIndex)\n\nParameters\n----------\nlevel : int, str, or list-like, default 0\n If a string is given, must be the name of a level\n If list-like, elements must be names or indexes of levels.\n\nReturns\n-------\nindex : Index or MultiIndex", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1490, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1490", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Compatibility with MultiIndex", "shared_code_with": ""}, "pandas.Index.fillna": {"type": "function", "docstring": "Fill NA/NaN values with the specified value\n\nParameters\n----------\nvalue : scalar\n Scalar value to use to fill holes (e.g. 0).\n This value cannot be a list-likes.\ndowncast : dict, default is None\n a dict of item->dtype of what to downcast if possible,\n or the string 'infer' which will try to downcast to an appropriate\n equal type (e.g. float64 to int64 if possible)\n\nReturns\n-------\nfilled : Index", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1938, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1938", "errors": [["SS03", "Summary does not end with a period"], ["PR08", "Parameter \"downcast\" description should start with a capital letter"], ["PR09", "Parameter \"downcast\" description should finish with \".\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Missing Values", "shared_code_with": ""}, "pandas.Index.dropna": {"type": "function", "docstring": "Return Index without NA/NaN values\n\nParameters\n----------\nhow : {'any', 'all'}, default 'any'\n If the Index is a MultiIndex, drop the value when any or all levels\n are NaN.\n\nReturns\n-------\nvalid : Index", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1963, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1963", "errors": [["SS03", "Summary does not end with a period"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Missing Values", "shared_code_with": ""}, "pandas.Index.isna": {"type": "function", "docstring": "Detect missing values.\n\nReturn a boolean same-sized object indicating if the values are NA.\nNA values, such as ``None``, :attr:`numpy.NaN` or :attr:`pd.NaT`, get\nmapped to ``True`` values.\nEverything else get mapped to ``False`` values. Characters such as\nempty strings `''` or :attr:`numpy.inf` are not considered NA values\n(unless you set ``pandas.options.mode.use_inf_as_na = True``).\n\n.. versionadded:: 0.20.0\n\nReturns\n-------\nnumpy.ndarray\n A boolean array of whether my values are NA\n\nSee Also\n--------\npandas.Index.notna : Boolean inverse of isna.\npandas.Index.dropna : Omit entries with missing values.\npandas.isna : Top-level isna.\nSeries.isna : Detect missing values in Series object.\n\nExamples\n--------\nShow which entries in a pandas.Index are NA. The result is an\narray.\n\n>>> idx = pd.Index([5.2, 6.0, np.NaN])\n>>> idx\nFloat64Index([5.2, 6.0, nan], dtype='float64')\n>>> idx.isna()\narray([False, False, True], dtype=bool)\n\nEmpty strings are not considered NA values. None is considered an NA\nvalue.\n\n>>> idx = pd.Index(['black', '', 'red', None])\n>>> idx\nIndex(['black', '', 'red', None], dtype='object')\n>>> idx.isna()\narray([False, False, False, True], dtype=bool)\n\nFor datetimes, `NaT` (Not a Time) is considered as an NA value.\n\n>>> idx = pd.DatetimeIndex([pd.Timestamp('1940-04-25'),\n... pd.Timestamp(''), None, pd.NaT])\n>>> idx\nDatetimeIndex(['1940-04-25', 'NaT', 'NaT', 'NaT'],\n dtype='datetime64[ns]', freq=None)\n>>> idx.isna()\narray([False, True, True, True], dtype=bool)", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1815, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1815", "errors": [["RT05", "Return value description should finish with \".\""], ["SA05", "pandas.Index.notna in `See Also` section does not need `pandas` prefix, use Index.notna instead."], ["SA05", "pandas.Index.dropna in `See Also` section does not need `pandas` prefix, use Index.dropna instead."], ["SA05", "pandas.isna in `See Also` section does not need `pandas` prefix, use isna instead."], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 33, in pandas.Index.isna\nFailed example:\n idx.isna()\nExpected:\n array([False, False, True], dtype=bool)\nGot:\n array([False, False, True])\n**********************************************************************\nLine 42, in pandas.Index.isna\nFailed example:\n idx.isna()\nExpected:\n array([False, False, False, True], dtype=bool)\nGot:\n array([False, False, False, True])\n**********************************************************************\nLine 52, in pandas.Index.isna\nFailed example:\n idx.isna()\nExpected:\n array([False, True, True, True], dtype=bool)\nGot:\n array([False, True, True, True])\n"]], "warnings": [], "examples_errors": "**********************************************************************\nLine 33, in pandas.Index.isna\nFailed example:\n idx.isna()\nExpected:\n array([False, False, True], dtype=bool)\nGot:\n array([False, False, True])\n**********************************************************************\nLine 42, in pandas.Index.isna\nFailed example:\n idx.isna()\nExpected:\n array([False, False, False, True], dtype=bool)\nGot:\n array([False, False, False, True])\n**********************************************************************\nLine 52, in pandas.Index.isna\nFailed example:\n idx.isna()\nExpected:\n array([False, True, True, True], dtype=bool)\nGot:\n array([False, True, True, True])\n", "in_api": true, "section": "Index", "subsection": "Missing Values", "shared_code_with": ""}, "pandas.Index.notna": {"type": "function", "docstring": "Detect existing (non-missing) values.\n\nReturn a boolean same-sized object indicating if the values are not NA.\nNon-missing values get mapped to ``True``. Characters such as empty\nstrings ``''`` or :attr:`numpy.inf` are not considered NA values\n(unless you set ``pandas.options.mode.use_inf_as_na = True``).\nNA values, such as None or :attr:`numpy.NaN`, get mapped to ``False``\nvalues.\n\n.. versionadded:: 0.20.0\n\nReturns\n-------\nnumpy.ndarray\n Boolean array to indicate which entries are not NA.\n\nSee Also\n--------\nIndex.notnull : Alias of notna.\nIndex.isna: Inverse of notna.\npandas.notna : Top-level notna.\n\nExamples\n--------\nShow which entries in an Index are not NA. The result is an\narray.\n\n>>> idx = pd.Index([5.2, 6.0, np.NaN])\n>>> idx\nFloat64Index([5.2, 6.0, nan], dtype='float64')\n>>> idx.notna()\narray([ True, True, False])\n\nEmpty strings are not considered NA values. None is considered a NA\nvalue.\n\n>>> idx = pd.Index(['black', '', 'red', None])\n>>> idx\nIndex(['black', '', 'red', None], dtype='object')\n>>> idx.notna()\narray([ True, True, True, False])", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1873, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1873", "errors": [["SA05", "pandas.notna in `See Also` section does not need `pandas` prefix, use notna instead."]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Missing Values", "shared_code_with": ""}, "pandas.Index.astype": {"type": "function", "docstring": "Create an Index with values cast to dtypes. The class of a new Index\nis determined by dtype. When conversion is impossible, a ValueError\nexception is raised.\n\nParameters\n----------\ndtype : numpy dtype or pandas type\n Note that any signed integer `dtype` is treated as ``'int64'``,\n and any unsigned integer `dtype` is treated as ``'uint64'``,\n regardless of the size.\ncopy : bool, default True\n By default, astype always returns a newly allocated object.\n If copy is set to False and internal requirements on dtype are\n satisfied, the original data is used to create a new Index\n or the original Index is returned.\n\n .. versionadded:: 0.19.0", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 731, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L731", "errors": [["SS06", "Summary should fit in a single line"], ["RT01", "No Returns section found"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Conversion", "shared_code_with": ""}, "pandas.Index.item": {"type": "function", "docstring": "Return the first element of the underlying data as a python scalar.", "deprecated": false, "file": "pandas/core/base.py", "file_line": 711, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L711", "errors": [["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Conversion", "shared_code_with": ""}, "pandas.Index.map": {"type": "function", "docstring": "Map values using input correspondence (a dict, Series, or function).\n\nParameters\n----------\nmapper : function, dict, or Series\n Mapping correspondence.\nna_action : {None, 'ignore'}\n If 'ignore', propagate NA values, without passing them to the\n mapping correspondence.\n\nReturns\n-------\napplied : Union[Index, MultiIndex], inferred\n The output of the mapping function applied to the index.\n If the function returns a tuple with more than one element\n a MultiIndex will be returned.", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4495, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4495", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Conversion", "shared_code_with": ""}, "pandas.Index.ravel": {"type": "function", "docstring": "Return an ndarray of the flattened values of the underlying data.\n\nSee Also\n--------\nnumpy.ndarray.ravel", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 689, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L689", "errors": [["PR01", "Parameters {order} not documented"], ["RT01", "No Returns section found"], ["SA04", "Missing description for See Also \"numpy.ndarray.ravel\" reference"]], "warnings": [["ES01", "No extended summary found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Conversion", "shared_code_with": ""}, "pandas.Index.to_list": {"type": "function", "docstring": "Return a list of the values.\n\nThese are each a scalar type, which is a Python scalar\n(for str, int, float) or a pandas scalar\n(for Timestamp/Timedelta/Interval/Period)\n\nSee Also\n--------\nnumpy.ndarray.tolist", "deprecated": false, "file": "pandas/core/base.py", "file_line": 1096, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L1096", "errors": [["RT01", "No Returns section found"], ["SA04", "Missing description for See Also \"numpy.ndarray.tolist\" reference"]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Conversion", "shared_code_with": ""}, "pandas.Index.to_native_types": {"type": "function", "docstring": "Format specified values of `self` and return them.\n\nParameters\n----------\nslicer : int, array-like\n An indexer into `self` that specifies which values\n are used in the formatting process.\nkwargs : dict\n Options for specifying how the values should be formatted.\n These options include the following:\n\n 1) na_rep : str\n The value that serves as a placeholder for NULL values\n 2) quoting : bool or None\n Whether or not there are quoted values in `self`\n 3) date_format : str\n The format used to represent date-like values", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1023, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1023", "errors": [["PR01", "Parameters {**kwargs} not documented"], ["PR02", "Unknown parameters {kwargs}"], ["PR09", "Parameter \"kwargs\" description should finish with \".\""], ["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Conversion", "shared_code_with": ""}, "pandas.Index.to_series": {"type": "function", "docstring": "Create a Series with both index and values equal to the index keys\nuseful with map for returning an indexer based on an index.\n\nParameters\n----------\nindex : Index, optional\n index of resulting Series. If None, defaults to original index\nname : string, optional\n name of resulting Series. If None, defaults to name of original\n index\n\nReturns\n-------\nSeries : dtype will be based on the type of the Index values.", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1126, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1126", "errors": [["SS06", "Summary should fit in a single line"], ["PR08", "Parameter \"index\" description should start with a capital letter"], ["PR09", "Parameter \"index\" description should finish with \".\""], ["PR06", "Parameter \"name\" type should use \"str\" instead of \"string\""], ["PR08", "Parameter \"name\" description should start with a capital letter"], ["PR09", "Parameter \"name\" description should finish with \".\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Conversion", "shared_code_with": ""}, "pandas.Index.to_frame": {"type": "function", "docstring": "Create a DataFrame with a column containing the Index.\n\n.. versionadded:: 0.24.0\n\nParameters\n----------\nindex : boolean, default True\n Set the index of the returned DataFrame as the original Index.\n\nname : object, default None\n The passed name should substitute for the index name (if it has\n one).\n\nReturns\n-------\nDataFrame\n DataFrame containing the original Index data.\n\nSee Also\n--------\nIndex.to_series : Convert an Index to a Series.\nSeries.to_frame : Convert Series to DataFrame.\n\nExamples\n--------\n>>> idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal')\n>>> idx.to_frame()\n animal\nanimal\nAnt Ant\nBear Bear\nCow Cow\n\nBy default, the original Index is reused. To enforce a new Index:\n\n>>> idx.to_frame(index=False)\n animal\n0 Ant\n1 Bear\n2 Cow\n\nTo override the name of the resulting column, specify `name`:\n\n>>> idx.to_frame(index=False, name='zoo')\n zoo\n0 Ant\n1 Bear\n2 Cow", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1153, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1153", "errors": [["PR06", "Parameter \"index\" type should use \"bool\" instead of \"boolean\""]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Conversion", "shared_code_with": ""}, "pandas.Index.view": {"type": "function", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 699, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L699", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Conversion", "shared_code_with": ""}, "pandas.Index.argsort": {"type": "function", "docstring": "Return the integer indices that would sort the index.\n\nParameters\n----------\n*args\n Passed to `numpy.ndarray.argsort`.\n**kwargs\n Passed to `numpy.ndarray.argsort`.\n\nReturns\n-------\nnumpy.ndarray\n Integer indices that would sort the index if used as\n an indexer.\n\nSee Also\n--------\nnumpy.argsort : Similar method for NumPy arrays.\nIndex.sort_values : Return sorted copy of Index.\n\nExamples\n--------\n>>> idx = pd.Index(['b', 'a', 'd', 'c'])\n>>> idx\nIndex(['b', 'a', 'd', 'c'], dtype='object')\n\n>>> order = idx.argsort()\n>>> order\narray([1, 0, 3, 2])\n\n>>> idx[order]\nIndex(['a', 'b', 'c', 'd'], dtype='object')", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4301, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4301", "errors": [], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Sorting", "shared_code_with": ""}, "pandas.Index.searchsorted": {"type": "function", "docstring": "Find indices where elements should be inserted to maintain order.\n\nFind the indices into a sorted IndexOpsMixin `self` such that, if the\ncorresponding elements in `value` were inserted before the indices,\nthe order of `self` would be preserved.\n\nParameters\n----------\nvalue : array_like\n Values to insert into `self`.\nside : {'left', 'right'}, optional\n If 'left', the index of the first suitable location found is given.\n If 'right', return the last such index. If there is no suitable\n index, return either 0 or N (where N is the length of `self`).\nsorter : 1-D array_like, optional\n Optional array of integer indices that sort `self` into ascending\n order. They are typically the result of ``np.argsort``.\n\nReturns\n-------\nint or array of int\n A scalar or array of insertion points with the\n same shape as `value`.\n\n .. versionchanged :: 0.24.0\n If `value` is a scalar, an int is now always returned.\n Previously, scalar inputs returned an 1-item array for\n :class:`Series` and :class:`Categorical`.\n\nSee Also\n--------\nnumpy.searchsorted\n\nNotes\n-----\nBinary search is used to find the required insertion points.\n\nExamples\n--------\n\n>>> x = pd.Series([1, 2, 3])\n>>> x\n0 1\n1 2\n2 3\ndtype: int64\n\n>>> x.searchsorted(4)\n3\n\n>>> x.searchsorted([0, 4])\narray([0, 3])\n\n>>> x.searchsorted([1, 3], side='left')\narray([0, 2])\n\n>>> x.searchsorted([1, 3], side='right')\narray([1, 3])\n\n>>> x = pd.Categorical(['apple', 'bread', 'bread',\n 'cheese', 'milk'], ordered=True)\n[apple, bread, bread, cheese, milk]\nCategories (4, object): [apple < bread < cheese < milk]\n\n>>> x.searchsorted('bread')\n1\n\n>>> x.searchsorted(['bread'], side='right')\narray([3])", "deprecated": false, "file": "pandas/core/base.py", "file_line": 1497, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/base.py#L1497", "errors": [["GL04", "Private classes (IndexOpsMixin) should not be mentioned in public docstrings"], ["SA04", "Missing description for See Also \"numpy.searchsorted\" reference"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 61, in pandas.Index.searchsorted\nFailed example:\n x = pd.Categorical(['apple', 'bread', 'bread',\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.searchsorted[6]>\", line 1\n x = pd.Categorical(['apple', 'bread', 'bread',\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 66, in pandas.Index.searchsorted\nFailed example:\n x.searchsorted('bread')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.searchsorted[7]>\", line 1, in <module>\n x.searchsorted('bread')\n File \"/home/stijn_vanhoey/githubs/forks/pandas/pandas/core/series.py\", line 2337, in searchsorted\n side=side, sorter=sorter)\n TypeError: '<' not supported between instances of 'int' and 'str'\n**********************************************************************\nLine 69, in pandas.Index.searchsorted\nFailed example:\n x.searchsorted(['bread'], side='right')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.searchsorted[8]>\", line 1, in <module>\n x.searchsorted(['bread'], side='right')\n File \"/home/stijn_vanhoey/githubs/forks/pandas/pandas/core/series.py\", line 2337, in searchsorted\n side=side, sorter=sorter)\n TypeError: '<' not supported between instances of 'int' and 'str'\n"], ["EX03", "flake8 error: E902 TokenError: EOF in multi-line statement"], ["EX03", "flake8 error: E999 SyntaxError: invalid syntax"]], "warnings": [], "examples_errors": "**********************************************************************\nLine 61, in pandas.Index.searchsorted\nFailed example:\n x = pd.Categorical(['apple', 'bread', 'bread',\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.searchsorted[6]>\", line 1\n x = pd.Categorical(['apple', 'bread', 'bread',\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 66, in pandas.Index.searchsorted\nFailed example:\n x.searchsorted('bread')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.searchsorted[7]>\", line 1, in <module>\n x.searchsorted('bread')\n File \"/home/stijn_vanhoey/githubs/forks/pandas/pandas/core/series.py\", line 2337, in searchsorted\n side=side, sorter=sorter)\n TypeError: '<' not supported between instances of 'int' and 'str'\n**********************************************************************\nLine 69, in pandas.Index.searchsorted\nFailed example:\n x.searchsorted(['bread'], side='right')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.Index.searchsorted[8]>\", line 1, in <module>\n x.searchsorted(['bread'], side='right')\n File \"/home/stijn_vanhoey/githubs/forks/pandas/pandas/core/series.py\", line 2337, in searchsorted\n side=side, sorter=sorter)\n TypeError: '<' not supported between instances of 'int' and 'str'\n", "in_api": true, "section": "Index", "subsection": "Sorting", "shared_code_with": ""}, "pandas.Index.sort_values": {"type": "function", "docstring": "Return a sorted copy of the index.\n\nReturn a sorted copy of the index, and optionally return the indices\nthat sorted the index itself.\n\nParameters\n----------\nreturn_indexer : bool, default False\n Should the indices that would sort the index be returned.\nascending : bool, default True\n Should the index values be sorted in an ascending order.\n\nReturns\n-------\nsorted_index : pandas.Index\n Sorted copy of the index.\nindexer : numpy.ndarray, optional\n The indices that the index itself was sorted by.\n\nSee Also\n--------\npandas.Series.sort_values : Sort values of a Series.\npandas.DataFrame.sort_values : Sort values in a DataFrame.\n\nExamples\n--------\n>>> idx = pd.Index([10, 100, 1, 1000])\n>>> idx\nInt64Index([10, 100, 1, 1000], dtype='int64')\n\nSort values in ascending order (default behavior).\n\n>>> idx.sort_values()\nInt64Index([1, 10, 100, 1000], dtype='int64')\n\nSort values in descending order, and also get the indices `idx` was\nsorted by.\n\n>>> idx.sort_values(ascending=False, return_indexer=True)\n(Int64Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2]))", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4184, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4184", "errors": [["SA05", "pandas.Series.sort_values in `See Also` section does not need `pandas` prefix, use Series.sort_values instead."], ["SA05", "pandas.DataFrame.sort_values in `See Also` section does not need `pandas` prefix, use DataFrame.sort_values instead."]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Sorting", "shared_code_with": ""}, "pandas.Index.shift": {"type": "function", "docstring": "Shift index by desired number of time frequency increments.\n\nThis method is for shifting the values of datetime-like indexes\nby a specified time increment a given number of times.\n\nParameters\n----------\nperiods : int, default 1\n Number of periods (or increments) to shift by,\n can be positive or negative.\nfreq : pandas.DateOffset, pandas.Timedelta or string, optional\n Frequency increment to shift by.\n If None, the index is shifted by its own `freq` attribute.\n Offset aliases are valid strings, e.g., 'D', 'W', 'M' etc.\n\nReturns\n-------\npandas.Index\n shifted index\n\nSee Also\n--------\nSeries.shift : Shift values of Series.\n\nNotes\n-----\nThis method is only implemented for datetime-like index classes,\ni.e., DatetimeIndex, PeriodIndex and TimedeltaIndex.\n\nExamples\n--------\nPut the first 5 month starts of 2011 into an index.\n\n>>> month_starts = pd.date_range('1/1/2011', periods=5, freq='MS')\n>>> month_starts\nDatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01', '2011-04-01',\n '2011-05-01'],\n dtype='datetime64[ns]', freq='MS')\n\nShift the index by 10 days.\n\n>>> month_starts.shift(10, freq='D')\nDatetimeIndex(['2011-01-11', '2011-02-11', '2011-03-11', '2011-04-11',\n '2011-05-11'],\n dtype='datetime64[ns]', freq=None)\n\nThe default value of `freq` is the `freq` attribute of the index,\nwhich is 'MS' (month start) in this example.\n\n>>> month_starts.shift(10)\nDatetimeIndex(['2011-11-01', '2011-12-01', '2012-01-01', '2012-02-01',\n '2012-03-01'],\n dtype='datetime64[ns]', freq='MS')", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4242, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4242", "errors": [["PR06", "Parameter \"freq\" type should use \"str\" instead of \"string\""], ["RT04", "Return value description should start with a capital letter"], ["RT05", "Return value description should finish with \".\""]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Time-specific operations", "shared_code_with": ""}, "pandas.Index.append": {"type": "function", "docstring": "Append a collection of Index options together.\n\nParameters\n----------\nother : Index or list/tuple of indices\n\nReturns\n-------\nappended : Index", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 3987, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L3987", "errors": [["PR07", "Parameter \"other\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Combining / joining / set operations", "shared_code_with": ""}, "pandas.Index.join": {"type": "function", "docstring": "Compute join_index and indexers to conform data\nstructures to the new index.\n\nParameters\n----------\nother : Index\nhow : {'left', 'right', 'inner', 'outer'}\nlevel : int or level name, default None\nreturn_indexers : boolean, default False\nsort : boolean, default False\n Sort the join keys lexicographically in the result Index. If False,\n the order of the join keys depends on the join type (how keyword)\n\n .. versionadded:: 0.20.0\n\nReturns\n-------\njoin_index, (left_indexer, right_indexer)", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 3230, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L3230", "errors": [["SS06", "Summary should fit in a single line"], ["PR07", "Parameter \"other\" has no description"], ["PR07", "Parameter \"how\" has no description"], ["PR07", "Parameter \"level\" has no description"], ["PR06", "Parameter \"return_indexers\" type should use \"bool\" instead of \"boolean\""], ["PR07", "Parameter \"return_indexers\" has no description"], ["PR06", "Parameter \"sort\" type should use \"bool\" instead of \"boolean\""], ["PR09", "Parameter \"sort\" description should finish with \".\""], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Combining / joining / set operations", "shared_code_with": ""}, "pandas.Index.intersection": {"type": "function", "docstring": "Form the intersection of two Index objects.\n\nThis returns a new Index with elements common to the index and `other`.\n\nParameters\n----------\nother : Index or array-like\nsort : False or None, default False\n Whether to sort the resulting index.\n\n * False : do not sort the result.\n * None : sort the result, except when `self` and `other` are equal\n or when the values cannot be compared.\n\n .. versionadded:: 0.24.0\n\n .. versionchanged:: 0.24.1\n\n Changed the default from ``True`` to ``False``, to match\n the behaviour of 0.23.4 and earlier.\n\nReturns\n-------\nintersection : Index\n\nExamples\n--------\n\n>>> idx1 = pd.Index([1, 2, 3, 4])\n>>> idx2 = pd.Index([3, 4, 5, 6])\n>>> idx1.intersection(idx2)\nInt64Index([3, 4], dtype='int64')", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 2356, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L2356", "errors": [["PR07", "Parameter \"other\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Combining / joining / set operations", "shared_code_with": ""}, "pandas.Index.union": {"type": "function", "docstring": "Form the union of two Index objects.\n\nParameters\n----------\nother : Index or array-like\nsort : bool or None, default None\n Whether to sort the resulting Index.\n\n * None : Sort the result, except when\n\n 1. `self` and `other` are equal.\n 2. `self` or `other` has length 0.\n 3. Some values in `self` or `other` cannot be compared.\n A RuntimeWarning is issued in this case.\n\n * False : do not sort the result.\n\n .. versionadded:: 0.24.0\n\n .. versionchanged:: 0.24.1\n\n Changed the default value from ``True`` to ``None``\n (without change in behaviour).\n\nReturns\n-------\nunion : Index\n\nExamples\n--------\n\n>>> idx1 = pd.Index([1, 2, 3, 4])\n>>> idx2 = pd.Index([3, 4, 5, 6])\n>>> idx1.union(idx2)\nInt64Index([1, 2, 3, 4, 5, 6], dtype='int64')", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 2253, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L2253", "errors": [["PR07", "Parameter \"other\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Combining / joining / set operations", "shared_code_with": ""}, "pandas.Index.difference": {"type": "function", "docstring": "Return a new Index with elements from the index that are not in\n`other`.\n\nThis is the set difference of two Index objects.\n\nParameters\n----------\nother : Index or array-like\nsort : False or None, default None\n Whether to sort the resulting index. By default, the\n values are attempted to be sorted, but any TypeError from\n incomparable elements is caught by pandas.\n\n * None : Attempt to sort the result, but catch any TypeErrors\n from comparing incomparable elements.\n * False : Do not sort the result.\n\n .. versionadded:: 0.24.0\n\n .. versionchanged:: 0.24.1\n\n Changed the default value from ``True`` to ``None``\n (without change in behaviour).\n\nReturns\n-------\ndifference : Index\n\nExamples\n--------\n\n>>> idx1 = pd.Index([2, 1, 3, 4])\n>>> idx2 = pd.Index([3, 4, 5, 6])\n>>> idx1.difference(idx2)\nInt64Index([1, 2], dtype='int64')\n>>> idx1.difference(idx2, sort=False)\nInt64Index([2, 1], dtype='int64')", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 2444, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L2444", "errors": [["SS06", "Summary should fit in a single line"], ["PR07", "Parameter \"other\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Combining / joining / set operations", "shared_code_with": ""}, "pandas.Index.symmetric_difference": {"type": "function", "docstring": "Compute the symmetric difference of two Index objects.\n\nParameters\n----------\nother : Index or array-like\nresult_name : str\nsort : False or None, default None\n Whether to sort the resulting index. By default, the\n values are attempted to be sorted, but any TypeError from\n incomparable elements is caught by pandas.\n\n * None : Attempt to sort the result, but catch any TypeErrors\n from comparing incomparable elements.\n * False : Do not sort the result.\n\n .. versionadded:: 0.24.0\n\n .. versionchanged:: 0.24.1\n\n Changed the default value from ``True`` to ``None``\n (without change in behaviour).\n\nReturns\n-------\nsymmetric_difference : Index\n\nNotes\n-----\n``symmetric_difference`` contains elements that appear in either\n``idx1`` or ``idx2`` but not both. Equivalent to the Index created by\n``idx1.difference(idx2) | idx2.difference(idx1)`` with duplicates\ndropped.\n\nExamples\n--------\n>>> idx1 = pd.Index([1, 2, 3, 4])\n>>> idx2 = pd.Index([2, 3, 4, 5])\n>>> idx1.symmetric_difference(idx2)\nInt64Index([1, 5], dtype='int64')\n\nYou can also use the ``^`` operator:\n\n>>> idx1 ^ idx2\nInt64Index([1, 5], dtype='int64')", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 2509, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L2509", "errors": [["PR07", "Parameter \"other\" has no description"], ["PR07", "Parameter \"result_name\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Combining / joining / set operations", "shared_code_with": ""}, "pandas.Index.asof": {"type": "function", "docstring": "Return the label from the index, or, if not present, the previous one.\n\nAssuming that the index is sorted, return the passed index label if it\nis in the index, or return the previous index label if the passed one\nis not in the index.\n\nParameters\n----------\nlabel : object\n The label up to which the method returns the latest index label.\n\nReturns\n-------\nobject\n The passed label if it is in the index. The previous label if the\n passed label is not in the sorted index or `NaN` if there is no\n such label.\n\nSee Also\n--------\nSeries.asof : Return the latest value in a Series up to the\n passed index.\nmerge_asof : Perform an asof merge (similar to left join but it\n matches on nearest key rather than equal key).\nIndex.get_loc : An `asof` is a thin wrapper around `get_loc`\n with method='pad'.\n\nExamples\n--------\n`Index.asof` returns the latest index label up to the passed label.\n\n>>> idx = pd.Index(['2013-12-31', '2014-01-02', '2014-01-03'])\n>>> idx.asof('2014-01-01')\n'2013-12-31'\n\nIf the label is in the index, the method returns the passed label.\n\n>>> idx.asof('2014-01-02')\n'2014-01-02'\n\nIf all of the labels in the index are later than the passed label,\nNaN is returned.\n\n>>> idx.asof('1999-01-02')\nnan\n\nIf the index is not sorted, an error is raised.\n\n>>> idx_not_sorted = pd.Index(['2013-12-31', '2015-01-02',\n... '2014-01-03'])\n>>> idx_not_sorted.asof('2013-12-31')\nTraceback (most recent call last):\nValueError: index must be monotonic increasing or decreasing", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4080, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4080", "errors": [], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.asof_locs": {"type": "function", "docstring": "Finds the locations (indices) of the labels from the index for\nevery entry in the `where` argument.\n\nAs in the `asof` function, if the label (a particular entry in\n`where`) is not in the index, the latest index label upto the\npassed label is chosen and its index returned.\n\nIf all of the labels in the index are later than a label in `where`,\n-1 is returned.\n\n`mask` is used to ignore NA values in the index during calculation.\n\nParameters\n----------\nwhere : Index\n An Index consisting of an array of timestamps.\nmask : array-like\n Array of booleans denoting where values in the original\n data are not NA.\n\nReturns\n-------\nnumpy.ndarray\n An array of locations (indices) of the labels from the Index\n which correspond to the return values of the `asof` function\n for every element in `where`.", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4145, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4145", "errors": [["SS05", "Summary must start with infinitive verb, not third person (e.g. use \"Generate\" instead of \"Generates\")"], ["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.contains": {"type": "function", "docstring": "Return a boolean indicating whether the provided key is in the index.\n\nParameters\n----------\nkey : label\n The key to check if it is present in the index.\n\nReturns\n-------\nbool\n Whether the key search is in the index.\n\nSee Also\n--------\nIndex.isin : Returns an ndarray of boolean dtype indicating whether the\n list-like key is in the index.\n\nExamples\n--------\n>>> idx = pd.Index([1, 2, 3, 4])\n>>> idx\nInt64Index([1, 2, 3, 4], dtype='int64')\n\n>>> idx.contains(2)\nTrue\n>>> idx.contains(6)\nFalse\n\nThis is equivalent to:\n\n>>> 2 in idx\nTrue\n>>> 6 in idx\nFalse", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 3925, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L3925", "errors": [], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.get_duplicates": {"type": "function", "docstring": "Extract duplicated index elements.\n\n.. deprecated:: 0.23.0\n Use idx[idx.duplicated()].unique() instead\n\nReturns a sorted list of index elements which appear more than once in\nthe index.\n\nReturns\n-------\narray-like\n List of duplicated indexes.\n\nSee Also\n--------\nIndex.duplicated : Return boolean array denoting duplicates.\nIndex.drop_duplicates : Return Index with duplicates removed.\n\nExamples\n--------\n\nWorks on different Index of types.\n\n>>> pd.Index([1, 2, 2, 3, 3, 3, 4]).get_duplicates() # doctest: +SKIP\n[2, 3]\n\nNote that for a DatetimeIndex, it does not return a list but a new\nDatetimeIndex:\n\n>>> dates = pd.to_datetime(['2018-01-01', '2018-01-02', '2018-01-03',\n... '2018-01-03', '2018-01-04', '2018-01-04'],\n... format='%Y-%m-%d')\n>>> pd.Index(dates).get_duplicates() # doctest: +SKIP\nDatetimeIndex(['2018-01-03', '2018-01-04'],\n dtype='datetime64[ns]', freq=None)\n\nSorts duplicated elements even when indexes are unordered.\n\n>>> pd.Index([1, 2, 3, 2, 3, 4, 3]).get_duplicates() # doctest: +SKIP\n[2, 3]\n\nReturn empty array-like structure when all elements are unique.\n\n>>> pd.Index([1, 2, 3, 4]).get_duplicates() # doctest: +SKIP\n[]\n>>> dates = pd.to_datetime(['2018-01-01', '2018-01-02', '2018-01-03'],\n... format='%Y-%m-%d')\n>>> pd.Index(dates).get_duplicates() # doctest: +SKIP\nDatetimeIndex([], dtype='datetime64[ns]', freq=None)", "deprecated": true, "file": "pandas/core/indexes/base.py", "file_line": 2108, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L2108", "errors": [], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.get_indexer": {"type": "function", "docstring": "Compute indexer and mask for new index given the current index. The\nindexer should be then used as an input to ndarray.take to align the\ncurrent data to the new index.\n\nParameters\n----------\ntarget : Index\nmethod : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional\n * default: exact matches only.\n * pad / ffill: find the PREVIOUS index value if no exact match.\n * backfill / bfill: use NEXT index value if no exact match\n * nearest: use the NEAREST index value if no exact match. Tied\n distances are broken by preferring the larger index value.\nlimit : int, optional\n Maximum number of consecutive labels in ``target`` to match for\n inexact matches.\ntolerance : optional\n Maximum distance between original and new labels for inexact\n matches. The values of the index at the matching locations most\n satisfy the equation ``abs(index[indexer] - target) <= tolerance``.\n\n Tolerance may be a scalar value, which applies the same tolerance\n to all values, or list-like, which applies variable tolerance per\n element. List-like includes list, tuple, array, Series, and must be\n the same size as the index and its dtype must exactly match the\n index's type.\n\n .. versionadded:: 0.21.0 (list-like tolerance)\n\nReturns\n-------\nindexer : ndarray of int\n Integers from 0 to n - 1 indicating that the index at these\n positions matches the corresponding target values. Missing values\n in the target are marked by -1.\n\nExamples\n--------\n>>> index = pd.Index(['c', 'a', 'b'])\n>>> index.get_indexer(['a', 'b', 'x'])\narray([ 1, 2, -1])\n\nNotice that the return value is an array of locations in ``index``\nand ``x`` is marked by -1, as it is not in ``index``.", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 2714, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L2714", "errors": [["SS06", "Summary should fit in a single line"], ["PR07", "Parameter \"target\" has no description"], ["PR08", "Parameter \"method\" description should start with a capital letter"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.get_indexer_for": {"type": "function", "docstring": "Guaranteed return of an indexer even when non-unique.\n\nThis dispatches to get_indexer or get_indexer_nonunique\nas appropriate.", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4446, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4446", "errors": [["PR01", "Parameters {target, **kwargs} not documented"], ["RT01", "No Returns section found"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.get_indexer_non_unique": {"type": "function", "docstring": "Compute indexer and mask for new index given the current index. The\nindexer should be then used as an input to ndarray.take to align the\ncurrent data to the new index.\n\nParameters\n----------\ntarget : Index\n\nReturns\n-------\nindexer : ndarray of int\n Integers from 0 to n - 1 indicating that the index at these\n positions matches the corresponding target values. Missing values\n in the target are marked by -1.\nmissing : ndarray of int\n An indexer into the target of the values not found.\n These correspond to the -1 in the indexer array", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4428, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4428", "errors": [["SS06", "Summary should fit in a single line"], ["PR07", "Parameter \"target\" has no description"], ["RT05", "Return value description should finish with \".\""]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.get_level_values": {"type": "function", "docstring": "Return an Index of values for requested level.\n\nThis is primarily useful to get an individual level of values from a\nMultiIndex, but is provided on Index as well for compatability.\n\nParameters\n----------\nlevel : int or str\n It is either the integer position or the name of the level.\n\nReturns\n-------\nvalues : Index\n Calling object, as there is only one level in the Index.\n\nSee Also\n--------\nMultiIndex.get_level_values : Get values for a level of a MultiIndex.\n\nNotes\n-----\nFor Index, level should be 0, since there are no multiple levels.\n\nExamples\n--------\n\n>>> idx = pd.Index(list('abc'))\n>>> idx\nIndex(['a', 'b', 'c'], dtype='object')\n\nGet level values by supplying `level` as integer:\n\n>>> idx.get_level_values(0)\nIndex(['a', 'b', 'c'], dtype='object')", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1448, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1448", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.get_loc": {"type": "function", "docstring": "Get integer location, slice or boolean mask for requested label.\n\nParameters\n----------\nkey : label\nmethod : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional\n * default: exact matches only.\n * pad / ffill: find the PREVIOUS index value if no exact match.\n * backfill / bfill: use NEXT index value if no exact match\n * nearest: use the NEAREST index value if no exact match. Tied\n distances are broken by preferring the larger index value.\ntolerance : optional\n Maximum distance from index value for inexact matches. The value of\n the index at the matching location most satisfy the equation\n ``abs(index[loc] - key) <= tolerance``.\n\n Tolerance may be a scalar\n value, which applies the same tolerance to all values, or\n list-like, which applies variable tolerance per element. List-like\n includes list, tuple, array, Series, and must be the same size as\n the index and its dtype must exactly match the index's type.\n\n .. versionadded:: 0.21.0 (list-like tolerance)\n\nReturns\n-------\nloc : int if unique index, slice if monotonic index, else mask\n\nExamples\n---------\n>>> unique_index = pd.Index(list('abc'))\n>>> unique_index.get_loc('b')\n1\n\n>>> monotonic_index = pd.Index(list('abbc'))\n>>> monotonic_index.get_loc('b')\nslice(1, 3, None)\n\n>>> non_monotonic_index = pd.Index(list('abcb'))\n>>> non_monotonic_index.get_loc('b')\narray([False, True, False, True], dtype=bool)", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 2649, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L2649", "errors": [["PR07", "Parameter \"key\" has no description"], ["PR08", "Parameter \"method\" description should start with a capital letter"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 41, in pandas.Index.get_loc\nFailed example:\n non_monotonic_index.get_loc('b')\nExpected:\n array([False, True, False, True], dtype=bool)\nGot:\n array([False, True, False, True])\n"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 41, in pandas.Index.get_loc\nFailed example:\n non_monotonic_index.get_loc('b')\nExpected:\n array([False, True, False, True], dtype=bool)\nGot:\n array([False, True, False, True])\n", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.get_slice_bound": {"type": "function", "docstring": "Calculate slice bound that corresponds to given label.\n\nReturns leftmost (one-past-the-rightmost if ``side=='right'``) position\nof given label.\n\nParameters\n----------\nlabel : object\nside : {'left', 'right'}\nkind : {'ix', 'loc', 'getitem'}", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4773, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4773", "errors": [["PR07", "Parameter \"label\" has no description"], ["PR07", "Parameter \"side\" has no description"], ["PR07", "Parameter \"kind\" has no description"], ["RT01", "No Returns section found"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.get_value": {"type": "function", "docstring": "Fast lookup of value from 1-dimensional ndarray. Only use this if you\nknow what you're doing.", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4341, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4341", "errors": [["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {key, series} not documented"], ["RT01", "No Returns section found"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.get_values": {"type": "function", "docstring": "Return `Index` data as an `numpy.ndarray`.\n\nReturns\n-------\nnumpy.ndarray\n A one-dimensional numpy array of the `Index` values.\n\nSee Also\n--------\nIndex.values : The attribute that get_values wraps.\n\nExamples\n--------\nGetting the `Index` values of a `DataFrame`:\n\n>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]],\n... index=['a', 'b', 'c'], columns=['A', 'B', 'C'])\n>>> df\n A B C\na 1 2 3\nb 4 5 6\nc 7 8 9\n>>> df.index.get_values()\narray(['a', 'b', 'c'], dtype=object)\n\nStandalone `Index` values:\n\n>>> idx = pd.Index(['1', '2', '3'])\n>>> idx.get_values()\narray(['1', '2', '3'], dtype=object)\n\n`MultiIndex` arrays also have only one dimension:\n\n>>> midx = pd.MultiIndex.from_arrays([[1, 2, 3], ['a', 'b', 'c']],\n... names=('number', 'letter'))\n>>> midx.get_values()\narray([(1, 'a'), (2, 'b'), (3, 'c')], dtype=object)\n>>> midx.get_values().ndim\n1", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 3662, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L3662", "errors": [["EX03", "flake8 error: E127 continuation line over-indented for visual indent"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.set_value": {"type": "function", "docstring": "Fast lookup of value from 1-dimensional ndarray.\n\nNotes\n-----\nOnly use this if you know what you're doing.", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4397, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4397", "errors": [["PR01", "Parameters {arr, key, value} not documented"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.isin": {"type": "function", "docstring": "Return a boolean array where the index values are in `values`.\n\nCompute boolean array of whether each index value is found in the\npassed set of values. The length of the returned boolean array matches\nthe length of the index.\n\nParameters\n----------\nvalues : set or list-like\n Sought values.\n\n .. versionadded:: 0.18.1\n\n Support for values as a set.\n\nlevel : str or int, optional\n Name or position of the index level to use (if the index is a\n `MultiIndex`).\n\nReturns\n-------\nis_contained : ndarray\n NumPy array of boolean values.\n\nSee Also\n--------\nSeries.isin : Same for Series.\nDataFrame.isin : Same method for DataFrames.\n\nNotes\n-----\nIn the case of `MultiIndex` you must either specify `values` as a\nlist-like object containing tuples that are the same length as the\nnumber of levels, or specify `level`. Otherwise it will raise a\n``ValueError``.\n\nIf `level` is specified:\n\n- if it is the name of one *and only one* index level, use that level;\n- otherwise it should be a number indicating level position.\n\nExamples\n--------\n>>> idx = pd.Index([1,2,3])\n>>> idx\nInt64Index([1, 2, 3], dtype='int64')\n\nCheck whether each index value in a list of values.\n>>> idx.isin([1, 4])\narray([ True, False, False])\n\n>>> midx = pd.MultiIndex.from_arrays([[1,2,3],\n... ['red', 'blue', 'green']],\n... names=('number', 'color'))\n>>> midx\nMultiIndex(levels=[[1, 2, 3], ['blue', 'green', 'red']],\n codes=[[0, 1, 2], [2, 0, 1]],\n names=['number', 'color'])\n\nCheck whether the strings in the 'color' level of the MultiIndex\nare in a list of colors.\n\n>>> midx.isin(['red', 'orange', 'yellow'], level='color')\narray([ True, False, False])\n\nTo check across the levels of a MultiIndex, pass a list of tuples:\n\n>>> midx.isin([(1, 'red'), (3, 'red')])\narray([ True, False, False])\n\nFor a DatetimeIndex, string values in `values` are converted to\nTimestamps.\n\n>>> dates = ['2000-03-11', '2000-03-12', '2000-03-13']\n>>> dti = pd.to_datetime(dates)\n>>> dti\nDatetimeIndex(['2000-03-11', '2000-03-12', '2000-03-13'],\ndtype='datetime64[ns]', freq=None)\n\n>>> dti.isin(['2000-03-11'])\narray([ True, False, False])", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4539, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4539", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["EX03", "flake8 error: E231 missing whitespace after ',' (4 times)"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.slice_indexer": {"type": "function", "docstring": "For an ordered or unique index, compute the slice indexer for input\nlabels and step.\n\nParameters\n----------\nstart : label, default None\n If None, defaults to the beginning\nend : label, default None\n If None, defaults to the end\nstep : int, default None\nkind : string, default None\n\nReturns\n-------\nindexer : slice\n\nRaises\n------\nKeyError : If key does not exist, or key is not unique and index is\n not ordered.\n\nNotes\n-----\nThis function assumes that the data is sorted, so use at your own peril\n\nExamples\n---------\nThis is a method on all index types. For example you can do:\n\n>>> idx = pd.Index(list('abcd'))\n>>> idx.slice_indexer(start='b', end='c')\nslice(1, 3)\n\n>>> idx = pd.MultiIndex.from_arrays([list('abcd'), list('efgh')])\n>>> idx.slice_indexer(start='b', end=('c', 'g'))\nslice(1, 3)", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4632, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4632", "errors": [["SS06", "Summary should fit in a single line"], ["PR09", "Parameter \"start\" description should finish with \".\""], ["PR09", "Parameter \"end\" description should finish with \".\""], ["PR07", "Parameter \"step\" has no description"], ["PR06", "Parameter \"kind\" type should use \"str\" instead of \"string\""], ["PR07", "Parameter \"kind\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 32, in pandas.Index.slice_indexer\nFailed example:\n idx.slice_indexer(start='b', end='c')\nExpected:\n slice(1, 3)\nGot:\n slice(1, 3, None)\n**********************************************************************\nLine 36, in pandas.Index.slice_indexer\nFailed example:\n idx.slice_indexer(start='b', end=('c', 'g'))\nExpected:\n slice(1, 3)\nGot:\n slice(1, 3, None)\n"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 32, in pandas.Index.slice_indexer\nFailed example:\n idx.slice_indexer(start='b', end='c')\nExpected:\n slice(1, 3)\nGot:\n slice(1, 3, None)\n**********************************************************************\nLine 36, in pandas.Index.slice_indexer\nFailed example:\n idx.slice_indexer(start='b', end=('c', 'g'))\nExpected:\n slice(1, 3)\nGot:\n slice(1, 3, None)\n", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Index.slice_locs": {"type": "function", "docstring": "Compute slice locations for input labels.\n\nParameters\n----------\nstart : label, default None\n If None, defaults to the beginning\nend : label, default None\n If None, defaults to the end\nstep : int, defaults None\n If None, defaults to 1\nkind : {'ix', 'loc', 'getitem'} or None\n\nReturns\n-------\nstart, end : int\n\nSee Also\n--------\nIndex.get_loc : Get location for a single label.\n\nNotes\n-----\nThis method only works if the index is monotonic or unique.\n\nExamples\n---------\n>>> idx = pd.Index(list('abcd'))\n>>> idx.slice_locs(start='b', end='c')\n(1, 3)", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 4831, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L4831", "errors": [["PR09", "Parameter \"start\" description should finish with \".\""], ["PR09", "Parameter \"end\" description should finish with \".\""], ["PR09", "Parameter \"step\" description should finish with \".\""], ["PR07", "Parameter \"kind\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.RangeIndex": {"type": "type", "docstring": "Immutable Index implementing a monotonic integer range.\n\nRangeIndex is a memory-saving special case of Int64Index limited to\nrepresenting monotonic ranges. Using RangeIndex may in some instances\nimprove computing speed.\n\nThis is the default index type used\nby DataFrame and Series when no explicit index is provided by the user.\n\nParameters\n----------\nstart : int (default: 0), or other RangeIndex instance\n If int and \"stop\" is not given, interpreted as \"stop\" instead.\nstop : int (default: 0)\nstep : int (default: 1)\nname : object, optional\n Name to be stored in the index\ncopy : bool, default False\n Unused, accepted for homogeneity with other index types.\n\nAttributes\n----------\nNone\n\nMethods\n-------\nfrom_range\n\nSee Also\n--------\nIndex : The base pandas Index type.\nInt64Index : Index of int64 data.", "deprecated": false, "file": "pandas/core/indexes/range.py", "file_line": 27, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/range.py#L27", "errors": [["PR01", "Parameters {fastpath, dtype} not documented"], ["PR07", "Parameter \"stop\" has no description"], ["PR07", "Parameter \"step\" has no description"], ["PR09", "Parameter \"name\" description should finish with \".\""]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Numeric Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Int64Index": {"type": "type", "docstring": "Immutable ndarray implementing an ordered, sliceable set. The basic object\nstoring axis labels for all pandas objects. Int64Index is a special case\nof `Index` with purely integer labels. \n\nParameters\n----------\ndata : array-like (1-dimensional)\ndtype : NumPy dtype (default: int64)\ncopy : bool\n Make a copy of input ndarray\nname : object\n Name to be stored in the index\n\nAttributes\n----------\nNone\n\nMethods\n-------\nNone\n\nSee Also\n--------\nIndex : The base pandas Index type.\n\nNotes\n-----\nAn Index instance can **only** contain hashable objects.", "deprecated": false, "file": "pandas/core/indexes/numeric.py", "file_line": 185, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/numeric.py#L185", "errors": [["SS03", "Summary does not end with a period"], ["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {fastpath} not documented"], ["PR07", "Parameter \"data\" has no description"], ["PR07", "Parameter \"dtype\" has no description"], ["PR09", "Parameter \"copy\" description should finish with \".\""], ["PR09", "Parameter \"name\" description should finish with \".\""]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Numeric Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.UInt64Index": {"type": "type", "docstring": "Immutable ndarray implementing an ordered, sliceable set. The basic object\nstoring axis labels for all pandas objects. UInt64Index is a special case\nof `Index` with purely unsigned integer labels. \n\nParameters\n----------\ndata : array-like (1-dimensional)\ndtype : NumPy dtype (default: uint64)\ncopy : bool\n Make a copy of input ndarray\nname : object\n Name to be stored in the index\n\nAttributes\n----------\nNone\n\nMethods\n-------\nNone\n\nSee Also\n--------\nIndex : The base pandas Index type.\n\nNotes\n-----\nAn Index instance can **only** contain hashable objects.", "deprecated": false, "file": "pandas/core/indexes/numeric.py", "file_line": 239, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/numeric.py#L239", "errors": [["SS03", "Summary does not end with a period"], ["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {fastpath} not documented"], ["PR07", "Parameter \"data\" has no description"], ["PR07", "Parameter \"dtype\" has no description"], ["PR09", "Parameter \"copy\" description should finish with \".\""], ["PR09", "Parameter \"name\" description should finish with \".\""]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Numeric Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.Float64Index": {"type": "type", "docstring": "Immutable ndarray implementing an ordered, sliceable set. The basic object\nstoring axis labels for all pandas objects. Float64Index is a special case\nof `Index` with purely float labels. \n\nParameters\n----------\ndata : array-like (1-dimensional)\ndtype : NumPy dtype (default: float64)\ncopy : bool\n Make a copy of input ndarray\nname : object\n Name to be stored in the index\n\nAttributes\n----------\nNone\n\nMethods\n-------\nNone\n\nSee Also\n--------\nIndex : The base pandas Index type.\n\nNotes\n-----\nAn Index instance can **only** contain hashable objects.", "deprecated": false, "file": "pandas/core/indexes/numeric.py", "file_line": 312, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/numeric.py#L312", "errors": [["SS03", "Summary does not end with a period"], ["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {fastpath} not documented"], ["PR07", "Parameter \"data\" has no description"], ["PR07", "Parameter \"dtype\" has no description"], ["PR09", "Parameter \"copy\" description should finish with \".\""], ["PR09", "Parameter \"name\" description should finish with \".\""]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Numeric Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.RangeIndex.from_range": {"type": "method", "docstring": "Create RangeIndex from a range (py3), or xrange (py2) object.", "deprecated": false, "file": "pandas/core/indexes/range.py", "file_line": 125, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/range.py#L125", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["PR01", "Parameters {data, dtype, name, **kwargs} not documented"], ["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "Numeric Index", "subsection": "Selecting", "shared_code_with": ""}, "pandas.CategoricalIndex": {"type": "type", "docstring": "Immutable Index implementing an ordered, sliceable set. CategoricalIndex\nrepresents a sparsely populated Index with an underlying Categorical.\n\nParameters\n----------\ndata : array-like or Categorical, (1-dimensional)\ncategories : optional, array-like\n categories for the CategoricalIndex\nordered : boolean,\n designating if the categories are ordered\ncopy : bool\n Make a copy of input ndarray\nname : object\n Name to be stored in the index\n\nAttributes\n----------\ncodes\ncategories\nordered\n\nMethods\n-------\nrename_categories\nreorder_categories\nadd_categories\nremove_categories\nremove_unused_categories\nset_categories\nas_ordered\nas_unordered\nmap\n\nSee Also\n--------\nCategorical, Index", "deprecated": false, "file": "pandas/core/indexes/category.py", "file_line": 43, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/category.py#L43", "errors": [["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {fastpath, dtype} not documented"], ["PR07", "Parameter \"data\" has no description"], ["PR08", "Parameter \"categories\" description should start with a capital letter"], ["PR09", "Parameter \"categories\" description should finish with \".\""], ["PR06", "Parameter \"ordered\" type should use \"bool\" instead of \"boolean\""], ["PR08", "Parameter \"ordered\" description should start with a capital letter"], ["PR09", "Parameter \"ordered\" description should finish with \".\""], ["PR09", "Parameter \"copy\" description should finish with \".\""], ["PR09", "Parameter \"name\" description should finish with \".\""], ["SA04", "Missing description for See Also \"Categorical\" reference"], ["SA04", "Missing description for See Also \"Index\" reference"]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "CategoricalIndex", "subsection": "Selecting", "shared_code_with": ""}, "pandas.CategoricalIndex.codes": {"type": "property", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/category.py", "file_line": 306, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/category.py#L306", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "CategoricalIndex", "subsection": "Categorical Components", "shared_code_with": ""}, "pandas.CategoricalIndex.categories": {"type": "property", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/category.py", "file_line": 310, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/category.py#L310", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "CategoricalIndex", "subsection": "Categorical Components", "shared_code_with": ""}, "pandas.CategoricalIndex.ordered": {"type": "property", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/category.py", "file_line": 314, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/category.py#L314", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "CategoricalIndex", "subsection": "Categorical Components", "shared_code_with": ""}, "pandas.CategoricalIndex.rename_categories": {"type": "function", "docstring": "Renames categories.\n\nParameters\n----------\nnew_categories : list-like, dict-like or callable\n\n * list-like: all items must be unique and the number of items in\n the new categories must match the existing number of categories.\n\n * dict-like: specifies a mapping from\n old categories to new. Categories not contained in the mapping\n are passed through and extra categories in the mapping are\n ignored.\n\n .. versionadded:: 0.21.0\n\n * callable : a callable that is called on all items in the old\n categories and whose return values comprise the new categories.\n\n .. versionadded:: 0.23.0\n\n .. warning::\n\n Currently, Series are considered list like. In a future version\n of pandas they'll be considered dict-like.\n\ninplace : boolean (default: False)\n Whether or not to rename the categories inplace or return a copy of\n this categorical with renamed categories.\n\nReturns\n-------\ncat : Categorical or None\n With ``inplace=False``, the new categorical is returned.\n With ``inplace=True``, there is no return value.\n\nRaises\n------\nValueError\n If new categories are list-like and do not have the same number of\n items than the current categories or do not validate as categories\n\nSee Also\n--------\nreorder_categories\nadd_categories\nremove_categories\nremove_unused_categories\nset_categories\n\nExamples\n--------\n>>> c = pd.Categorical(['a', 'a', 'b'])\n>>> c.rename_categories([0, 1])\n[0, 0, 1]\nCategories (2, int64): [0, 1]\n\nFor dict-like ``new_categories``, extra keys are ignored and\ncategories not in the dictionary are passed through\n\n>>> c.rename_categories({'a': 'A', 'c': 'C'})\n[A, A, b]\nCategories (2, object): [A, b]\n\nYou may also provide a callable to create the new categories\n\n>>> c.rename_categories(lambda x: x.upper())\n[A, A, B]\nCategories (2, object): [A, B]", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["SS05", "Summary must start with infinitive verb, not third person (e.g. use \"Generate\" instead of \"Generates\")"], ["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {new_categories, inplace}"], ["PR08", "Parameter \"new_categories\" description should start with a capital letter"], ["PR09", "Parameter \"new_categories\" description should finish with \".\""], ["PR06", "Parameter \"inplace\" type should use \"bool\" instead of \"boolean\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["SA04", "Missing description for See Also \"reorder_categories\" reference"], ["SA04", "Missing description for See Also \"add_categories\" reference"], ["SA04", "Missing description for See Also \"remove_categories\" reference"], ["SA04", "Missing description for See Also \"remove_unused_categories\" reference"], ["SA04", "Missing description for See Also \"set_categories\" reference"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "CategoricalIndex", "subsection": "Categorical Components", "shared_code_with": ""}, "pandas.CategoricalIndex.reorder_categories": {"type": "function", "docstring": "Reorders categories as specified in new_categories.\n\n`new_categories` need to include all old categories and no new category\nitems.\n\nParameters\n----------\nnew_categories : Index-like\n The categories in new order.\nordered : boolean, optional\n Whether or not the categorical is treated as a ordered categorical.\n If not given, do not change the ordered information.\ninplace : boolean (default: False)\n Whether or not to reorder the categories inplace or return a copy of\n this categorical with reordered categories.\n\nReturns\n-------\ncat : Categorical with reordered categories or None if inplace.\n\nRaises\n------\nValueError\n If the new categories do not contain all old category items or any\n new ones\n\nSee Also\n--------\nrename_categories\nadd_categories\nremove_categories\nremove_unused_categories\nset_categories", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["SS05", "Summary must start with infinitive verb, not third person (e.g. use \"Generate\" instead of \"Generates\")"], ["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {ordered, new_categories, inplace}"], ["PR06", "Parameter \"ordered\" type should use \"bool\" instead of \"boolean\""], ["PR06", "Parameter \"inplace\" type should use \"bool\" instead of \"boolean\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["SA04", "Missing description for See Also \"rename_categories\" reference"], ["SA04", "Missing description for See Also \"add_categories\" reference"], ["SA04", "Missing description for See Also \"remove_categories\" reference"], ["SA04", "Missing description for See Also \"remove_unused_categories\" reference"], ["SA04", "Missing description for See Also \"set_categories\" reference"]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "CategoricalIndex", "subsection": "Categorical Components", "shared_code_with": "pandas.CategoricalIndex.rename_categories"}, "pandas.CategoricalIndex.add_categories": {"type": "function", "docstring": "Add new categories.\n\n`new_categories` will be included at the last/highest place in the\ncategories and will be unused directly after this call.\n\nParameters\n----------\nnew_categories : category or list-like of category\n The new categories to be included.\ninplace : boolean (default: False)\n Whether or not to add the categories inplace or return a copy of\n this categorical with added categories.\n\nReturns\n-------\ncat : Categorical with new categories added or None if inplace.\n\nRaises\n------\nValueError\n If the new categories include old categories or do not validate as\n categories\n\nSee Also\n--------\nrename_categories\nreorder_categories\nremove_categories\nremove_unused_categories\nset_categories", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {new_categories, inplace}"], ["PR06", "Parameter \"inplace\" type should use \"bool\" instead of \"boolean\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["SA04", "Missing description for See Also \"rename_categories\" reference"], ["SA04", "Missing description for See Also \"reorder_categories\" reference"], ["SA04", "Missing description for See Also \"remove_categories\" reference"], ["SA04", "Missing description for See Also \"remove_unused_categories\" reference"], ["SA04", "Missing description for See Also \"set_categories\" reference"]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "CategoricalIndex", "subsection": "Categorical Components", "shared_code_with": "pandas.CategoricalIndex.reorder_categories"}, "pandas.CategoricalIndex.remove_categories": {"type": "function", "docstring": "Removes the specified categories.\n\n`removals` must be included in the old categories. Values which were in\nthe removed categories will be set to NaN\n\nParameters\n----------\nremovals : category or list of categories\n The categories which should be removed.\ninplace : boolean (default: False)\n Whether or not to remove the categories inplace or return a copy of\n this categorical with removed categories.\n\nReturns\n-------\ncat : Categorical with removed categories or None if inplace.\n\nRaises\n------\nValueError\n If the removals are not contained in the categories\n\nSee Also\n--------\nrename_categories\nreorder_categories\nadd_categories\nremove_unused_categories\nset_categories", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["SS05", "Summary must start with infinitive verb, not third person (e.g. use \"Generate\" instead of \"Generates\")"], ["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {removals, inplace}"], ["PR06", "Parameter \"inplace\" type should use \"bool\" instead of \"boolean\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["SA04", "Missing description for See Also \"rename_categories\" reference"], ["SA04", "Missing description for See Also \"reorder_categories\" reference"], ["SA04", "Missing description for See Also \"add_categories\" reference"], ["SA04", "Missing description for See Also \"remove_unused_categories\" reference"], ["SA04", "Missing description for See Also \"set_categories\" reference"]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "CategoricalIndex", "subsection": "Categorical Components", "shared_code_with": "pandas.CategoricalIndex.add_categories"}, "pandas.CategoricalIndex.remove_unused_categories": {"type": "function", "docstring": "Removes categories which are not used.\n\nParameters\n----------\ninplace : boolean (default: False)\n Whether or not to drop unused categories inplace or return a copy of\n this categorical with unused categories dropped.\n\nReturns\n-------\ncat : Categorical with unused categories dropped or None if inplace.\n\nSee Also\n--------\nrename_categories\nreorder_categories\nadd_categories\nremove_categories\nset_categories", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["SS05", "Summary must start with infinitive verb, not third person (e.g. use \"Generate\" instead of \"Generates\")"], ["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {inplace}"], ["PR06", "Parameter \"inplace\" type should use \"bool\" instead of \"boolean\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["SA04", "Missing description for See Also \"rename_categories\" reference"], ["SA04", "Missing description for See Also \"reorder_categories\" reference"], ["SA04", "Missing description for See Also \"add_categories\" reference"], ["SA04", "Missing description for See Also \"remove_categories\" reference"], ["SA04", "Missing description for See Also \"set_categories\" reference"]], "warnings": [["ES01", "No extended summary found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "CategoricalIndex", "subsection": "Categorical Components", "shared_code_with": "pandas.CategoricalIndex.remove_categories"}, "pandas.CategoricalIndex.set_categories": {"type": "function", "docstring": "Sets the categories to the specified new_categories.\n\n`new_categories` can include new categories (which will result in\nunused categories) or remove old categories (which results in values\nset to NaN). If `rename==True`, the categories will simple be renamed\n(less or more items than in old categories will result in values set to\nNaN or in unused categories respectively).\n\nThis method can be used to perform more than one action of adding,\nremoving, and reordering simultaneously and is therefore faster than\nperforming the individual steps via the more specialised methods.\n\nOn the other hand this methods does not do checks (e.g., whether the\nold categories are included in the new categories on a reorder), which\ncan result in surprising changes, for example when using special string\ndtypes on python3, which does not considers a S1 string equal to a\nsingle char python string.\n\nParameters\n----------\nnew_categories : Index-like\n The categories in new order.\nordered : boolean, (default: False)\n Whether or not the categorical is treated as a ordered categorical.\n If not given, do not change the ordered information.\nrename : boolean (default: False)\n Whether or not the new_categories should be considered as a rename\n of the old categories or as reordered categories.\ninplace : boolean (default: False)\n Whether or not to reorder the categories inplace or return a copy of\n this categorical with reordered categories.\n\nReturns\n-------\ncat : Categorical with reordered categories or None if inplace.\n\nRaises\n------\nValueError\n If new_categories does not validate as categories\n\nSee Also\n--------\nrename_categories\nreorder_categories\nadd_categories\nremove_categories\nremove_unused_categories", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["SS05", "Summary must start with infinitive verb, not third person (e.g. use \"Generate\" instead of \"Generates\")"], ["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {ordered, new_categories, inplace, rename}"], ["PR06", "Parameter \"ordered\" type should use \"bool\" instead of \"boolean\""], ["PR06", "Parameter \"rename\" type should use \"bool\" instead of \"boolean\""], ["PR06", "Parameter \"inplace\" type should use \"bool\" instead of \"boolean\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["SA04", "Missing description for See Also \"rename_categories\" reference"], ["SA04", "Missing description for See Also \"reorder_categories\" reference"], ["SA04", "Missing description for See Also \"add_categories\" reference"], ["SA04", "Missing description for See Also \"remove_categories\" reference"], ["SA04", "Missing description for See Also \"remove_unused_categories\" reference"]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "CategoricalIndex", "subsection": "Categorical Components", "shared_code_with": "pandas.CategoricalIndex.remove_unused_categories"}, "pandas.CategoricalIndex.as_ordered": {"type": "function", "docstring": "Set the Categorical to be ordered.\n\nParameters\n----------\ninplace : boolean (default: False)\n Whether or not to set the ordered attribute inplace or return a copy\n of this categorical with ordered set to True", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {inplace}"], ["PR06", "Parameter \"inplace\" type should use \"bool\" instead of \"boolean\""], ["PR09", "Parameter \"inplace\" description should finish with \".\""], ["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "CategoricalIndex", "subsection": "Categorical Components", "shared_code_with": "pandas.CategoricalIndex.set_categories"}, "pandas.CategoricalIndex.as_unordered": {"type": "function", "docstring": "Set the Categorical to be unordered.\n\nParameters\n----------\ninplace : boolean (default: False)\n Whether or not to set the ordered attribute inplace or return a copy\n of this categorical with ordered set to False", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {inplace}"], ["PR06", "Parameter \"inplace\" type should use \"bool\" instead of \"boolean\""], ["PR09", "Parameter \"inplace\" description should finish with \".\""], ["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "CategoricalIndex", "subsection": "Categorical Components", "shared_code_with": "pandas.CategoricalIndex.as_ordered"}, "pandas.CategoricalIndex.map": {"type": "function", "docstring": "Map values using input correspondence (a dict, Series, or function).\n\nMaps the values (their categories, not the codes) of the index to new\ncategories. If the mapping correspondence is one-to-one the result is a\n:class:`~pandas.CategoricalIndex` which has the same order property as\nthe original, otherwise an :class:`~pandas.Index` is returned.\n\nIf a `dict` or :class:`~pandas.Series` is used any unmapped category is\nmapped to `NaN`. Note that if this happens an :class:`~pandas.Index`\nwill be returned.\n\nParameters\n----------\nmapper : function, dict, or Series\n Mapping correspondence.\n\nReturns\n-------\npandas.CategoricalIndex or pandas.Index\n Mapped index.\n\nSee Also\n--------\nIndex.map : Apply a mapping correspondence on an\n :class:`~pandas.Index`.\nSeries.map : Apply a mapping correspondence on a\n :class:`~pandas.Series`.\nSeries.apply : Apply more complex functions on a\n :class:`~pandas.Series`.\n\nExamples\n--------\n>>> idx = pd.CategoricalIndex(['a', 'b', 'c'])\n>>> idx\nCategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'],\n ordered=False, dtype='category')\n>>> idx.map(lambda x: x.upper())\nCategoricalIndex(['A', 'B', 'C'], categories=['A', 'B', 'C'],\n ordered=False, dtype='category')\n>>> idx.map({'a': 'first', 'b': 'second', 'c': 'third'})\nCategoricalIndex(['first', 'second', 'third'], categories=['first',\n 'second', 'third'], ordered=False, dtype='category')\n\nIf the mapping is one-to-one the ordering of the categories is\npreserved:\n\n>>> idx = pd.CategoricalIndex(['a', 'b', 'c'], ordered=True)\n>>> idx\nCategoricalIndex(['a', 'b', 'c'], categories=['a', 'b', 'c'],\n ordered=True, dtype='category')\n>>> idx.map({'a': 3, 'b': 2, 'c': 1})\nCategoricalIndex([3, 2, 1], categories=[3, 2, 1], ordered=True,\n dtype='category')\n\nIf the mapping is not one-to-one an :class:`~pandas.Index` is returned:\n\n>>> idx.map({'a': 'first', 'b': 'second', 'c': 'first'})\nIndex(['first', 'second', 'first'], dtype='object')\n\nIf a `dict` is used, all unmapped categories are mapped to `NaN` and\nthe result is an :class:`~pandas.Index`:\n\n>>> idx.map({'a': 'first', 'b': 'second'})\nIndex(['first', 'second', nan], dtype='object')", "deprecated": false, "file": "pandas/core/indexes/category.py", "file_line": 667, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/category.py#L667", "errors": [], "warnings": [], "examples_errors": "", "in_api": true, "section": "CategoricalIndex", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.CategoricalIndex.equals": {"type": "function", "docstring": "Determines if two CategorialIndex objects contain the same elements.", "deprecated": false, "file": "pandas/core/indexes/category.py", "file_line": 233, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/category.py#L233", "errors": [["SS05", "Summary must start with infinitive verb, not third person (e.g. use \"Generate\" instead of \"Generates\")"], ["PR01", "Parameters {other} not documented"], ["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "CategoricalIndex", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.IntervalIndex": {"type": "_WritableDoc", "docstring": "Immutable index of intervals that are closed on the same side.\n\n.. versionadded:: 0.20.0\n\n.. warning::\n\n The indexing behaviors are provisional and may change in\n a future version of pandas.\n\nParameters\n----------\ndata : array-like (1-dimensional)\n Array-like containing Interval objects from which to build the\n IntervalIndex.\nclosed : {'left', 'right', 'both', 'neither'}, default 'right'\n Whether the intervals are closed on the left-side, right-side, both or\n neither.\ndtype : dtype or None, default None\n If None, dtype will be inferred.\n\n .. versionadded:: 0.23.0\ncopy : bool, default False\n Copy the input data.\nname : object, optional\n Name to be stored in the index.\nverify_integrity : bool, default True\n Verify that the IntervalIndex is valid.\n\nAttributes\n----------\nleft\nright\nclosed\nmid\nlength\nis_non_overlapping_monotonic\nis_overlapping\nvalues\n\nMethods\n-------\nfrom_arrays\nfrom_tuples\nfrom_breaks\noverlaps\nset_closed\nto_tuples\ncontains\n\nSee Also\n--------\nIndex : The base pandas Index type.\nInterval : A bounded slice-like interval; the elements of an IntervalIndex.\ninterval_range : Function to create a fixed frequency IntervalIndex.\ncut : Bin values into discrete Intervals.\nqcut : Bin values into equal-sized Intervals based on rank or sample quantiles.\n\nNotes\n------\nSee the `user guide\n<http://pandas.pydata.org/pandas-docs/stable/advanced.html#intervalindex>`_\nfor more.\n\nExamples\n--------\nA new ``IntervalIndex`` is typically constructed using\n:func:`interval_range`:\n\n>>> pd.interval_range(start=0, end=5)\nIntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]],\n closed='right',\n dtype='interval[int64]')\n\nIt may also be constructed using one of the constructor\nmethods: :meth:`IntervalIndex.from_arrays`,\n:meth:`IntervalIndex.from_breaks`, and :meth:`IntervalIndex.from_tuples`.\n\nSee further examples in the doc strings of ``interval_range`` and the\nmentioned constructor methods.", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 129, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L129", "errors": [], "warnings": [], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "Modifying and Computations", "shared_code_with": ""}, "pandas.IntervalIndex.from_arrays": {"type": "method", "docstring": "Construct from two arrays defining the left and right bounds.\n\nParameters\n----------\nleft : array-like (1-dimensional)\n Left bounds for each interval.\nright : array-like (1-dimensional)\n Right bounds for each interval.\nclosed : {'left', 'right', 'both', 'neither'}, default 'right'\n Whether the intervals are closed on the left-side, right-side, both\n or neither.\ncopy : boolean, default False\n Copy the data.\ndtype : dtype, optional\n If None, dtype will be inferred.\n\n .. versionadded:: 0.23.0\n\nReturns\n-------\nIntervalIndex\n\nRaises\n------\nValueError\n When a value is missing in only one of `left` or `right`.\n When a value in `left` is greater than the corresponding value\n in `right`.\n\nSee Also\n--------\ninterval_range : Function to create a fixed frequency IntervalIndex.\nIntervalIndex.from_breaks : Construct an IntervalIndex from an array of\n splits.\nIntervalIndex.from_tuples : Construct an IntervalIndex from an\n array-like of tuples.\n\nNotes\n-----\nEach element of `left` must be less than or equal to the `right`\nelement at the same position. If an element is missing, it must be\nmissing in both `left` and `right`. A TypeError is raised when\nusing an unsupported type for `left` or `right`. At the moment,\n'category', 'object', and 'string' subtypes are not supported.\n\nExamples\n--------\n>>> IntervalIndex.from_arrays([0, 1, 2], [1, 2, 3])\nIntervalIndex([(0, 1], (1, 2], (2, 3]],\n closed='right',\n dtype='interval[int64]')", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 183, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L183", "errors": [["PR01", "Parameters {name} not documented"], ["PR06", "Parameter \"copy\" type should use \"bool\" instead of \"boolean\""], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 49, in pandas.IntervalIndex.from_arrays\nFailed example:\n IntervalIndex.from_arrays([0, 1, 2], [1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.IntervalIndex.from_arrays[0]>\", line 1, in <module>\n IntervalIndex.from_arrays([0, 1, 2], [1, 2, 3])\n NameError: name 'IntervalIndex' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'IntervalIndex'"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "**********************************************************************\nLine 49, in pandas.IntervalIndex.from_arrays\nFailed example:\n IntervalIndex.from_arrays([0, 1, 2], [1, 2, 3])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.IntervalIndex.from_arrays[0]>\", line 1, in <module>\n IntervalIndex.from_arrays([0, 1, 2], [1, 2, 3])\n NameError: name 'IntervalIndex' is not defined\n", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.IntervalIndex.from_tuples": {"type": "method", "docstring": "Construct an IntervalIndex from an array-like of tuples\n\nParameters\n----------\ndata : array-like (1-dimensional)\n Array of tuples\nclosed : {'left', 'right', 'both', 'neither'}, default 'right'\n Whether the intervals are closed on the left-side, right-side, both\n or neither.\ncopy : boolean, default False\n by-default copy the data, this is compat only and ignored\ndtype : dtype or None, default None\n If None, dtype will be inferred\n\n ..versionadded:: 0.23.0\n\nSee Also\n--------\ninterval_range : Function to create a fixed frequency IntervalIndex.\nIntervalIndex.from_arrays : Construct an IntervalIndex from a left and\n right array.\nIntervalIndex.from_breaks : Construct an IntervalIndex from an array of\n splits.\n\nExamples\n--------\n>>> pd.IntervalIndex.from_tuples([(0, 1), (1, 2)])\nIntervalIndex([(0, 1], (1, 2]],\n closed='right', dtype='interval[int64]')", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 207, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L207", "errors": [["SS03", "Summary does not end with a period"], ["PR01", "Parameters {name} not documented"], ["PR09", "Parameter \"data\" description should finish with \".\""], ["PR06", "Parameter \"copy\" type should use \"bool\" instead of \"boolean\""], ["PR08", "Parameter \"copy\" description should start with a capital letter"], ["PR09", "Parameter \"copy\" description should finish with \".\""], ["PR09", "Parameter \"dtype\" description should finish with \".\""], ["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.IntervalIndex.from_breaks": {"type": "method", "docstring": "Construct an IntervalIndex from an array of splits.\n\nParameters\n----------\nbreaks : array-like (1-dimensional)\n Left and right bounds for each interval.\nclosed : {'left', 'right', 'both', 'neither'}, default 'right'\n Whether the intervals are closed on the left-side, right-side, both\n or neither.\ncopy : boolean, default False\n copy the data\ndtype : dtype or None, default None\n If None, dtype will be inferred\n\n .. versionadded:: 0.23.0\n\nSee Also\n--------\ninterval_range : Function to create a fixed frequency IntervalIndex.\nIntervalIndex.from_arrays : Construct from a left and right array.\nIntervalIndex.from_tuples : Construct from a sequence of tuples.\n\nExamples\n--------\n>>> pd.IntervalIndex.from_breaks([0, 1, 2, 3])\nIntervalIndex([(0, 1], (1, 2], (2, 3]],\n closed='right',\n dtype='interval[int64]')", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 174, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L174", "errors": [["PR01", "Parameters {name} not documented"], ["PR06", "Parameter \"copy\" type should use \"bool\" instead of \"boolean\""], ["PR08", "Parameter \"copy\" description should start with a capital letter"], ["PR09", "Parameter \"copy\" description should finish with \".\""], ["PR09", "Parameter \"dtype\" description should finish with \".\""], ["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.IntervalIndex.contains": {"type": "function", "docstring": "Return a boolean indicating if the key is IN the index\n\nWe accept / allow keys to be not *just* actual\nobjects.\n\nParameters\n----------\nkey : int, float, Interval\n\nReturns\n-------\nboolean", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 260, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L260", "errors": [["SS03", "Summary does not end with a period"], ["PR07", "Parameter \"key\" has no description"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.IntervalIndex.left": {"type": "property", "docstring": "Return the left endpoints of each Interval in the IntervalIndex as\nan Index", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 301, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L301", "errors": [["SS03", "Summary does not end with a period"], ["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.IntervalIndex.right": {"type": "property", "docstring": "Return the right endpoints of each Interval in the IntervalIndex as\nan Index", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 309, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L309", "errors": [["SS03", "Summary does not end with a period"], ["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.IntervalIndex.mid": {"type": "CachedProperty", "docstring": "Return the midpoint of each Interval in the IntervalIndex as an Index", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": "pandas.Index.name"}, "pandas.IntervalIndex.closed": {"type": "property", "docstring": "Whether the intervals are closed on the left-side, right-side, both or\nneither", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 317, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L317", "errors": [["SS03", "Summary does not end with a period"], ["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.IntervalIndex.length": {"type": "property", "docstring": "Return an Index with entries denoting the length of each Interval in\nthe IntervalIndex", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 335, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L335", "errors": [["SS03", "Summary does not end with a period"], ["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.IntervalIndex.values": {"type": "CachedProperty", "docstring": "Return the IntervalIndex's data as an IntervalArray.", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": "pandas.IntervalIndex.mid"}, "pandas.IntervalIndex.is_non_overlapping_monotonic": {"type": "CachedProperty", "docstring": "Return True if the IntervalIndex is non-overlapping (no Intervals share\npoints) and is either monotonic increasing or monotonic decreasing,\nelse False", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["SS03", "Summary does not end with a period"], ["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": "pandas.IntervalIndex.values"}, "pandas.IntervalIndex.is_overlapping": {"type": "property", "docstring": "Return True if the IntervalIndex has overlapping intervals, else False.\n\nTwo intervals overlap if they share a common point, including closed\nendpoints. Intervals that only have an open endpoint in common do not\noverlap.\n\n.. versionadded:: 0.24.0\n\nReturns\n-------\nbool\n Boolean indicating if the IntervalIndex has overlapping intervals.\n\nSee Also\n--------\nInterval.overlaps : Check whether two Interval objects overlap.\nIntervalIndex.overlaps : Check an IntervalIndex elementwise for\n overlaps.\n\nExamples\n--------\n>>> index = pd.IntervalIndex.from_tuples([(0, 2), (1, 3), (4, 5)])\n>>> index\nIntervalIndex([(0, 2], (1, 3], (4, 5]],\n closed='right',\n dtype='interval[int64]')\n>>> index.is_overlapping\nTrue\n\nIntervals that share closed endpoints overlap:\n\n>>> index = pd.interval_range(0, 3, closed='both')\n>>> index\nIntervalIndex([[0, 1], [1, 2], [2, 3]],\n closed='both',\n dtype='interval[int64]')\n>>> index.is_overlapping\nTrue\n\nIntervals that only have an open endpoint in common do not overlap:\n\n>>> index = pd.interval_range(0, 3, closed='left')\n>>> index\nIntervalIndex([[0, 1), [1, 2), [2, 3)],\n closed='left',\n dtype='interval[int64]')\n>>> index.is_overlapping\nFalse", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 474, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L474", "errors": [], "warnings": [], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.IntervalIndex.get_loc": {"type": "function", "docstring": "Get integer location, slice or boolean mask for requested label.\n\nParameters\n----------\nkey : label\nmethod : {None}, optional\n * default: matches where the label is within an interval only.\n\nReturns\n-------\nloc : int if unique index, slice if monotonic index, else mask\n\nExamples\n---------\n>>> i1, i2 = pd.Interval(0, 1), pd.Interval(1, 2)\n>>> index = pd.IntervalIndex([i1, i2])\n>>> index.get_loc(1)\n0\n\nYou can also supply an interval or an location for a point inside an\ninterval.\n\n>>> index.get_loc(pd.Interval(0, 2))\narray([0, 1], dtype=int64)\n>>> index.get_loc(1.5)\n1\n\nIf a label is in several intervals, you get the locations of all the\nrelevant intervals.\n\n>>> i3 = pd.Interval(0, 2)\n>>> overlapping_index = pd.IntervalIndex([i2, i3])\n>>> overlapping_index.get_loc(1.5)\narray([0, 1], dtype=int64)", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 721, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L721", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["PR07", "Parameter \"key\" has no description"], ["PR08", "Parameter \"method\" description should start with a capital letter"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 23, in pandas.IntervalIndex.get_loc\nFailed example:\n index.get_loc(pd.Interval(0, 2))\nExpected:\n array([0, 1], dtype=int64)\nGot:\n slice(0, 2, None)\n**********************************************************************\nLine 33, in pandas.IntervalIndex.get_loc\nFailed example:\n overlapping_index.get_loc(1.5)\nExpected:\n array([0, 1], dtype=int64)\nGot:\n array([0, 1])\n"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 23, in pandas.IntervalIndex.get_loc\nFailed example:\n index.get_loc(pd.Interval(0, 2))\nExpected:\n array([0, 1], dtype=int64)\nGot:\n slice(0, 2, None)\n**********************************************************************\nLine 33, in pandas.IntervalIndex.get_loc\nFailed example:\n overlapping_index.get_loc(1.5)\nExpected:\n array([0, 1], dtype=int64)\nGot:\n array([0, 1])\n", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.IntervalIndex.get_indexer": {"type": "function", "docstring": "Compute indexer and mask for new index given the current index. The\nindexer should be then used as an input to ndarray.take to align the\ncurrent data to the new index.\n\nParameters\n----------\ntarget : IntervalIndex or list of Intervals\nmethod : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional\n * default: exact matches only.\n * pad / ffill: find the PREVIOUS index value if no exact match.\n * backfill / bfill: use NEXT index value if no exact match\n * nearest: use the NEAREST index value if no exact match. Tied\n distances are broken by preferring the larger index value.\nlimit : int, optional\n Maximum number of consecutive labels in ``target`` to match for\n inexact matches.\ntolerance : optional\n Maximum distance between original and new labels for inexact\n matches. The values of the index at the matching locations most\n satisfy the equation ``abs(index[indexer] - target) <= tolerance``.\n\n Tolerance may be a scalar value, which applies the same tolerance\n to all values, or list-like, which applies variable tolerance per\n element. List-like includes list, tuple, array, Series, and must be\n the same size as the index and its dtype must exactly match the\n index's type.\n\n .. versionadded:: 0.21.0 (list-like tolerance)\n\nReturns\n-------\nindexer : ndarray of int\n Integers from 0 to n - 1 indicating that the index at these\n positions matches the corresponding target values. Missing values\n in the target are marked by -1.\n\nExamples\n--------\n>>> index = pd.Index(['c', 'a', 'b'])\n>>> index.get_indexer(['a', 'b', 'x'])\narray([ 1, 2, -1])\n\nNotice that the return value is an array of locations in ``index``\nand ``x`` is marked by -1, as it is not in ``index``.", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 811, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L811", "errors": [["SS06", "Summary should fit in a single line"], ["PR07", "Parameter \"target\" has no description"], ["PR08", "Parameter \"method\" description should start with a capital letter"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.IntervalIndex.set_closed": {"type": "function", "docstring": "Return an IntervalIndex identical to the current one, but closed on the\nspecified side\n\n.. versionadded:: 0.24.0\n\nParameters\n----------\nclosed : {'left', 'right', 'both', 'neither'}\n Whether the intervals are closed on the left-side, right-side, both\n or neither.\n\nReturns\n-------\nnew_index : IntervalIndex\n\nExamples\n--------\n>>> index = pd.interval_range(0, 3)\n>>> index\nIntervalIndex([(0, 1], (1, 2], (2, 3]],\n closed='right',\n dtype='interval[int64]')\n>>> index.set_closed('both')\nIntervalIndex([[0, 1], [1, 2], [2, 3]],\n closed='both',\n dtype='interval[int64]')", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 325, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L325", "errors": [["SS03", "Summary does not end with a period"], ["SS06", "Summary should fit in a single line"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.IntervalIndex.overlaps": {"type": "function", "docstring": "Check elementwise if an Interval overlaps the values in the IntervalIndex.\n\nTwo intervals overlap if they share a common point, including closed\nendpoints. Intervals that only have an open endpoint in common do not\noverlap.\n\n.. versionadded:: 0.24.0\n\nParameters\n----------\nother : Interval\n Interval to check against for an overlap.\n\nReturns\n-------\nndarray\n Boolean array positionally indicating where an overlap occurs.\n\nSee Also\n--------\nInterval.overlaps : Check whether two Interval objects overlap.\n\nExamples\n--------\n>>> intervals = pd.IntervalIndex.from_tuples([(0, 1), (1, 3), (2, 4)])\n>>> intervals\nIntervalIndex([(0, 1], (1, 3], (2, 4]],\n closed='right',\n dtype='interval[int64]')\n>>> intervals.overlaps(pd.Interval(0.5, 1.5))\narray([ True, True, False])\n\nIntervals that share closed endpoints overlap:\n\n>>> intervals.overlaps(pd.Interval(1, 3, closed='left'))\narray([ True, True, True])\n\nIntervals that only have an open endpoint in common do not overlap:\n\n>>> intervals.overlaps(pd.Interval(1, 2, closed='right'))\narray([False, True, False])", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 1092, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L1092", "errors": [], "warnings": [], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.IntervalIndex.to_tuples": {"type": "function", "docstring": "Return an Index of tuples of the form (left, right)\n\nParameters\n----------\nna_tuple : boolean, default True\n Returns NA as a tuple if True, ``(nan, nan)``, or just as the NA\n value itself if False, ``nan``.\n\n .. versionadded:: 0.23.0\n\nReturns\n-------\ntuples: Index\n\nExamples\n--------\n>>> idx = pd.IntervalIndex.from_arrays([0, np.nan, 2], [1, np.nan, 3])\n>>> idx.to_tuples()\nIndex([(0.0, 1.0), (nan, nan), (2.0, 3.0)], dtype='object')\n>>> idx.to_tuples(na_tuple=False)\nIndex([(0.0, 1.0), nan, (2.0, 3.0)], dtype='object')", "deprecated": false, "file": "pandas/core/indexes/interval.py", "file_line": 281, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/interval.py#L281", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"], ["PR06", "Parameter \"na_tuple\" type should use \"bool\" instead of \"boolean\""], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "IntervalIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.MultiIndex": {"type": "type", "docstring": "A multi-level, or hierarchical, index object for pandas objects.\n\nParameters\n----------\nlevels : sequence of arrays\n The unique labels for each level.\ncodes : sequence of arrays\n Integers for each level designating which label at each location.\n\n .. versionadded:: 0.24.0\nlabels : sequence of arrays\n Integers for each level designating which label at each location.\n\n .. deprecated:: 0.24.0\n Use ``codes`` instead\nsortorder : optional int\n Level of sortedness (must be lexicographically sorted by that\n level).\nnames : optional sequence of objects\n Names for each of the index levels. (name is accepted for compat).\ncopy : bool, default False\n Copy the meta-data.\nverify_integrity : bool, default True\n Check that the levels/codes are consistent and valid.\n\nAttributes\n----------\nnames\nlevels\ncodes\nnlevels\nlevshape\n\nMethods\n-------\nfrom_arrays\nfrom_tuples\nfrom_product\nfrom_frame\nset_levels\nset_codes\nto_frame\nto_flat_index\nis_lexsorted\nsortlevel\ndroplevel\nswaplevel\nreorder_levels\nremove_unused_levels\n\nSee Also\n--------\nMultiIndex.from_arrays : Convert list of arrays to MultiIndex.\nMultiIndex.from_product : Create a MultiIndex from the cartesian product\n of iterables.\nMultiIndex.from_tuples : Convert list of tuples to a MultiIndex.\nMultiIndex.from_frame : Make a MultiIndex from a DataFrame.\nIndex : The base pandas Index type.\n\nExamples\n---------\nA new ``MultiIndex`` is typically constructed using one of the helper\nmethods :meth:`MultiIndex.from_arrays`, :meth:`MultiIndex.from_product`\nand :meth:`MultiIndex.from_tuples`. For example (using ``.from_arrays``):\n\n>>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]\n>>> pd.MultiIndex.from_arrays(arrays, names=('number', 'color'))\nMultiIndex(levels=[[1, 2], ['blue', 'red']],\n codes=[[0, 0, 1, 1], [1, 0, 1, 0]],\n names=['number', 'color'])\n\nSee further examples for how to construct a MultiIndex in the doc strings\nof the mentioned helper methods.\n\nNotes\n-----\nSee the `user guide\n<http://pandas.pydata.org/pandas-docs/stable/advanced.html>`_ for more.", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 123, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L123", "errors": [["PR01", "Parameters {_set_identity, name, dtype} not documented"], ["PR02", "Unknown parameters {labels}"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "IntervalIndex Components", "shared_code_with": ""}, "pandas.IndexSlice": {"type": "_IndexSlice", "docstring": "Create an object to more easily perform multi-index slicing\n\nSee Also\n--------\nMultiIndex.remove_unused_levels : New MultiIndex with no unused levels.\n\nNotes\n-----\nSee :ref:`Defined Levels <advanced.shown_levels>`\nfor further info on slicing a MultiIndex.\n\nExamples\n--------\n\n>>> midx = pd.MultiIndex.from_product([['A0','A1'], ['B0','B1','B2','B3']])\n>>> columns = ['foo', 'bar']\n>>> dfmi = pd.DataFrame(np.arange(16).reshape((len(midx), len(columns))),\n index=midx, columns=columns)\n\nUsing the default slice command:\n\n>>> dfmi.loc[(slice(None), slice('B0', 'B1')), :]\n foo bar\n A0 B0 0 1\n B1 2 3\n A1 B0 8 9\n B1 10 11\n\nUsing the IndexSlice class for a more intuitive command:\n\n>>> idx = pd.IndexSlice\n>>> dfmi.loc[idx[:, 'B0':'B1'], :]\n foo bar\n A0 B0 0 1\n B1 2 3\n A1 B0 8 9\n B1 10 11", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["SS03", "Summary does not end with a period"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 18, in pandas.IndexSlice\nFailed example:\n dfmi = pd.DataFrame(np.arange(16).reshape((len(midx), len(columns))),\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.IndexSlice[2]>\", line 1\n dfmi = pd.DataFrame(np.arange(16).reshape((len(midx), len(columns))),\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 23, in pandas.IndexSlice\nFailed example:\n dfmi.loc[(slice(None), slice('B0', 'B1')), :]\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.IndexSlice[3]>\", line 1, in <module>\n dfmi.loc[(slice(None), slice('B0', 'B1')), :]\n NameError: name 'dfmi' is not defined\n**********************************************************************\nLine 33, in pandas.IndexSlice\nFailed example:\n dfmi.loc[idx[:, 'B0':'B1'], :]\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.IndexSlice[5]>\", line 1, in <module>\n dfmi.loc[idx[:, 'B0':'B1'], :]\n NameError: name 'dfmi' is not defined\n"], ["EX03", "flake8 error: E231 missing whitespace after ',' (4 times)"], ["EX03", "flake8 error: E902 TokenError: EOF in multi-line statement"], ["EX03", "flake8 error: E999 SyntaxError: invalid syntax"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "**********************************************************************\nLine 18, in pandas.IndexSlice\nFailed example:\n dfmi = pd.DataFrame(np.arange(16).reshape((len(midx), len(columns))),\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.IndexSlice[2]>\", line 1\n dfmi = pd.DataFrame(np.arange(16).reshape((len(midx), len(columns))),\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 23, in pandas.IndexSlice\nFailed example:\n dfmi.loc[(slice(None), slice('B0', 'B1')), :]\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.IndexSlice[3]>\", line 1, in <module>\n dfmi.loc[(slice(None), slice('B0', 'B1')), :]\n NameError: name 'dfmi' is not defined\n**********************************************************************\nLine 33, in pandas.IndexSlice\nFailed example:\n dfmi.loc[idx[:, 'B0':'B1'], :]\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.IndexSlice[5]>\", line 1, in <module>\n dfmi.loc[idx[:, 'B0':'B1'], :]\n NameError: name 'dfmi' is not defined\n", "in_api": true, "section": "MultiIndex", "subsection": "IntervalIndex Components", "shared_code_with": "pandas.IntervalIndex.is_non_overlapping_monotonic"}, "pandas.MultiIndex.from_arrays": {"type": "method", "docstring": "Convert arrays to MultiIndex.\n\nParameters\n----------\narrays : list / sequence of array-likes\n Each array-like gives one level's value for each data point.\n len(arrays) is the number of levels.\nsortorder : int or None\n Level of sortedness (must be lexicographically sorted by that\n level).\nnames : list / sequence of str, optional\n Names for the levels in the index.\n\nReturns\n-------\nindex : MultiIndex\n\nSee Also\n--------\nMultiIndex.from_tuples : Convert list of tuples to MultiIndex.\nMultiIndex.from_product : Make a MultiIndex from cartesian product\n of iterables.\nMultiIndex.from_frame : Make a MultiIndex from a DataFrame.\n\nExamples\n--------\n>>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]\n>>> pd.MultiIndex.from_arrays(arrays, names=('number', 'color'))\nMultiIndex(levels=[[1, 2], ['blue', 'red']],\n codes=[[0, 0, 1, 1], [1, 0, 1, 0]],\n names=['number', 'color'])", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 292, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L292", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Constructors", "shared_code_with": ""}, "pandas.MultiIndex.from_tuples": {"type": "method", "docstring": "Convert list of tuples to MultiIndex.\n\nParameters\n----------\ntuples : list / sequence of tuple-likes\n Each tuple is the index of one row/column.\nsortorder : int or None\n Level of sortedness (must be lexicographically sorted by that\n level).\nnames : list / sequence of str, optional\n Names for the levels in the index.\n\nReturns\n-------\nindex : MultiIndex\n\nSee Also\n--------\nMultiIndex.from_arrays : Convert list of arrays to MultiIndex.\nMultiIndex.from_product : Make a MultiIndex from cartesian product\n of iterables.\nMultiIndex.from_frame : Make a MultiIndex from a DataFrame.\n\nExamples\n--------\n>>> tuples = [(1, u'red'), (1, u'blue'),\n... (2, u'red'), (2, u'blue')]\n>>> pd.MultiIndex.from_tuples(tuples, names=('number', 'color'))\nMultiIndex(levels=[[1, 2], ['blue', 'red']],\n codes=[[0, 0, 1, 1], [1, 0, 1, 0]],\n names=['number', 'color'])", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 347, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L347", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Constructors", "shared_code_with": ""}, "pandas.MultiIndex.from_product": {"type": "method", "docstring": "Make a MultiIndex from the cartesian product of multiple iterables.\n\nParameters\n----------\niterables : list / sequence of iterables\n Each iterable has unique labels for each level of the index.\nsortorder : int or None\n Level of sortedness (must be lexicographically sorted by that\n level).\nnames : list / sequence of str, optional\n Names for the levels in the index.\n\nReturns\n-------\nindex : MultiIndex\n\nSee Also\n--------\nMultiIndex.from_arrays : Convert list of arrays to MultiIndex.\nMultiIndex.from_tuples : Convert list of tuples to MultiIndex.\nMultiIndex.from_frame : Make a MultiIndex from a DataFrame.\n\nExamples\n--------\n>>> numbers = [0, 1, 2]\n>>> colors = ['green', 'purple']\n>>> pd.MultiIndex.from_product([numbers, colors],\n... names=['number', 'color'])\nMultiIndex(levels=[[0, 1, 2], ['green', 'purple']],\n codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],\n names=['number', 'color'])", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 404, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L404", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Constructors", "shared_code_with": ""}, "pandas.MultiIndex.from_frame": {"type": "method", "docstring": "Make a MultiIndex from a DataFrame.\n\n.. versionadded:: 0.24.0\n\nParameters\n----------\ndf : DataFrame\n DataFrame to be converted to MultiIndex.\nsortorder : int, optional\n Level of sortedness (must be lexicographically sorted by that\n level).\nnames : list-like, optional\n If no names are provided, use the column names, or tuple of column\n names if the columns is a MultiIndex. If a sequence, overwrite\n names with the given sequence.\n\nReturns\n-------\nMultiIndex\n The MultiIndex representation of the given DataFrame.\n\nSee Also\n--------\nMultiIndex.from_arrays : Convert list of arrays to MultiIndex.\nMultiIndex.from_tuples : Convert list of tuples to MultiIndex.\nMultiIndex.from_product : Make a MultiIndex from cartesian product\n of iterables.\n\nExamples\n--------\n>>> df = pd.DataFrame([['HI', 'Temp'], ['HI', 'Precip'],\n... ['NJ', 'Temp'], ['NJ', 'Precip']],\n... columns=['a', 'b'])\n>>> df\n a b\n0 HI Temp\n1 HI Precip\n2 NJ Temp\n3 NJ Precip\n\n>>> pd.MultiIndex.from_frame(df)\nMultiIndex(levels=[['HI', 'NJ'], ['Precip', 'Temp']],\n codes=[[0, 0, 1, 1], [1, 0, 1, 0]],\n names=['a', 'b'])\n\nUsing explicit names, instead of the column names\n\n>>> pd.MultiIndex.from_frame(df, names=['state', 'observation'])\nMultiIndex(levels=[['HI', 'NJ'], ['Precip', 'Temp']],\n codes=[[0, 0, 1, 1], [1, 0, 1, 0]],\n names=['state', 'observation'])", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 451, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L451", "errors": [], "warnings": [], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Constructors", "shared_code_with": ""}, "pandas.MultiIndex.names": {"type": "property", "docstring": "Names of levels in MultiIndex", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 1018, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L1018", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Properties", "shared_code_with": ""}, "pandas.MultiIndex.levels": {"type": "property", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 515, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L515", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Properties", "shared_code_with": ""}, "pandas.MultiIndex.codes": {"type": "property", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 661, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L661", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Properties", "shared_code_with": ""}, "pandas.MultiIndex.nlevels": {"type": "property", "docstring": "Integer number of levels in this MultiIndex.", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 1723, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L1723", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Properties", "shared_code_with": ""}, "pandas.MultiIndex.levshape": {"type": "property", "docstring": "A tuple with the length of each level.", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 1728, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L1728", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Properties", "shared_code_with": ""}, "pandas.MultiIndex.set_levels": {"type": "function", "docstring": "Set new levels on MultiIndex. Defaults to returning\nnew index.\n\nParameters\n----------\nlevels : sequence or list of sequence\n new level(s) to apply\nlevel : int, level name, or sequence of int/level names (default None)\n level(s) to set (None for all levels)\ninplace : bool\n if True, mutates in place\nverify_integrity : bool (default True)\n if True, checks that levels and codes are compatible\n\nReturns\n-------\nnew index (of same type and class...etc)\n\nExamples\n--------\n>>> idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\n (2, u'one'), (2, u'two')],\n names=['foo', 'bar'])\n>>> idx.set_levels([['a','b'], [1,2]])\nMultiIndex(levels=[[u'a', u'b'], [1, 2]],\n codes=[[0, 0, 1, 1], [0, 1, 0, 1]],\n names=[u'foo', u'bar'])\n>>> idx.set_levels(['a','b'], level=0)\nMultiIndex(levels=[[u'a', u'b'], [u'one', u'two']],\n codes=[[0, 0, 1, 1], [0, 1, 0, 1]],\n names=[u'foo', u'bar'])\n>>> idx.set_levels(['a','b'], level='bar')\nMultiIndex(levels=[[1, 2], [u'a', u'b']],\n codes=[[0, 0, 1, 1], [0, 1, 0, 1]],\n names=[u'foo', u'bar'])\n>>> idx.set_levels([['a','b'], [1,2]], level=[0,1])\nMultiIndex(levels=[[u'a', u'b'], [1, 2]],\n codes=[[0, 0, 1, 1], [0, 1, 0, 1]],\n names=[u'foo', u'bar'])", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 594, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L594", "errors": [["SS06", "Summary should fit in a single line"], ["PR08", "Parameter \"levels\" description should start with a capital letter"], ["PR09", "Parameter \"levels\" description should finish with \".\""], ["PR08", "Parameter \"level\" description should start with a capital letter"], ["PR09", "Parameter \"level\" description should finish with \".\""], ["PR08", "Parameter \"inplace\" description should start with a capital letter"], ["PR09", "Parameter \"inplace\" description should finish with \".\""], ["PR08", "Parameter \"verify_integrity\" description should start with a capital letter"], ["PR09", "Parameter \"verify_integrity\" description should finish with \".\""], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 22, in pandas.MultiIndex.set_levels\nFailed example:\n idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_levels[0]>\", line 1\n idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 25, in pandas.MultiIndex.set_levels\nFailed example:\n idx.set_levels([['a','b'], [1,2]])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_levels[1]>\", line 1, in <module>\n idx.set_levels([['a','b'], [1,2]])\n NameError: name 'idx' is not defined\n**********************************************************************\nLine 29, in pandas.MultiIndex.set_levels\nFailed example:\n idx.set_levels(['a','b'], level=0)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_levels[2]>\", line 1, in <module>\n idx.set_levels(['a','b'], level=0)\n NameError: name 'idx' is not defined\n**********************************************************************\nLine 33, in pandas.MultiIndex.set_levels\nFailed example:\n idx.set_levels(['a','b'], level='bar')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_levels[3]>\", line 1, in <module>\n idx.set_levels(['a','b'], level='bar')\n NameError: name 'idx' is not defined\n**********************************************************************\nLine 37, in pandas.MultiIndex.set_levels\nFailed example:\n idx.set_levels([['a','b'], [1,2]], level=[0,1])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_levels[4]>\", line 1, in <module>\n idx.set_levels([['a','b'], [1,2]], level=[0,1])\n NameError: name 'idx' is not defined\n"], ["EX03", "flake8 error: E902 TokenError: EOF in multi-line statement"], ["EX03", "flake8 error: E999 SyntaxError: invalid syntax"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 22, in pandas.MultiIndex.set_levels\nFailed example:\n idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_levels[0]>\", line 1\n idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 25, in pandas.MultiIndex.set_levels\nFailed example:\n idx.set_levels([['a','b'], [1,2]])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_levels[1]>\", line 1, in <module>\n idx.set_levels([['a','b'], [1,2]])\n NameError: name 'idx' is not defined\n**********************************************************************\nLine 29, in pandas.MultiIndex.set_levels\nFailed example:\n idx.set_levels(['a','b'], level=0)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_levels[2]>\", line 1, in <module>\n idx.set_levels(['a','b'], level=0)\n NameError: name 'idx' is not defined\n**********************************************************************\nLine 33, in pandas.MultiIndex.set_levels\nFailed example:\n idx.set_levels(['a','b'], level='bar')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_levels[3]>\", line 1, in <module>\n idx.set_levels(['a','b'], level='bar')\n NameError: name 'idx' is not defined\n**********************************************************************\nLine 37, in pandas.MultiIndex.set_levels\nFailed example:\n idx.set_levels([['a','b'], [1,2]], level=[0,1])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_levels[4]>\", line 1, in <module>\n idx.set_levels([['a','b'], [1,2]], level=[0,1])\n NameError: name 'idx' is not defined\n", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Components", "shared_code_with": ""}, "pandas.MultiIndex.set_codes": {"type": "function", "docstring": "Set new codes on MultiIndex. Defaults to returning\nnew index.\n\n.. versionadded:: 0.24.0\n\n New name for deprecated method `set_labels`.\n\nParameters\n----------\ncodes : sequence or list of sequence\n new codes to apply\nlevel : int, level name, or sequence of int/level names (default None)\n level(s) to set (None for all levels)\ninplace : bool\n if True, mutates in place\nverify_integrity : bool (default True)\n if True, checks that levels and codes are compatible\n\nReturns\n-------\nnew index (of same type and class...etc)\n\nExamples\n--------\n>>> idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\n (2, u'one'), (2, u'two')],\n names=['foo', 'bar'])\n>>> idx.set_codes([[1,0,1,0], [0,0,1,1]])\nMultiIndex(levels=[[1, 2], [u'one', u'two']],\n codes=[[1, 0, 1, 0], [0, 0, 1, 1]],\n names=[u'foo', u'bar'])\n>>> idx.set_codes([1,0,1,0], level=0)\nMultiIndex(levels=[[1, 2], [u'one', u'two']],\n codes=[[1, 0, 1, 0], [0, 1, 0, 1]],\n names=[u'foo', u'bar'])\n>>> idx.set_codes([0,0,1,1], level='bar')\nMultiIndex(levels=[[1, 2], [u'one', u'two']],\n codes=[[0, 0, 1, 1], [0, 0, 1, 1]],\n names=[u'foo', u'bar'])\n>>> idx.set_codes([[1,0,1,0], [0,0,1,1]], level=[0,1])\nMultiIndex(levels=[[1, 2], [u'one', u'two']],\n codes=[[1, 0, 1, 0], [0, 0, 1, 1]],\n names=[u'foo', u'bar'])", "deprecated": false, "file": "pandas/util/_decorators.py", "file_line": 708, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/util/_decorators.py#L708", "errors": [["SS06", "Summary should fit in a single line"], ["PR08", "Parameter \"codes\" description should start with a capital letter"], ["PR09", "Parameter \"codes\" description should finish with \".\""], ["PR08", "Parameter \"level\" description should start with a capital letter"], ["PR09", "Parameter \"level\" description should finish with \".\""], ["PR08", "Parameter \"inplace\" description should start with a capital letter"], ["PR09", "Parameter \"inplace\" description should finish with \".\""], ["PR08", "Parameter \"verify_integrity\" description should start with a capital letter"], ["PR09", "Parameter \"verify_integrity\" description should finish with \".\""], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 26, in pandas.MultiIndex.set_codes\nFailed example:\n idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_codes[0]>\", line 1\n idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 29, in pandas.MultiIndex.set_codes\nFailed example:\n idx.set_codes([[1,0,1,0], [0,0,1,1]])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_codes[1]>\", line 1, in <module>\n idx.set_codes([[1,0,1,0], [0,0,1,1]])\n NameError: name 'idx' is not defined\n**********************************************************************\nLine 33, in pandas.MultiIndex.set_codes\nFailed example:\n idx.set_codes([1,0,1,0], level=0)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_codes[2]>\", line 1, in <module>\n idx.set_codes([1,0,1,0], level=0)\n NameError: name 'idx' is not defined\n**********************************************************************\nLine 37, in pandas.MultiIndex.set_codes\nFailed example:\n idx.set_codes([0,0,1,1], level='bar')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_codes[3]>\", line 1, in <module>\n idx.set_codes([0,0,1,1], level='bar')\n NameError: name 'idx' is not defined\n**********************************************************************\nLine 41, in pandas.MultiIndex.set_codes\nFailed example:\n idx.set_codes([[1,0,1,0], [0,0,1,1]], level=[0,1])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_codes[4]>\", line 1, in <module>\n idx.set_codes([[1,0,1,0], [0,0,1,1]], level=[0,1])\n NameError: name 'idx' is not defined\n"], ["EX03", "flake8 error: E902 TokenError: EOF in multi-line statement"], ["EX03", "flake8 error: E999 SyntaxError: invalid syntax"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 26, in pandas.MultiIndex.set_codes\nFailed example:\n idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_codes[0]>\", line 1\n idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 29, in pandas.MultiIndex.set_codes\nFailed example:\n idx.set_codes([[1,0,1,0], [0,0,1,1]])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_codes[1]>\", line 1, in <module>\n idx.set_codes([[1,0,1,0], [0,0,1,1]])\n NameError: name 'idx' is not defined\n**********************************************************************\nLine 33, in pandas.MultiIndex.set_codes\nFailed example:\n idx.set_codes([1,0,1,0], level=0)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_codes[2]>\", line 1, in <module>\n idx.set_codes([1,0,1,0], level=0)\n NameError: name 'idx' is not defined\n**********************************************************************\nLine 37, in pandas.MultiIndex.set_codes\nFailed example:\n idx.set_codes([0,0,1,1], level='bar')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_codes[3]>\", line 1, in <module>\n idx.set_codes([0,0,1,1], level='bar')\n NameError: name 'idx' is not defined\n**********************************************************************\nLine 41, in pandas.MultiIndex.set_codes\nFailed example:\n idx.set_codes([[1,0,1,0], [0,0,1,1]], level=[0,1])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.set_codes[4]>\", line 1, in <module>\n idx.set_codes([[1,0,1,0], [0,0,1,1]], level=[0,1])\n NameError: name 'idx' is not defined\n", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Components", "shared_code_with": ""}, "pandas.MultiIndex.to_hierarchical": {"type": "function", "docstring": "Return a MultiIndex reshaped to conform to the\nshapes given by n_repeat and n_shuffle.\n\n.. deprecated:: 0.24.0\n\nUseful to replicate and rearrange a MultiIndex for combination\nwith another Index with n_repeat items.\n\nParameters\n----------\nn_repeat : int\n Number of times to repeat the labels on self\nn_shuffle : int\n Controls the reordering of the labels. If the result is going\n to be an inner level in a MultiIndex, n_shuffle will need to be\n greater than one. The size of each label must divisible by\n n_shuffle.\n\nReturns\n-------\nMultiIndex\n\nExamples\n--------\n>>> idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\n (2, u'one'), (2, u'two')])\n>>> idx.to_hierarchical(3)\nMultiIndex(levels=[[1, 2], [u'one', u'two']],\n codes=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1],\n [0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1]])", "deprecated": true, "file": "pandas/core/indexes/multi.py", "file_line": 1482, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L1482", "errors": [["SS06", "Summary should fit in a single line"], ["PR09", "Parameter \"n_repeat\" description should finish with \".\""], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 26, in pandas.MultiIndex.to_hierarchical\nFailed example:\n idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.to_hierarchical[0]>\", line 1\n idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 28, in pandas.MultiIndex.to_hierarchical\nFailed example:\n idx.to_hierarchical(3)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.to_hierarchical[1]>\", line 1, in <module>\n idx.to_hierarchical(3)\n NameError: name 'idx' is not defined\n"], ["EX03", "flake8 error: E902 TokenError: EOF in multi-line statement"], ["EX03", "flake8 error: E999 SyntaxError: unexpected EOF while parsing"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 26, in pandas.MultiIndex.to_hierarchical\nFailed example:\n idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.to_hierarchical[0]>\", line 1\n idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 28, in pandas.MultiIndex.to_hierarchical\nFailed example:\n idx.to_hierarchical(3)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.MultiIndex.to_hierarchical[1]>\", line 1, in <module>\n idx.to_hierarchical(3)\n NameError: name 'idx' is not defined\n", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Components", "shared_code_with": ""}, "pandas.MultiIndex.to_flat_index": {"type": "function", "docstring": "Convert a MultiIndex to an Index of Tuples containing the level values.\n\n.. versionadded:: 0.24.0\n\nReturns\n-------\npd.Index\n Index with the MultiIndex data represented in Tuples.\n\nNotes\n-----\nThis method will simply return the caller if called by anything other\nthan a MultiIndex.\n\nExamples\n--------\n>>> index = pd.MultiIndex.from_product(\n... [['foo', 'bar'], ['baz', 'qux']],\n... names=['a', 'b'])\n>>> index.to_flat_index()\nIndex([('foo', 'baz'), ('foo', 'qux'),\n ('bar', 'baz'), ('bar', 'qux')],\n dtype='object')", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 1526, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L1526", "errors": [], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Components", "shared_code_with": ""}, "pandas.MultiIndex.to_frame": {"type": "function", "docstring": "Create a DataFrame with the levels of the MultiIndex as columns.\n\nColumn ordering is determined by the DataFrame constructor with data as\na dict.\n\n.. versionadded:: 0.24.0\n\nParameters\n----------\nindex : boolean, default True\n Set the index of the returned DataFrame as the original MultiIndex.\n\nname : list / sequence of strings, optional\n The passed names should substitute index level names.\n\nReturns\n-------\nDataFrame : a DataFrame containing the original MultiIndex data.\n\nSee Also\n--------\nDataFrame", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 1429, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L1429", "errors": [["PR06", "Parameter \"index\" type should use \"bool\" instead of \"boolean\""], ["PR06", "Parameter \"name\" type should use \"str\" instead of \"string\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["SA04", "Missing description for See Also \"DataFrame\" reference"]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Components", "shared_code_with": ""}, "pandas.MultiIndex.is_lexsorted": {"type": "function", "docstring": "Return True if the codes are lexicographically sorted", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 1558, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L1558", "errors": [["SS03", "Summary does not end with a period"], ["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Components", "shared_code_with": ""}, "pandas.MultiIndex.sortlevel": {"type": "function", "docstring": "Sort MultiIndex at the requested level. The result will respect the\noriginal ordering of the associated factor at that level.\n\nParameters\n----------\nlevel : list-like, int or str, default 0\n If a string is given, must be a name of the level\n If list-like must be names or ints of levels.\nascending : boolean, default True\n False to sort in descending order\n Can also be a list to specify a directed ordering\nsort_remaining : sort by the remaining levels after level\n\nReturns\n-------\nsorted_index : pd.MultiIndex\n Resulting index\nindexer : np.ndarray\n Indices of output values in original index", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 2038, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L2038", "errors": [["SS06", "Summary should fit in a single line"], ["PR06", "Parameter \"ascending\" type should use \"bool\" instead of \"boolean\""], ["PR09", "Parameter \"ascending\" description should finish with \".\""], ["PR07", "Parameter \"sort_remaining\" has no description"], ["RT05", "Return value description should finish with \".\""], ["RT05", "Return value description should finish with \".\""]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Components", "shared_code_with": ""}, "pandas.MultiIndex.droplevel": {"type": "function", "docstring": "Return index with requested level(s) removed.\n\nIf resulting index has only 1 level left, the result will be\nof Index type, not MultiIndex.\n\n.. versionadded:: 0.23.1 (support for non-MultiIndex)\n\nParameters\n----------\nlevel : int, str, or list-like, default 0\n If a string is given, must be the name of a level\n If list-like, elements must be names or indexes of levels.\n\nReturns\n-------\nindex : Index or MultiIndex", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1490, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1490", "errors": [["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Components", "shared_code_with": "pandas.Index.droplevel"}, "pandas.MultiIndex.swaplevel": {"type": "function", "docstring": "Swap level i with level j.\n\nCalling this method does not change the ordering of the values.\n\nParameters\n----------\ni : int, str, default -2\n First level of index to be swapped. Can pass level name as string.\n Type of parameters can be mixed.\nj : int, str, default -1\n Second level of index to be swapped. Can pass level name as string.\n Type of parameters can be mixed.\n\nReturns\n-------\nMultiIndex\n A new MultiIndex\n\n.. versionchanged:: 0.18.1\n\n The indexes ``i`` and ``j`` are now optional, and default to\n the two innermost levels of the index.\n\nSee Also\n--------\nSeries.swaplevel : Swap levels i and j in a MultiIndex.\nDataframe.swaplevel : Swap levels i and j in a MultiIndex on a\n particular axis.\n\nExamples\n--------\n>>> mi = pd.MultiIndex(levels=[['a', 'b'], ['bb', 'aa']],\n... codes=[[0, 0, 1, 1], [0, 1, 0, 1]])\n>>> mi\nMultiIndex(levels=[['a', 'b'], ['bb', 'aa']],\n codes=[[0, 0, 1, 1], [0, 1, 0, 1]])\n>>> mi.swaplevel(0, 1)\nMultiIndex(levels=[['bb', 'aa'], ['a', 'b']],\n codes=[[0, 1, 0, 1], [0, 0, 1, 1]])", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 1941, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L1941", "errors": [["RT05", "Return value description should finish with \".\""]], "warnings": [], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Components", "shared_code_with": ""}, "pandas.MultiIndex.reorder_levels": {"type": "function", "docstring": "Rearrange levels using input order. May not drop or duplicate levels\n\nParameters\n----------", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 1997, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L1997", "errors": [["SS03", "Summary does not end with a period"], ["PR01", "Parameters {order} not documented"], ["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Components", "shared_code_with": ""}, "pandas.MultiIndex.remove_unused_levels": {"type": "function", "docstring": "Create a new MultiIndex from the current that removes\nunused levels, meaning that they are not expressed in the labels.\n\nThe resulting MultiIndex will have the same outward\nappearance, meaning the same .values and ordering. It will also\nbe .equals() to the original.\n\n.. versionadded:: 0.20.0\n\nReturns\n-------\nMultiIndex\n\nExamples\n--------\n>>> i = pd.MultiIndex.from_product([range(2), list('ab')])\nMultiIndex(levels=[[0, 1], ['a', 'b']],\n codes=[[0, 0, 1, 1], [0, 1, 0, 1]])\n\n>>> i[2:]\nMultiIndex(levels=[[0, 1], ['a', 'b']],\n codes=[[1, 1], [0, 1]])\n\nThe 0 from the first level is not represented\nand can be removed\n\n>>> i[2:].remove_unused_levels()\nMultiIndex(levels=[[1], ['a', 'b']],\n codes=[[0, 0], [0, 1]])", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 1641, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L1641", "errors": [["SS06", "Summary should fit in a single line"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 17, in pandas.MultiIndex.remove_unused_levels\nFailed example:\n i = pd.MultiIndex.from_product([range(2), list('ab')])\nExpected:\n MultiIndex(levels=[[0, 1], ['a', 'b']],\n codes=[[0, 0, 1, 1], [0, 1, 0, 1]])\nGot nothing\n"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 17, in pandas.MultiIndex.remove_unused_levels\nFailed example:\n i = pd.MultiIndex.from_product([range(2), list('ab')])\nExpected:\n MultiIndex(levels=[[0, 1], ['a', 'b']],\n codes=[[0, 0, 1, 1], [0, 1, 0, 1]])\nGot nothing\n", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Components", "shared_code_with": ""}, "pandas.MultiIndex.get_loc": {"type": "function", "docstring": "Get location for a label or a tuple of labels as an integer, slice or\nboolean mask.\n\nParameters\n----------\nkey : label or tuple of labels (one for each level)\nmethod : None\n\nReturns\n-------\nloc : int, slice object or boolean mask\n If the key is past the lexsort depth, the return may be a\n boolean mask array, otherwise it is always a slice or int.\n\nExamples\n---------\n>>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')])\n\n>>> mi.get_loc('b')\nslice(1, 3, None)\n\n>>> mi.get_loc(('b', 'e'))\n1\n\nNotes\n------\nThe key cannot be a slice, list of same-level labels, a boolean mask,\nor a sequence of such. If you want to use those, use\n:meth:`MultiIndex.get_locs` instead.\n\nSee Also\n--------\nIndex.get_loc : The get_loc method for (single-level) index.\nMultiIndex.slice_locs : Get slice location given start label(s) and\n end label(s).\nMultiIndex.get_locs : Get location for a label/slice/list/mask or a\n sequence of such.", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 2338, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L2338", "errors": [["SS06", "Summary should fit in a single line"], ["PR07", "Parameter \"key\" has no description"], ["PR07", "Parameter \"method\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Selecting", "shared_code_with": ""}, "pandas.MultiIndex.get_loc_level": {"type": "function", "docstring": "Get both the location for the requested label(s) and the\nresulting sliced index.\n\nParameters\n----------\nkey : label or sequence of labels\nlevel : int/level name or list thereof, optional\ndrop_level : bool, default True\n if ``False``, the resulting index will not drop any level.\n\nReturns\n-------\nloc : A 2-tuple where the elements are:\n Element 0: int, slice object or boolean array\n Element 1: The resulting sliced multiindex/index. If the key\n contains all levels, this will be ``None``.\n\nExamples\n--------\n>>> mi = pd.MultiIndex.from_arrays([list('abb'), list('def')],\n... names=['A', 'B'])\n\n>>> mi.get_loc_level('b')\n(slice(1, 3, None), Index(['e', 'f'], dtype='object', name='B'))\n\n>>> mi.get_loc_level('e', level='B')\n(array([False, True, False], dtype=bool),\nIndex(['b'], dtype='object', name='A'))\n\n>>> mi.get_loc_level(['b', 'e'])\n(1, None)\n\nSee Also\n---------\nMultiIndex.get_loc : Get location for a label or a tuple of labels.\nMultiIndex.get_locs : Get location for a label/slice/list/mask or a\n sequence of such.", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 2438, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L2438", "errors": [["SS06", "Summary should fit in a single line"], ["PR07", "Parameter \"key\" has no description"], ["PR07", "Parameter \"level\" has no description"], ["PR08", "Parameter \"drop_level\" description should start with a capital letter"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 27, in pandas.MultiIndex.get_loc_level\nFailed example:\n mi.get_loc_level('e', level='B')\nExpected:\n (array([False, True, False], dtype=bool),\n Index(['b'], dtype='object', name='A'))\nGot:\n (array([False, True, False]), Index(['b'], dtype='object', name='A'))\n"]], "warnings": [], "examples_errors": "**********************************************************************\nLine 27, in pandas.MultiIndex.get_loc_level\nFailed example:\n mi.get_loc_level('e', level='B')\nExpected:\n (array([False, True, False], dtype=bool),\n Index(['b'], dtype='object', name='A'))\nGot:\n (array([False, True, False]), Index(['b'], dtype='object', name='A'))\n", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Selecting", "shared_code_with": ""}, "pandas.MultiIndex.get_indexer": {"type": "function", "docstring": "Compute indexer and mask for new index given the current index. The\nindexer should be then used as an input to ndarray.take to align the\ncurrent data to the new index.\n\nParameters\n----------\ntarget : MultiIndex or list of tuples\nmethod : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional\n * default: exact matches only.\n * pad / ffill: find the PREVIOUS index value if no exact match.\n * backfill / bfill: use NEXT index value if no exact match\n * nearest: use the NEAREST index value if no exact match. Tied\n distances are broken by preferring the larger index value.\nlimit : int, optional\n Maximum number of consecutive labels in ``target`` to match for\n inexact matches.\ntolerance : optional\n Maximum distance between original and new labels for inexact\n matches. The values of the index at the matching locations most\n satisfy the equation ``abs(index[indexer] - target) <= tolerance``.\n\n Tolerance may be a scalar value, which applies the same tolerance\n to all values, or list-like, which applies variable tolerance per\n element. List-like includes list, tuple, array, Series, and must be\n the same size as the index and its dtype must exactly match the\n index's type.\n\n .. versionadded:: 0.21.0 (list-like tolerance)\n\nReturns\n-------\nindexer : ndarray of int\n Integers from 0 to n - 1 indicating that the index at these\n positions matches the corresponding target values. Missing values\n in the target are marked by -1.\n\nExamples\n--------\n>>> index = pd.Index(['c', 'a', 'b'])\n>>> index.get_indexer(['a', 'b', 'x'])\narray([ 1, 2, -1])\n\nNotice that the return value is an array of locations in ``index``\nand ``x`` is marked by -1, as it is not in ``index``.", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 2140, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L2140", "errors": [["SS06", "Summary should fit in a single line"], ["PR07", "Parameter \"target\" has no description"], ["PR08", "Parameter \"method\" description should start with a capital letter"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Selecting", "shared_code_with": ""}, "pandas.MultiIndex.get_level_values": {"type": "function", "docstring": "Return vector of label values for requested level,\nequal to the length of the index.\n\nParameters\n----------\nlevel : int or str\n ``level`` is either the integer position of the level in the\n MultiIndex, or the name of the level.\n\nReturns\n-------\nvalues : Index\n ``values`` is a level of this MultiIndex converted to\n a single :class:`Index` (or subclass thereof).\n\nExamples\n---------\n\nCreate a MultiIndex:\n\n>>> mi = pd.MultiIndex.from_arrays((list('abc'), list('def')))\n>>> mi.names = ['level_1', 'level_2']\n\nGet level values by supplying level as either integer or name:\n\n>>> mi.get_level_values(0)\nIndex(['a', 'b', 'c'], dtype='object', name='level_1')\n>>> mi.get_level_values('level_2')\nIndex(['d', 'e', 'f'], dtype='object', name='level_2')", "deprecated": false, "file": "pandas/core/indexes/multi.py", "file_line": 1380, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/multi.py#L1380", "errors": [["SS06", "Summary should fit in a single line"], ["PR08", "Parameter \"level\" description should start with a capital letter"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT04", "Return value description should start with a capital letter"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "MultiIndex", "subsection": "MultiIndex Selecting", "shared_code_with": ""}, "pandas.DatetimeIndex": {"type": "type", "docstring": "Immutable ndarray of datetime64 data, represented internally as int64, and\nwhich can be boxed to Timestamp objects that are subclasses of datetime and\ncarry metadata such as frequency information.\n\nParameters\n----------\ndata : array-like (1-dimensional), optional\n Optional datetime-like data to construct index with\ncopy : bool\n Make a copy of input ndarray\nfreq : string or pandas offset object, optional\n One of pandas date offset strings or corresponding objects. The string\n 'infer' can be passed in order to set the frequency of the index as the\n inferred frequency upon creation\n\nstart : starting value, datetime-like, optional\n If data is None, start is used as the start point in generating regular\n timestamp data.\n\n .. deprecated:: 0.24.0\n\nperiods : int, optional, > 0\n Number of periods to generate, if generating index. Takes precedence\n over end argument\n\n .. deprecated:: 0.24.0\n\nend : end time, datetime-like, optional\n If periods is none, generated index will extend to first conforming\n time on or just past end argument\n\n .. deprecated:: 0.24.0\n\nclosed : string or None, default None\n Make the interval closed with respect to the given frequency to\n the 'left', 'right', or both sides (None)\n\n .. deprecated:: 0.24. 0\n\ntz : pytz.timezone or dateutil.tz.tzfile\nambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'\n When clocks moved backward due to DST, ambiguous times may arise.\n For example in Central European Time (UTC+01), when going from 03:00\n DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC\n and at 01:30:00 UTC. In such a situation, the `ambiguous` parameter\n dictates how ambiguous times should be handled.\n\n - 'infer' will attempt to infer fall dst-transition hours based on\n order\n - bool-ndarray where True signifies a DST time, False signifies a\n non-DST time (note that this flag is only applicable for ambiguous\n times)\n - 'NaT' will return NaT where there are ambiguous times\n - 'raise' will raise an AmbiguousTimeError if there are ambiguous times\nname : object\n Name to be stored in the index\ndayfirst : bool, default False\n If True, parse dates in `data` with the day first order\nyearfirst : bool, default False\n If True parse dates in `data` with the year first order\n\nAttributes\n----------\nyear\nmonth\nday\nhour\nminute\nsecond\nmicrosecond\nnanosecond\ndate\ntime\ntimetz\ndayofyear\nweekofyear\nweek\ndayofweek\nweekday\nquarter\ntz\nfreq\nfreqstr\nis_month_start\nis_month_end\nis_quarter_start\nis_quarter_end\nis_year_start\nis_year_end\nis_leap_year\ninferred_freq\n\nMethods\n-------\nnormalize\nstrftime\nsnap\ntz_convert\ntz_localize\nround\nfloor\nceil\nto_period\nto_perioddelta\nto_pydatetime\nto_series\nto_frame\nmonth_name\nday_name\n\nNotes\n-----\nTo learn more about the frequency strings, please see `this link\n<http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.\n\nCreating a DatetimeIndex based on `start`, `periods`, and `end` has\nbeen deprecated in favor of :func:`date_range`.\n\nSee Also\n---------\nIndex : The base pandas Index type.\nTimedeltaIndex : Index of timedelta64 data.\nPeriodIndex : Index of Period data.\nto_datetime : Convert argument to datetime.\ndate_range : Create a fixed-frequency DatetimeIndex.", "deprecated": false, "file": "pandas/core/indexes/datetimes.py", "file_line": 100, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/datetimes.py#L100", "errors": [["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {verify_integrity, data, copy, normalize, dtype, periods} not documented"], ["PR02", "Unknown parameters {periods , copy , data }"], ["PR09", "Parameter \"data \" description should finish with \".\""], ["PR09", "Parameter \"copy \" description should finish with \".\""], ["PR06", "Parameter \"freq\" type should use \"str\" instead of \"string\""], ["PR09", "Parameter \"freq\" description should finish with \".\""], ["PR09", "Parameter \"periods \" description should finish with \".\""], ["PR09", "Parameter \"end\" description should finish with \".\""], ["PR06", "Parameter \"closed\" type should use \"str\" instead of \"string\""], ["PR09", "Parameter \"closed\" description should finish with \".\""], ["PR07", "Parameter \"tz\" has no description"], ["PR09", "Parameter \"ambiguous\" description should finish with \".\""], ["PR09", "Parameter \"name\" description should finish with \".\""], ["PR09", "Parameter \"dayfirst\" description should finish with \".\""], ["PR09", "Parameter \"yearfirst\" description should finish with \".\""]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "MultiIndex Selecting", "shared_code_with": ""}, "pandas.DatetimeIndex.year": {"type": "property", "docstring": "The year of the datetime.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": ""}, "pandas.DatetimeIndex.month": {"type": "property", "docstring": "The month as January=1, December=12.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.year"}, "pandas.DatetimeIndex.day": {"type": "property", "docstring": "The days of the datetime.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.month"}, "pandas.DatetimeIndex.hour": {"type": "property", "docstring": "The hours of the datetime.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.day"}, "pandas.DatetimeIndex.minute": {"type": "property", "docstring": "The minutes of the datetime.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.hour"}, "pandas.DatetimeIndex.second": {"type": "property", "docstring": "The seconds of the datetime.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.minute"}, "pandas.DatetimeIndex.microsecond": {"type": "property", "docstring": "The microseconds of the datetime.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.second"}, "pandas.DatetimeIndex.nanosecond": {"type": "property", "docstring": "The nanoseconds of the datetime.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.microsecond"}, "pandas.DatetimeIndex.date": {"type": "property", "docstring": "Returns numpy array of python datetime.date objects (namely, the date\npart of Timestamps without timezone information).", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.nanosecond"}, "pandas.DatetimeIndex.time": {"type": "property", "docstring": "Returns numpy array of datetime.time. The time part of the Timestamps.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.date"}, "pandas.DatetimeIndex.timetz": {"type": "property", "docstring": "Returns numpy array of datetime.time also containing timezone\ninformation. The time part of the Timestamps.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.time"}, "pandas.DatetimeIndex.dayofyear": {"type": "property", "docstring": "The ordinal day of the year.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.timetz"}, "pandas.DatetimeIndex.weekofyear": {"type": "property", "docstring": "The week ordinal of the year.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.dayofyear"}, "pandas.DatetimeIndex.week": {"type": "property", "docstring": "The week ordinal of the year.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.weekofyear"}, "pandas.DatetimeIndex.dayofweek": {"type": "property", "docstring": "The day of the week with Monday=0, Sunday=6.\n\nReturn the day of the week. It is assumed the week starts on\nMonday, which is denoted by 0 and ends on Sunday which is denoted\nby 6. This method is available on both Series with datetime\nvalues (using the `dt` accessor) or DatetimeIndex.\n\nReturns\n-------\nSeries or Index\n Containing integers indicating the day number.\n\nSee Also\n--------\nSeries.dt.dayofweek : Alias.\nSeries.dt.weekday : Alias.\nSeries.dt.day_name : Returns the name of the day of the week.\n\nExamples\n--------\n>>> s = pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series()\n>>> s.dt.dayofweek\n2016-12-31 5\n2017-01-01 6\n2017-01-02 0\n2017-01-03 1\n2017-01-04 2\n2017-01-05 3\n2017-01-06 4\n2017-01-07 5\n2017-01-08 6\nFreq: D, dtype: int64", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.week"}, "pandas.DatetimeIndex.weekday": {"type": "property", "docstring": "The day of the week with Monday=0, Sunday=6.\n\nReturn the day of the week. It is assumed the week starts on\nMonday, which is denoted by 0 and ends on Sunday which is denoted\nby 6. This method is available on both Series with datetime\nvalues (using the `dt` accessor) or DatetimeIndex.\n\nReturns\n-------\nSeries or Index\n Containing integers indicating the day number.\n\nSee Also\n--------\nSeries.dt.dayofweek : Alias.\nSeries.dt.weekday : Alias.\nSeries.dt.day_name : Returns the name of the day of the week.\n\nExamples\n--------\n>>> s = pd.date_range('2016-12-31', '2017-01-08', freq='D').to_series()\n>>> s.dt.dayofweek\n2016-12-31 5\n2017-01-01 6\n2017-01-02 0\n2017-01-03 1\n2017-01-04 2\n2017-01-05 3\n2017-01-06 4\n2017-01-07 5\n2017-01-08 6\nFreq: D, dtype: int64", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.dayofweek"}, "pandas.DatetimeIndex.quarter": {"type": "property", "docstring": "The quarter of the date.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.weekday"}, "pandas.DatetimeIndex.tz": {"type": "property", "docstring": "", "deprecated": false, "file": "pandas/core/indexes/datetimes.py", "file_line": 364, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/datetimes.py#L364", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": ""}, "pandas.DatetimeIndex.freq": {"type": "property", "docstring": "Return the frequency object if it is set, otherwise None.", "deprecated": false, "file": "pandas/core/indexes/datetimelike.py", "file_line": 76, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/datetimelike.py#L76", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": ""}, "pandas.DatetimeIndex.freqstr": {"type": "property", "docstring": "Return the frequency object as a string if it is set, otherwise None.", "deprecated": false, "file": "pandas/core/indexes/datetimelike.py", "file_line": 88, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/datetimelike.py#L88", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": ""}, "pandas.DatetimeIndex.is_month_start": {"type": "property", "docstring": "Indicates whether the date is the first day of the month.\n\nReturns\n-------\nSeries or array\n For Series, returns a Series with boolean values.\n For DatetimeIndex, returns a boolean array.\n\nSee Also\n--------\nis_month_start : Return a boolean indicating whether the date\n is the first day of the month.\nis_month_end : Return a boolean indicating whether the date\n is the last day of the month.\n\nExamples\n--------\nThis method is available on Series with datetime values under\nthe ``.dt`` accessor, and directly on DatetimeIndex.\n\n>>> s = pd.Series(pd.date_range(\"2018-02-27\", periods=3))\n>>> s\n0 2018-02-27\n1 2018-02-28\n2 2018-03-01\ndtype: datetime64[ns]\n>>> s.dt.is_month_start\n0 False\n1 False\n2 True\ndtype: bool\n>>> s.dt.is_month_end\n0 False\n1 True\n2 False\ndtype: bool\n\n>>> idx = pd.date_range(\"2018-02-27\", periods=3)\n>>> idx.is_month_start\narray([False, False, True])\n>>> idx.is_month_end\narray([False, True, False])", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.quarter"}, "pandas.DatetimeIndex.is_month_end": {"type": "property", "docstring": "Indicates whether the date is the last day of the month.\n\nReturns\n-------\nSeries or array\n For Series, returns a Series with boolean values.\n For DatetimeIndex, returns a boolean array.\n\nSee Also\n--------\nis_month_start : Return a boolean indicating whether the date\n is the first day of the month.\nis_month_end : Return a boolean indicating whether the date\n is the last day of the month.\n\nExamples\n--------\nThis method is available on Series with datetime values under\nthe ``.dt`` accessor, and directly on DatetimeIndex.\n\n>>> s = pd.Series(pd.date_range(\"2018-02-27\", periods=3))\n>>> s\n0 2018-02-27\n1 2018-02-28\n2 2018-03-01\ndtype: datetime64[ns]\n>>> s.dt.is_month_start\n0 False\n1 False\n2 True\ndtype: bool\n>>> s.dt.is_month_end\n0 False\n1 True\n2 False\ndtype: bool\n\n>>> idx = pd.date_range(\"2018-02-27\", periods=3)\n>>> idx.is_month_start\narray([False, False, True])\n>>> idx.is_month_end\narray([False, True, False])", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.is_month_start"}, "pandas.DatetimeIndex.is_quarter_start": {"type": "property", "docstring": "Indicator for whether the date is the first day of a quarter.\n\nReturns\n-------\nis_quarter_start : Series or DatetimeIndex\n The same type as the original data with boolean values. Series will\n have the same name and index. DatetimeIndex will have the same\n name.\n\nSee Also\n--------\nquarter : Return the quarter of the date.\nis_quarter_end : Similar property for indicating the quarter start.\n\nExamples\n--------\nThis method is available on Series with datetime values under\nthe ``.dt`` accessor, and directly on DatetimeIndex.\n\n>>> df = pd.DataFrame({'dates': pd.date_range(\"2017-03-30\",\n... periods=4)})\n>>> df.assign(quarter=df.dates.dt.quarter,\n... is_quarter_start=df.dates.dt.is_quarter_start)\n dates quarter is_quarter_start\n0 2017-03-30 1 False\n1 2017-03-31 1 False\n2 2017-04-01 2 True\n3 2017-04-02 2 False\n\n>>> idx = pd.date_range('2017-03-30', periods=4)\n>>> idx\nDatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'],\n dtype='datetime64[ns]', freq='D')\n\n>>> idx.is_quarter_start\narray([False, False, True, False])", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.is_month_end"}, "pandas.DatetimeIndex.is_quarter_end": {"type": "property", "docstring": "Indicator for whether the date is the last day of a quarter.\n\nReturns\n-------\nis_quarter_end : Series or DatetimeIndex\n The same type as the original data with boolean values. Series will\n have the same name and index. DatetimeIndex will have the same\n name.\n\nSee Also\n--------\nquarter : Return the quarter of the date.\nis_quarter_start : Similar property indicating the quarter start.\n\nExamples\n--------\nThis method is available on Series with datetime values under\nthe ``.dt`` accessor, and directly on DatetimeIndex.\n\n>>> df = pd.DataFrame({'dates': pd.date_range(\"2017-03-30\",\n... periods=4)})\n>>> df.assign(quarter=df.dates.dt.quarter,\n... is_quarter_end=df.dates.dt.is_quarter_end)\n dates quarter is_quarter_end\n0 2017-03-30 1 False\n1 2017-03-31 1 True\n2 2017-04-01 2 False\n3 2017-04-02 2 False\n\n>>> idx = pd.date_range('2017-03-30', periods=4)\n>>> idx\nDatetimeIndex(['2017-03-30', '2017-03-31', '2017-04-01', '2017-04-02'],\n dtype='datetime64[ns]', freq='D')\n\n>>> idx.is_quarter_end\narray([False, True, False, False])", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.is_quarter_start"}, "pandas.DatetimeIndex.is_year_start": {"type": "property", "docstring": "Indicate whether the date is the first day of a year.\n\nReturns\n-------\nSeries or DatetimeIndex\n The same type as the original data with boolean values. Series will\n have the same name and index. DatetimeIndex will have the same\n name.\n\nSee Also\n--------\nis_year_end : Similar property indicating the last day of the year.\n\nExamples\n--------\nThis method is available on Series with datetime values under\nthe ``.dt`` accessor, and directly on DatetimeIndex.\n\n>>> dates = pd.Series(pd.date_range(\"2017-12-30\", periods=3))\n>>> dates\n0 2017-12-30\n1 2017-12-31\n2 2018-01-01\ndtype: datetime64[ns]\n\n>>> dates.dt.is_year_start\n0 False\n1 False\n2 True\ndtype: bool\n\n>>> idx = pd.date_range(\"2017-12-30\", periods=3)\n>>> idx\nDatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'],\n dtype='datetime64[ns]', freq='D')\n\n>>> idx.is_year_start\narray([False, False, True])", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.is_quarter_end"}, "pandas.DatetimeIndex.is_year_end": {"type": "property", "docstring": "Indicate whether the date is the last day of the year.\n\nReturns\n-------\nSeries or DatetimeIndex\n The same type as the original data with boolean values. Series will\n have the same name and index. DatetimeIndex will have the same\n name.\n\nSee Also\n--------\nis_year_start : Similar property indicating the start of the year.\n\nExamples\n--------\nThis method is available on Series with datetime values under\nthe ``.dt`` accessor, and directly on DatetimeIndex.\n\n>>> dates = pd.Series(pd.date_range(\"2017-12-30\", periods=3))\n>>> dates\n0 2017-12-30\n1 2017-12-31\n2 2018-01-01\ndtype: datetime64[ns]\n\n>>> dates.dt.is_year_end\n0 False\n1 True\n2 False\ndtype: bool\n\n>>> idx = pd.date_range(\"2017-12-30\", periods=3)\n>>> idx\nDatetimeIndex(['2017-12-30', '2017-12-31', '2018-01-01'],\n dtype='datetime64[ns]', freq='D')\n\n>>> idx.is_year_end\narray([False, True, False])", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.is_year_start"}, "pandas.DatetimeIndex.is_leap_year": {"type": "property", "docstring": "Boolean indicator if the date belongs to a leap year.\n\nA leap year is a year, which has 366 days (instead of 365) including\n29th of February as an intercalary day.\nLeap years are years which are multiples of four with the exception\nof years divisible by 100 but not by 400.\n\nReturns\n-------\nSeries or ndarray\n Booleans indicating if dates belong to a leap year.\n\nExamples\n--------\nThis method is available on Series with datetime values under\nthe ``.dt`` accessor, and directly on DatetimeIndex.\n\n>>> idx = pd.date_range(\"2012-01-01\", \"2015-01-01\", freq=\"Y\")\n>>> idx\nDatetimeIndex(['2012-12-31', '2013-12-31', '2014-12-31'],\n dtype='datetime64[ns]', freq='A-DEC')\n>>> idx.is_leap_year\narray([ True, False, False], dtype=bool)\n\n>>> dates = pd.Series(idx)\n>>> dates_series\n0 2012-12-31\n1 2013-12-31\n2 2014-12-31\ndtype: datetime64[ns]\n>>> dates_series.dt.is_leap_year\n0 True\n1 False\n2 False\ndtype: bool", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 24, in pandas.DatetimeIndex.is_leap_year\nFailed example:\n idx.is_leap_year\nExpected:\n array([ True, False, False], dtype=bool)\nGot:\n array([ True, False, False])\n**********************************************************************\nLine 28, in pandas.DatetimeIndex.is_leap_year\nFailed example:\n dates_series\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.DatetimeIndex.is_leap_year[4]>\", line 1, in <module>\n dates_series\n NameError: name 'dates_series' is not defined\n**********************************************************************\nLine 33, in pandas.DatetimeIndex.is_leap_year\nFailed example:\n dates_series.dt.is_leap_year\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.DatetimeIndex.is_leap_year[5]>\", line 1, in <module>\n dates_series.dt.is_leap_year\n NameError: name 'dates_series' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'dates_series' (2 times)"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 24, in pandas.DatetimeIndex.is_leap_year\nFailed example:\n idx.is_leap_year\nExpected:\n array([ True, False, False], dtype=bool)\nGot:\n array([ True, False, False])\n**********************************************************************\nLine 28, in pandas.DatetimeIndex.is_leap_year\nFailed example:\n dates_series\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.DatetimeIndex.is_leap_year[4]>\", line 1, in <module>\n dates_series\n NameError: name 'dates_series' is not defined\n**********************************************************************\nLine 33, in pandas.DatetimeIndex.is_leap_year\nFailed example:\n dates_series.dt.is_leap_year\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.DatetimeIndex.is_leap_year[5]>\", line 1, in <module>\n dates_series.dt.is_leap_year\n NameError: name 'dates_series' is not defined\n", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.DatetimeIndex.is_year_end"}, "pandas.DatetimeIndex.inferred_freq": {"type": "CachedProperty", "docstring": "Tryies to return a string representing a frequency guess,\ngenerated by infer_freq. Returns None if it can't autodetect the\nfrequency.", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time/Date Components", "shared_code_with": "pandas.IndexSlice"}, "pandas.DatetimeIndex.indexer_at_time": {"type": "function", "docstring": "Returns index locations of index values at particular time of day\n(e.g. 9:30AM).\n\nParameters\n----------\ntime : datetime.time or string\n datetime.time or string in appropriate format (\"%H:%M\", \"%H%M\",\n \"%I:%M%p\", \"%I%M%p\", \"%H:%M:%S\", \"%H%M%S\", \"%I:%M:%S%p\",\n \"%I%M%S%p\").\n\nReturns\n-------\nvalues_at_time : array of integers\n\nSee Also\n--------\nindexer_between_time, DataFrame.at_time", "deprecated": false, "file": "pandas/core/indexes/datetimes.py", "file_line": 1285, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/datetimes.py#L1285", "errors": [["SS05", "Summary must start with infinitive verb, not third person (e.g. use \"Generate\" instead of \"Generates\")"], ["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {asof} not documented"], ["PR06", "Parameter \"time\" type should use \"str\" instead of \"string\""], ["PR08", "Parameter \"time\" description should start with a capital letter"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["SA04", "Missing description for See Also \"indexer_between_time\" reference"], ["SA04", "Missing description for See Also \"DataFrame.at_time\" reference"]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Selecting", "shared_code_with": ""}, "pandas.DatetimeIndex.indexer_between_time": {"type": "function", "docstring": "Return index locations of values between particular times of day\n(e.g., 9:00-9:30AM).\n\nParameters\n----------\nstart_time, end_time : datetime.time, str\n datetime.time or string in appropriate format (\"%H:%M\", \"%H%M\",\n \"%I:%M%p\", \"%I%M%p\", \"%H:%M:%S\", \"%H%M%S\", \"%I:%M:%S%p\",\n \"%I%M%S%p\").\ninclude_start : boolean, default True\ninclude_end : boolean, default True\n\nReturns\n-------\nvalues_between_time : array of integers\n\nSee Also\n--------\nindexer_at_time, DataFrame.between_time", "deprecated": false, "file": "pandas/core/indexes/datetimes.py", "file_line": 1322, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/datetimes.py#L1322", "errors": [["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {end_time, start_time} not documented"], ["PR02", "Unknown parameters {start_time, end_time}"], ["PR08", "Parameter \"start_time, end_time\" description should start with a capital letter"], ["PR06", "Parameter \"include_start\" type should use \"bool\" instead of \"boolean\""], ["PR07", "Parameter \"include_start\" has no description"], ["PR06", "Parameter \"include_end\" type should use \"bool\" instead of \"boolean\""], ["PR07", "Parameter \"include_end\" has no description"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["SA04", "Missing description for See Also \"indexer_at_time\" reference"], ["SA04", "Missing description for See Also \"DataFrame.between_time\" reference"]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Selecting", "shared_code_with": ""}, "pandas.DatetimeIndex.normalize": {"type": "function", "docstring": "Convert times to midnight.\n\nThe time component of the date-time is converted to midnight i.e.\n00:00:00. This is useful in cases, when the time does not matter.\nLength is unaltered. The timezones are unaffected.\n\nThis method is available on Series with datetime values under\nthe ``.dt`` accessor, and directly on Datetime Array/Index.\n\nReturns\n-------\nDatetimeArray, DatetimeIndex or Series\n The same type as the original data. Series will have the same\n name and index. DatetimeIndex will have the same name.\n\nSee Also\n--------\nfloor : Floor the datetimes to the specified freq.\nceil : Ceil the datetimes to the specified freq.\nround : Round the datetimes to the specified freq.\n\nExamples\n--------\n>>> idx = pd.date_range(start='2014-08-01 10:00', freq='H',\n... periods=3, tz='Asia/Calcutta')\n>>> idx\nDatetimeIndex(['2014-08-01 10:00:00+05:30',\n '2014-08-01 11:00:00+05:30',\n '2014-08-01 12:00:00+05:30'],\n dtype='datetime64[ns, Asia/Calcutta]', freq='H')\n>>> idx.normalize()\nDatetimeIndex(['2014-08-01 00:00:00+05:30',\n '2014-08-01 00:00:00+05:30',\n '2014-08-01 00:00:00+05:30'],\n dtype='datetime64[ns, Asia/Calcutta]', freq=None)", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time-specific operations", "shared_code_with": "pandas.CategoricalIndex.as_unordered"}, "pandas.DatetimeIndex.strftime": {"type": "function", "docstring": "Convert to Index using specified date_format.\n\nReturn an Index of formatted strings specified by date_format, which\nsupports the same string format as the python standard library. Details\nof the string format can be found in `python string format\ndoc <https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior>`__\n\nParameters\n----------\ndate_format : str\n Date format string (e.g. \"%Y-%m-%d\").\n\nReturns\n-------\nIndex\n Index of formatted strings\n\nSee Also\n--------\nto_datetime : Convert the given argument to datetime.\nDatetimeIndex.normalize : Return DatetimeIndex with times to midnight.\nDatetimeIndex.round : Round the DatetimeIndex to the specified freq.\nDatetimeIndex.floor : Floor the DatetimeIndex to the specified freq.\n\nExamples\n--------\n>>> rng = pd.date_range(pd.Timestamp(\"2018-03-10 09:00\"),\n... periods=3, freq='s')\n>>> rng.strftime('%B %d, %Y, %r')\nIndex(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM',\n 'March 10, 2018, 09:00:02 AM'],\n dtype='object')", "deprecated": false, "file": "pandas/core/indexes/datetimelike.py", "file_line": 47, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/datetimelike.py#L47", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {date_format}"], ["RT05", "Return value description should finish with \".\""]], "warnings": [], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time-specific operations", "shared_code_with": ""}, "pandas.DatetimeIndex.snap": {"type": "function", "docstring": "Snap time stamps to nearest occurring frequency", "deprecated": false, "file": "pandas/core/indexes/datetimes.py", "file_line": 744, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/datetimes.py#L744", "errors": [["SS03", "Summary does not end with a period"], ["PR01", "Parameters {freq} not documented"], ["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time-specific operations", "shared_code_with": ""}, "pandas.DatetimeIndex.tz_convert": {"type": "function", "docstring": "Convert tz-aware Datetime Array/Index from one time zone to another.\n\nParameters\n----------\ntz : string, pytz.timezone, dateutil.tz.tzfile or None\n Time zone for time. Corresponding timestamps would be converted\n to this time zone of the Datetime Array/Index. A `tz` of None will\n convert to UTC and remove the timezone information.\n\nReturns\n-------\nnormalized : same type as self\n\nRaises\n------\nTypeError\n If Datetime Array/Index is tz-naive.\n\nSee Also\n--------\nDatetimeIndex.tz : A timezone that has a variable offset from UTC.\nDatetimeIndex.tz_localize : Localize tz-naive DatetimeIndex to a\n given time zone, or remove timezone from a tz-aware DatetimeIndex.\n\nExamples\n--------\nWith the `tz` parameter, we can change the DatetimeIndex\nto other time zones:\n\n>>> dti = pd.date_range(start='2014-08-01 09:00',\n... freq='H', periods=3, tz='Europe/Berlin')\n\n>>> dti\nDatetimeIndex(['2014-08-01 09:00:00+02:00',\n '2014-08-01 10:00:00+02:00',\n '2014-08-01 11:00:00+02:00'],\n dtype='datetime64[ns, Europe/Berlin]', freq='H')\n\n>>> dti.tz_convert('US/Central')\nDatetimeIndex(['2014-08-01 02:00:00-05:00',\n '2014-08-01 03:00:00-05:00',\n '2014-08-01 04:00:00-05:00'],\n dtype='datetime64[ns, US/Central]', freq='H')\n\nWith the ``tz=None``, we can remove the timezone (after converting\nto UTC if necessary):\n\n>>> dti = pd.date_range(start='2014-08-01 09:00',freq='H',\n... periods=3, tz='Europe/Berlin')\n\n>>> dti\nDatetimeIndex(['2014-08-01 09:00:00+02:00',\n '2014-08-01 10:00:00+02:00',\n '2014-08-01 11:00:00+02:00'],\n dtype='datetime64[ns, Europe/Berlin]', freq='H')\n\n>>> dti.tz_convert(None)\nDatetimeIndex(['2014-08-01 07:00:00',\n '2014-08-01 08:00:00',\n '2014-08-01 09:00:00'],\n dtype='datetime64[ns]', freq='H')", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {tz}"], ["PR06", "Parameter \"tz\" type should use \"str\" instead of \"string\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX03", "flake8 error: E231 missing whitespace after ','"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time-specific operations", "shared_code_with": "pandas.DatetimeIndex.normalize"}, "pandas.DatetimeIndex.tz_localize": {"type": "function", "docstring": "Localize tz-naive Datetime Array/Index to tz-aware\nDatetime Array/Index.\n\nThis method takes a time zone (tz) naive Datetime Array/Index object\nand makes this time zone aware. It does not move the time to another\ntime zone.\nTime zone localization helps to switch from time zone aware to time\nzone unaware objects.\n\nParameters\n----------\ntz : string, pytz.timezone, dateutil.tz.tzfile or None\n Time zone to convert timestamps to. Passing ``None`` will\n remove the time zone information preserving local time.\nambiguous : 'infer', 'NaT', bool array, default 'raise'\n When clocks moved backward due to DST, ambiguous times may arise.\n For example in Central European Time (UTC+01), when going from\n 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at\n 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the\n `ambiguous` parameter dictates how ambiguous times should be\n handled.\n\n - 'infer' will attempt to infer fall dst-transition hours based on\n order\n - bool-ndarray where True signifies a DST time, False signifies a\n non-DST time (note that this flag is only applicable for\n ambiguous times)\n - 'NaT' will return NaT where there are ambiguous times\n - 'raise' will raise an AmbiguousTimeError if there are ambiguous\n times\n\nnonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta,\n default 'raise'\n A nonexistent time does not exist in a particular timezone\n where clocks moved forward due to DST.\n\n - 'shift_forward' will shift the nonexistent time forward to the\n closest existing time\n - 'shift_backward' will shift the nonexistent time backward to the\n closest existing time\n - 'NaT' will return NaT where there are nonexistent times\n - timedelta objects will shift nonexistent times by the timedelta\n - 'raise' will raise an NonExistentTimeError if there are\n nonexistent times\n\n .. versionadded:: 0.24.0\n\nerrors : {'raise', 'coerce'}, default None\n\n - 'raise' will raise a NonExistentTimeError if a timestamp is not\n valid in the specified time zone (e.g. due to a transition from\n or to DST time). Use ``nonexistent='raise'`` instead.\n - 'coerce' will return NaT if the timestamp can not be converted\n to the specified time zone. Use ``nonexistent='NaT'`` instead.\n\n .. deprecated:: 0.24.0\n\nReturns\n-------\nresult : same type as self\n Array/Index converted to the specified time zone.\n\nRaises\n------\nTypeError\n If the Datetime Array/Index is tz-aware and tz is not None.\n\nSee Also\n--------\nDatetimeIndex.tz_convert : Convert tz-aware DatetimeIndex from\n one time zone to another.\n\nExamples\n--------\n>>> tz_naive = pd.date_range('2018-03-01 09:00', periods=3)\n>>> tz_naive\nDatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00',\n '2018-03-03 09:00:00'],\n dtype='datetime64[ns]', freq='D')\n\nLocalize DatetimeIndex in US/Eastern time zone:\n\n>>> tz_aware = tz_naive.tz_localize(tz='US/Eastern')\n>>> tz_aware\nDatetimeIndex(['2018-03-01 09:00:00-05:00',\n '2018-03-02 09:00:00-05:00',\n '2018-03-03 09:00:00-05:00'],\n dtype='datetime64[ns, US/Eastern]', freq='D')\n\nWith the ``tz=None``, we can remove the time zone information\nwhile keeping the local time (not converted to UTC):\n\n>>> tz_aware.tz_localize(None)\nDatetimeIndex(['2018-03-01 09:00:00', '2018-03-02 09:00:00',\n '2018-03-03 09:00:00'],\n dtype='datetime64[ns]', freq='D')\n\nBe careful with DST changes. When there is sequential data, pandas can\ninfer the DST time:\n>>> s = pd.to_datetime(pd.Series([\n... '2018-10-28 01:30:00',\n... '2018-10-28 02:00:00',\n... '2018-10-28 02:30:00',\n... '2018-10-28 02:00:00',\n... '2018-10-28 02:30:00',\n... '2018-10-28 03:00:00',\n... '2018-10-28 03:30:00']))\n>>> s.dt.tz_localize('CET', ambiguous='infer')\n2018-10-28 01:30:00+02:00 0\n2018-10-28 02:00:00+02:00 1\n2018-10-28 02:30:00+02:00 2\n2018-10-28 02:00:00+01:00 3\n2018-10-28 02:30:00+01:00 4\n2018-10-28 03:00:00+01:00 5\n2018-10-28 03:30:00+01:00 6\ndtype: int64\n\nIn some cases, inferring the DST is impossible. In such cases, you can\npass an ndarray to the ambiguous parameter to set the DST explicitly\n\n>>> s = pd.to_datetime(pd.Series([\n... '2018-10-28 01:20:00',\n... '2018-10-28 02:36:00',\n... '2018-10-28 03:46:00']))\n>>> s.dt.tz_localize('CET', ambiguous=np.array([True, True, False]))\n0 2018-10-28 01:20:00+02:00\n1 2018-10-28 02:36:00+02:00\n2 2018-10-28 03:46:00+01:00\ndtype: datetime64[ns, CET]\n\nIf the DST transition causes nonexistent times, you can shift these\ndates forward or backwards with a timedelta object or `'shift_forward'`\nor `'shift_backwards'`.\n>>> s = pd.to_datetime(pd.Series([\n... '2015-03-29 02:30:00',\n... '2015-03-29 03:30:00']))\n>>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_forward')\n0 2015-03-29 03:00:00+02:00\n1 2015-03-29 03:30:00+02:00\ndtype: datetime64[ns, 'Europe/Warsaw']\n>>> s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_backward')\n0 2015-03-29 01:59:59.999999999+01:00\n1 2015-03-29 03:30:00+02:00\ndtype: datetime64[ns, 'Europe/Warsaw']\n>>> s.dt.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H'))\n0 2015-03-29 03:30:00+02:00\n1 2015-03-29 03:30:00+02:00\ndtype: datetime64[ns, 'Europe/Warsaw']", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {nonexistent, tz, errors, ambiguous}"], ["PR06", "Parameter \"tz\" type should use \"str\" instead of \"string\""], ["PR09", "Parameter \"ambiguous\" description should finish with \".\""], ["PR08", "Parameter \"nonexistent\" description should start with a capital letter"], ["PR09", "Parameter \"nonexistent\" description should finish with \".\""], ["PR08", "Parameter \"errors\" description should start with a capital letter"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 109, in pandas.DatetimeIndex.tz_localize\nFailed example:\n s.dt.tz_localize('CET', ambiguous='infer')\nExpected:\n 2018-10-28 01:30:00+02:00 0\n 2018-10-28 02:00:00+02:00 1\n 2018-10-28 02:30:00+02:00 2\n 2018-10-28 02:00:00+01:00 3\n 2018-10-28 02:30:00+01:00 4\n 2018-10-28 03:00:00+01:00 5\n 2018-10-28 03:30:00+01:00 6\n dtype: int64\nGot:\n 0 2018-10-28 01:30:00+02:00\n 1 2018-10-28 02:00:00+02:00\n 2 2018-10-28 02:30:00+02:00\n 3 2018-10-28 02:00:00+01:00\n 4 2018-10-28 02:30:00+01:00\n 5 2018-10-28 03:00:00+01:00\n 6 2018-10-28 03:30:00+01:00\n dtype: datetime64[ns, CET]\n**********************************************************************\nLine 138, in pandas.DatetimeIndex.tz_localize\nFailed example:\n s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_forward')\nExpected:\n 0 2015-03-29 03:00:00+02:00\n 1 2015-03-29 03:30:00+02:00\n dtype: datetime64[ns, 'Europe/Warsaw']\nGot:\n 0 2015-03-29 03:00:00+02:00\n 1 2015-03-29 03:30:00+02:00\n dtype: datetime64[ns, Europe/Warsaw]\n**********************************************************************\nLine 142, in pandas.DatetimeIndex.tz_localize\nFailed example:\n s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_backward')\nExpected:\n 0 2015-03-29 01:59:59.999999999+01:00\n 1 2015-03-29 03:30:00+02:00\n dtype: datetime64[ns, 'Europe/Warsaw']\nGot:\n 0 2015-03-29 01:59:59.999999999+01:00\n 1 2015-03-29 03:30:00+02:00\n dtype: datetime64[ns, Europe/Warsaw]\n**********************************************************************\nLine 146, in pandas.DatetimeIndex.tz_localize\nFailed example:\n s.dt.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H'))\nExpected:\n 0 2015-03-29 03:30:00+02:00\n 1 2015-03-29 03:30:00+02:00\n dtype: datetime64[ns, 'Europe/Warsaw']\nGot:\n 0 2015-03-29 03:30:00+02:00\n 1 2015-03-29 03:30:00+02:00\n dtype: datetime64[ns, Europe/Warsaw]\n"], ["EX03", "flake8 error: E122 continuation line missing indentation or outdented (12 times)"]], "warnings": [], "examples_errors": "**********************************************************************\nLine 109, in pandas.DatetimeIndex.tz_localize\nFailed example:\n s.dt.tz_localize('CET', ambiguous='infer')\nExpected:\n 2018-10-28 01:30:00+02:00 0\n 2018-10-28 02:00:00+02:00 1\n 2018-10-28 02:30:00+02:00 2\n 2018-10-28 02:00:00+01:00 3\n 2018-10-28 02:30:00+01:00 4\n 2018-10-28 03:00:00+01:00 5\n 2018-10-28 03:30:00+01:00 6\n dtype: int64\nGot:\n 0 2018-10-28 01:30:00+02:00\n 1 2018-10-28 02:00:00+02:00\n 2 2018-10-28 02:30:00+02:00\n 3 2018-10-28 02:00:00+01:00\n 4 2018-10-28 02:30:00+01:00\n 5 2018-10-28 03:00:00+01:00\n 6 2018-10-28 03:30:00+01:00\n dtype: datetime64[ns, CET]\n**********************************************************************\nLine 138, in pandas.DatetimeIndex.tz_localize\nFailed example:\n s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_forward')\nExpected:\n 0 2015-03-29 03:00:00+02:00\n 1 2015-03-29 03:30:00+02:00\n dtype: datetime64[ns, 'Europe/Warsaw']\nGot:\n 0 2015-03-29 03:00:00+02:00\n 1 2015-03-29 03:30:00+02:00\n dtype: datetime64[ns, Europe/Warsaw]\n**********************************************************************\nLine 142, in pandas.DatetimeIndex.tz_localize\nFailed example:\n s.dt.tz_localize('Europe/Warsaw', nonexistent='shift_backward')\nExpected:\n 0 2015-03-29 01:59:59.999999999+01:00\n 1 2015-03-29 03:30:00+02:00\n dtype: datetime64[ns, 'Europe/Warsaw']\nGot:\n 0 2015-03-29 01:59:59.999999999+01:00\n 1 2015-03-29 03:30:00+02:00\n dtype: datetime64[ns, Europe/Warsaw]\n**********************************************************************\nLine 146, in pandas.DatetimeIndex.tz_localize\nFailed example:\n s.dt.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H'))\nExpected:\n 0 2015-03-29 03:30:00+02:00\n 1 2015-03-29 03:30:00+02:00\n dtype: datetime64[ns, 'Europe/Warsaw']\nGot:\n 0 2015-03-29 03:30:00+02:00\n 1 2015-03-29 03:30:00+02:00\n dtype: datetime64[ns, Europe/Warsaw]\n", "in_api": true, "section": "DatetimeIndex", "subsection": "Time-specific operations", "shared_code_with": "pandas.DatetimeIndex.tz_convert"}, "pandas.DatetimeIndex.round": {"type": "function", "docstring": "Perform round operation on the data to the specified `freq`.\n\nParameters\n----------\nfreq : str or Offset\n The frequency level to round the index to. Must be a fixed\n frequency like 'S' (second) not 'ME' (month end). See\n :ref:`frequency aliases <timeseries.offset_aliases>` for\n a list of possible `freq` values.\nambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'\n Only relevant for DatetimeIndex:\n\n - 'infer' will attempt to infer fall dst-transition hours based on\n order\n - bool-ndarray where True signifies a DST time, False designates\n a non-DST time (note that this flag is only applicable for\n ambiguous times)\n - 'NaT' will return NaT where there are ambiguous times\n - 'raise' will raise an AmbiguousTimeError if there are ambiguous\n times\n\n .. versionadded:: 0.24.0\n\nnonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta,\n default 'raise'\n A nonexistent time does not exist in a particular timezone\n where clocks moved forward due to DST.\n\n - 'shift_forward' will shift the nonexistent time forward to the\n closest existing time\n - 'shift_backward' will shift the nonexistent time backward to the\n closest existing time\n - 'NaT' will return NaT where there are nonexistent times\n - timedelta objects will shift nonexistent times by the timedelta\n - 'raise' will raise an NonExistentTimeError if there are\n nonexistent times\n\n .. versionadded:: 0.24.0\n\nReturns\n-------\nDatetimeIndex, TimedeltaIndex, or Series\n Index of the same type for a DatetimeIndex or TimedeltaIndex,\n or a Series with the same index for a Series.\n\nRaises\n------\nValueError if the `freq` cannot be converted.\n\nExamples\n--------\n**DatetimeIndex**\n\n>>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')\n>>> rng\nDatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',\n '2018-01-01 12:01:00'],\n dtype='datetime64[ns]', freq='T')\n>>> rng.round('H')\nDatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',\n '2018-01-01 12:00:00'],\n dtype='datetime64[ns]', freq=None)\n\n**Series**\n\n>>> pd.Series(rng).dt.round(\"H\")\n0 2018-01-01 12:00:00\n1 2018-01-01 12:00:00\n2 2018-01-01 12:00:00\ndtype: datetime64[ns]", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {freq, nonexistent, ambiguous}"], ["PR09", "Parameter \"ambiguous\" description should finish with \".\""], ["PR08", "Parameter \"nonexistent\" description should start with a capital letter"], ["PR09", "Parameter \"nonexistent\" description should finish with \".\""]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time-specific operations", "shared_code_with": "pandas.DatetimeIndex.tz_localize"}, "pandas.DatetimeIndex.floor": {"type": "function", "docstring": "Perform floor operation on the data to the specified `freq`.\n\nParameters\n----------\nfreq : str or Offset\n The frequency level to floor the index to. Must be a fixed\n frequency like 'S' (second) not 'ME' (month end). See\n :ref:`frequency aliases <timeseries.offset_aliases>` for\n a list of possible `freq` values.\nambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'\n Only relevant for DatetimeIndex:\n\n - 'infer' will attempt to infer fall dst-transition hours based on\n order\n - bool-ndarray where True signifies a DST time, False designates\n a non-DST time (note that this flag is only applicable for\n ambiguous times)\n - 'NaT' will return NaT where there are ambiguous times\n - 'raise' will raise an AmbiguousTimeError if there are ambiguous\n times\n\n .. versionadded:: 0.24.0\n\nnonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta,\n default 'raise'\n A nonexistent time does not exist in a particular timezone\n where clocks moved forward due to DST.\n\n - 'shift_forward' will shift the nonexistent time forward to the\n closest existing time\n - 'shift_backward' will shift the nonexistent time backward to the\n closest existing time\n - 'NaT' will return NaT where there are nonexistent times\n - timedelta objects will shift nonexistent times by the timedelta\n - 'raise' will raise an NonExistentTimeError if there are\n nonexistent times\n\n .. versionadded:: 0.24.0\n\nReturns\n-------\nDatetimeIndex, TimedeltaIndex, or Series\n Index of the same type for a DatetimeIndex or TimedeltaIndex,\n or a Series with the same index for a Series.\n\nRaises\n------\nValueError if the `freq` cannot be converted.\n\nExamples\n--------\n**DatetimeIndex**\n\n>>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')\n>>> rng\nDatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',\n '2018-01-01 12:01:00'],\n dtype='datetime64[ns]', freq='T')\n>>> rng.floor('H')\nDatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00',\n '2018-01-01 12:00:00'],\n dtype='datetime64[ns]', freq=None)\n\n**Series**\n\n>>> pd.Series(rng).dt.floor(\"H\")\n0 2018-01-01 11:00:00\n1 2018-01-01 12:00:00\n2 2018-01-01 12:00:00\ndtype: datetime64[ns]", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {freq, nonexistent, ambiguous}"], ["PR09", "Parameter \"ambiguous\" description should finish with \".\""], ["PR08", "Parameter \"nonexistent\" description should start with a capital letter"], ["PR09", "Parameter \"nonexistent\" description should finish with \".\""]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time-specific operations", "shared_code_with": "pandas.DatetimeIndex.round"}, "pandas.DatetimeIndex.ceil": {"type": "function", "docstring": "Perform ceil operation on the data to the specified `freq`.\n\nParameters\n----------\nfreq : str or Offset\n The frequency level to ceil the index to. Must be a fixed\n frequency like 'S' (second) not 'ME' (month end). See\n :ref:`frequency aliases <timeseries.offset_aliases>` for\n a list of possible `freq` values.\nambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'\n Only relevant for DatetimeIndex:\n\n - 'infer' will attempt to infer fall dst-transition hours based on\n order\n - bool-ndarray where True signifies a DST time, False designates\n a non-DST time (note that this flag is only applicable for\n ambiguous times)\n - 'NaT' will return NaT where there are ambiguous times\n - 'raise' will raise an AmbiguousTimeError if there are ambiguous\n times\n\n .. versionadded:: 0.24.0\n\nnonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta,\n default 'raise'\n A nonexistent time does not exist in a particular timezone\n where clocks moved forward due to DST.\n\n - 'shift_forward' will shift the nonexistent time forward to the\n closest existing time\n - 'shift_backward' will shift the nonexistent time backward to the\n closest existing time\n - 'NaT' will return NaT where there are nonexistent times\n - timedelta objects will shift nonexistent times by the timedelta\n - 'raise' will raise an NonExistentTimeError if there are\n nonexistent times\n\n .. versionadded:: 0.24.0\n\nReturns\n-------\nDatetimeIndex, TimedeltaIndex, or Series\n Index of the same type for a DatetimeIndex or TimedeltaIndex,\n or a Series with the same index for a Series.\n\nRaises\n------\nValueError if the `freq` cannot be converted.\n\nExamples\n--------\n**DatetimeIndex**\n\n>>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')\n>>> rng\nDatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',\n '2018-01-01 12:01:00'],\n dtype='datetime64[ns]', freq='T')\n>>> rng.ceil('H')\nDatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',\n '2018-01-01 13:00:00'],\n dtype='datetime64[ns]', freq=None)\n\n**Series**\n\n>>> pd.Series(rng).dt.ceil(\"H\")\n0 2018-01-01 12:00:00\n1 2018-01-01 12:00:00\n2 2018-01-01 13:00:00\ndtype: datetime64[ns]", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {freq, nonexistent, ambiguous}"], ["PR09", "Parameter \"ambiguous\" description should finish with \".\""], ["PR08", "Parameter \"nonexistent\" description should start with a capital letter"], ["PR09", "Parameter \"nonexistent\" description should finish with \".\""]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time-specific operations", "shared_code_with": "pandas.DatetimeIndex.floor"}, "pandas.DatetimeIndex.month_name": {"type": "function", "docstring": "Return the month names of the DateTimeIndex with specified locale.\n\n.. versionadded:: 0.23.0\n\nParameters\n----------\nlocale : str, optional\n Locale determining the language in which to return the month name.\n Default is English locale.\n\nReturns\n-------\nIndex\n Index of month names.\n\nExamples\n--------\n>>> idx = pd.date_range(start='2018-01', freq='M', periods=3)\n>>> idx\nDatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31'],\n dtype='datetime64[ns]', freq='M')\n>>> idx.month_name()\nIndex(['January', 'February', 'March'], dtype='object')", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {locale}"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time-specific operations", "shared_code_with": "pandas.DatetimeIndex.ceil"}, "pandas.DatetimeIndex.day_name": {"type": "function", "docstring": "Return the day names of the DateTimeIndex with specified locale.\n\n.. versionadded:: 0.23.0\n\nParameters\n----------\nlocale : str, optional\n Locale determining the language in which to return the day name.\n Default is English locale.\n\nReturns\n-------\nIndex\n Index of day names.\n\nExamples\n--------\n>>> idx = pd.date_range(start='2018-01-01', freq='D', periods=3)\n>>> idx\nDatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03'],\n dtype='datetime64[ns]', freq='D')\n>>> idx.day_name()\nIndex(['Monday', 'Tuesday', 'Wednesday'], dtype='object')", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {locale}"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Time-specific operations", "shared_code_with": "pandas.DatetimeIndex.month_name"}, "pandas.DatetimeIndex.to_period": {"type": "function", "docstring": "Cast to PeriodArray/Index at a particular frequency.\n\nConverts DatetimeArray/Index to PeriodArray/Index.\n\nParameters\n----------\nfreq : string or Offset, optional\n One of pandas' :ref:`offset strings <timeseries.offset_aliases>`\n or an Offset object. Will be inferred by default.\n\nReturns\n-------\nPeriodArray/Index\n\nRaises\n------\nValueError\n When converting a DatetimeArray/Index with non-regular values,\n so that a frequency cannot be inferred.\n\nSee Also\n--------\nPeriodIndex: Immutable ndarray holding ordinal values.\nDatetimeIndex.to_pydatetime: Return DatetimeIndex as object.\n\nExamples\n--------\n>>> df = pd.DataFrame({\"y\": [1,2,3]},\n... index=pd.to_datetime([\"2000-03-31 00:00:00\",\n... \"2000-05-31 00:00:00\",\n... \"2000-08-31 00:00:00\"]))\n>>> df.index.to_period(\"M\")\nPeriodIndex(['2000-03', '2000-05', '2000-08'],\n dtype='period[M]', freq='M')\n\nInfer the daily frequency\n\n>>> idx = pd.date_range(\"2017-01-01\", periods=2)\n>>> idx.to_period()\nPeriodIndex(['2017-01-01', '2017-01-02'],\n dtype='period[D]', freq='D')", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {freq}"], ["PR06", "Parameter \"freq\" type should use \"str\" instead of \"string\""], ["RT03", "Return value has no description"], ["EX03", "flake8 error: E231 missing whitespace after ',' (2 times)"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Conversion", "shared_code_with": "pandas.DatetimeIndex.day_name"}, "pandas.DatetimeIndex.to_perioddelta": {"type": "function", "docstring": "Calculate TimedeltaArray of difference between index\nvalues and index converted to PeriodArray at specified\nfreq. Used for vectorized offsets\n\nParameters\n----------\nfreq : Period frequency\n\nReturns\n-------\nTimedeltaArray/Index", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["SS03", "Summary does not end with a period"], ["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {freq}"], ["PR07", "Parameter \"freq\" has no description"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Conversion", "shared_code_with": "pandas.DatetimeIndex.to_period"}, "pandas.DatetimeIndex.to_pydatetime": {"type": "function", "docstring": "Return Datetime Array/Index as object ndarray of datetime.datetime\nobjects\n\nReturns\n-------\ndatetimes : ndarray", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["SS03", "Summary does not end with a period"], ["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {**kwargs, *args} not documented"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Conversion", "shared_code_with": "pandas.DatetimeIndex.to_perioddelta"}, "pandas.DatetimeIndex.to_series": {"type": "function", "docstring": "Create a Series with both index and values equal to the index keys\nuseful with map for returning an indexer based on an index\n\nParameters\n----------\nkeep_tz : optional, defaults False\n Return the data keeping the timezone.\n\n If keep_tz is True:\n\n If the timezone is not set, the resulting\n Series will have a datetime64[ns] dtype.\n\n Otherwise the Series will have an datetime64[ns, tz] dtype; the\n tz will be preserved.\n\n If keep_tz is False:\n\n Series will have a datetime64[ns] dtype. TZ aware\n objects will have the tz removed.\n\n .. versionchanged:: 0.24\n The default value will change to True in a future release.\n You can set ``keep_tz=True`` to already obtain the future\n behaviour and silence the warning.\n\nindex : Index, optional\n index of resulting Series. If None, defaults to original index\nname : string, optional\n name of resulting Series. If None, defaults to name of original\n index\n\nReturns\n-------\nSeries", "deprecated": false, "file": "pandas/core/indexes/datetimes.py", "file_line": 678, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/datetimes.py#L678", "errors": [["SS03", "Summary does not end with a period"], ["SS06", "Summary should fit in a single line"], ["PR08", "Parameter \"index\" description should start with a capital letter"], ["PR09", "Parameter \"index\" description should finish with \".\""], ["PR06", "Parameter \"name\" type should use \"str\" instead of \"string\""], ["PR08", "Parameter \"name\" description should start with a capital letter"], ["PR09", "Parameter \"name\" description should finish with \".\""], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Conversion", "shared_code_with": ""}, "pandas.DatetimeIndex.to_frame": {"type": "function", "docstring": "Create a DataFrame with a column containing the Index.\n\n.. versionadded:: 0.24.0\n\nParameters\n----------\nindex : boolean, default True\n Set the index of the returned DataFrame as the original Index.\n\nname : object, default None\n The passed name should substitute for the index name (if it has\n one).\n\nReturns\n-------\nDataFrame\n DataFrame containing the original Index data.\n\nSee Also\n--------\nIndex.to_series : Convert an Index to a Series.\nSeries.to_frame : Convert Series to DataFrame.\n\nExamples\n--------\n>>> idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal')\n>>> idx.to_frame()\n animal\nanimal\nAnt Ant\nBear Bear\nCow Cow\n\nBy default, the original Index is reused. To enforce a new Index:\n\n>>> idx.to_frame(index=False)\n animal\n0 Ant\n1 Bear\n2 Cow\n\nTo override the name of the resulting column, specify `name`:\n\n>>> idx.to_frame(index=False, name='zoo')\n zoo\n0 Ant\n1 Bear\n2 Cow", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1153, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1153", "errors": [["PR06", "Parameter \"index\" type should use \"bool\" instead of \"boolean\""]], "warnings": [], "examples_errors": "", "in_api": true, "section": "DatetimeIndex", "subsection": "Conversion", "shared_code_with": "pandas.Index.to_frame"}, "pandas.TimedeltaIndex": {"type": "type", "docstring": "Immutable ndarray of timedelta64 data, represented internally as int64, and\nwhich can be boxed to timedelta objects\n\nParameters\n----------\ndata : array-like (1-dimensional), optional\n Optional timedelta-like data to construct index with\nunit : unit of the arg (D,h,m,s,ms,us,ns) denote the unit, optional\n which is an integer/float number\nfreq : string or pandas offset object, optional\n One of pandas date offset strings or corresponding objects. The string\n 'infer' can be passed in order to set the frequency of the index as the\n inferred frequency upon creation\ncopy : bool\n Make a copy of input ndarray\nstart : starting value, timedelta-like, optional\n If data is None, start is used as the start point in generating regular\n timedelta data.\n\n .. deprecated:: 0.24.0\n\nperiods : int, optional, > 0\n Number of periods to generate, if generating index. Takes precedence\n over end argument\n\n .. deprecated:: 0.24.0\n\nend : end time, timedelta-like, optional\n If periods is none, generated index will extend to first conforming\n time on or just past end argument\n\n .. deprecated:: 0.24. 0\n\nclosed : string or None, default None\n Make the interval closed with respect to the given frequency to\n the 'left', 'right', or both sides (None)\n\n .. deprecated:: 0.24. 0\n\nname : object\n Name to be stored in the index\n\nAttributes\n----------\ndays\nseconds\nmicroseconds\nnanoseconds\ncomponents\ninferred_freq\n\nMethods\n-------\nto_pytimedelta\nto_series\nround\nfloor\nceil\nto_frame\n\nSee Also\n---------\nIndex : The base pandas Index type.\nTimedelta : Represents a duration between two dates or times.\nDatetimeIndex : Index of datetime64 data.\nPeriodIndex : Index of Period data.\ntimedelta_range : Create a fixed-frequency TimedeltaIndex.\n\nNotes\n-----\nTo learn more about the frequency strings, please see `this link\n<http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.\n\nCreating a TimedeltaIndex based on `start`, `periods`, and `end` has\nbeen deprecated in favor of :func:`timedelta_range`.", "deprecated": false, "file": "pandas/core/indexes/timedeltas.py", "file_line": 71, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/timedeltas.py#L71", "errors": [["SS03", "Summary does not end with a period"], ["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {verify_integrity, data, copy, dtype, periods} not documented"], ["PR02", "Unknown parameters {periods , copy , data }"], ["PR09", "Parameter \"data \" description should finish with \".\""], ["PR08", "Parameter \"unit\" description should start with a capital letter"], ["PR09", "Parameter \"unit\" description should finish with \".\""], ["PR06", "Parameter \"freq\" type should use \"str\" instead of \"string\""], ["PR09", "Parameter \"freq\" description should finish with \".\""], ["PR09", "Parameter \"copy \" description should finish with \".\""], ["PR09", "Parameter \"periods \" description should finish with \".\""], ["PR09", "Parameter \"end\" description should finish with \".\""], ["PR06", "Parameter \"closed\" type should use \"str\" instead of \"string\""], ["PR09", "Parameter \"closed\" description should finish with \".\""], ["PR09", "Parameter \"name\" description should finish with \".\""]], "warnings": [["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "TimedeltaIndex", "subsection": "Conversion", "shared_code_with": ""}, "pandas.TimedeltaIndex.days": {"type": "property", "docstring": "Number of days for each element.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "TimedeltaIndex", "subsection": "Components", "shared_code_with": "pandas.DatetimeIndex.is_leap_year"}, "pandas.TimedeltaIndex.seconds": {"type": "property", "docstring": "Number of seconds (>= 0 and less than 1 day) for each element.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "TimedeltaIndex", "subsection": "Components", "shared_code_with": "pandas.TimedeltaIndex.days"}, "pandas.TimedeltaIndex.microseconds": {"type": "property", "docstring": "Number of microseconds (>= 0 and less than 1 second) for each element.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "TimedeltaIndex", "subsection": "Components", "shared_code_with": "pandas.TimedeltaIndex.seconds"}, "pandas.TimedeltaIndex.nanoseconds": {"type": "property", "docstring": "Number of nanoseconds (>= 0 and less than 1 microsecond) for each element.", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "TimedeltaIndex", "subsection": "Components", "shared_code_with": "pandas.TimedeltaIndex.microseconds"}, "pandas.TimedeltaIndex.components": {"type": "property", "docstring": "Return a dataframe of the components (days, hours, minutes,\nseconds, milliseconds, microseconds, nanoseconds) of the Timedeltas.\n\nReturns\n-------\na DataFrame", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "TimedeltaIndex", "subsection": "Components", "shared_code_with": "pandas.TimedeltaIndex.nanoseconds"}, "pandas.TimedeltaIndex.inferred_freq": {"type": "CachedProperty", "docstring": "Tryies to return a string representing a frequency guess,\ngenerated by infer_freq. Returns None if it can't autodetect the\nfrequency.", "deprecated": false, "file": null, "file_line": null, "github_link": "https://github.com/pandas-dev/pandas/blob/master/None#LNone", "errors": [["SS06", "Summary should fit in a single line"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "TimedeltaIndex", "subsection": "Components", "shared_code_with": "pandas.DatetimeIndex.inferred_freq"}, "pandas.TimedeltaIndex.to_pytimedelta": {"type": "function", "docstring": "Return Timedelta Array/Index as object ndarray of datetime.timedelta\nobjects.\n\nReturns\n-------\ndatetimes : ndarray", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {**kwargs, *args} not documented"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "TimedeltaIndex", "subsection": "Conversion", "shared_code_with": "pandas.DatetimeIndex.to_pydatetime"}, "pandas.TimedeltaIndex.to_series": {"type": "function", "docstring": "Create a Series with both index and values equal to the index keys\nuseful with map for returning an indexer based on an index.\n\nParameters\n----------\nindex : Index, optional\n index of resulting Series. If None, defaults to original index\nname : string, optional\n name of resulting Series. If None, defaults to name of original\n index\n\nReturns\n-------\nSeries : dtype will be based on the type of the Index values.", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1126, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1126", "errors": [["SS06", "Summary should fit in a single line"], ["PR08", "Parameter \"index\" description should start with a capital letter"], ["PR09", "Parameter \"index\" description should finish with \".\""], ["PR06", "Parameter \"name\" type should use \"str\" instead of \"string\""], ["PR08", "Parameter \"name\" description should start with a capital letter"], ["PR09", "Parameter \"name\" description should finish with \".\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "TimedeltaIndex", "subsection": "Conversion", "shared_code_with": "pandas.Index.to_series"}, "pandas.TimedeltaIndex.round": {"type": "function", "docstring": "Perform round operation on the data to the specified `freq`.\n\nParameters\n----------\nfreq : str or Offset\n The frequency level to round the index to. Must be a fixed\n frequency like 'S' (second) not 'ME' (month end). See\n :ref:`frequency aliases <timeseries.offset_aliases>` for\n a list of possible `freq` values.\nambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'\n Only relevant for DatetimeIndex:\n\n - 'infer' will attempt to infer fall dst-transition hours based on\n order\n - bool-ndarray where True signifies a DST time, False designates\n a non-DST time (note that this flag is only applicable for\n ambiguous times)\n - 'NaT' will return NaT where there are ambiguous times\n - 'raise' will raise an AmbiguousTimeError if there are ambiguous\n times\n\n .. versionadded:: 0.24.0\n\nnonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta,\n default 'raise'\n A nonexistent time does not exist in a particular timezone\n where clocks moved forward due to DST.\n\n - 'shift_forward' will shift the nonexistent time forward to the\n closest existing time\n - 'shift_backward' will shift the nonexistent time backward to the\n closest existing time\n - 'NaT' will return NaT where there are nonexistent times\n - timedelta objects will shift nonexistent times by the timedelta\n - 'raise' will raise an NonExistentTimeError if there are\n nonexistent times\n\n .. versionadded:: 0.24.0\n\nReturns\n-------\nDatetimeIndex, TimedeltaIndex, or Series\n Index of the same type for a DatetimeIndex or TimedeltaIndex,\n or a Series with the same index for a Series.\n\nRaises\n------\nValueError if the `freq` cannot be converted.\n\nExamples\n--------\n**DatetimeIndex**\n\n>>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')\n>>> rng\nDatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',\n '2018-01-01 12:01:00'],\n dtype='datetime64[ns]', freq='T')\n>>> rng.round('H')\nDatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',\n '2018-01-01 12:00:00'],\n dtype='datetime64[ns]', freq=None)\n\n**Series**\n\n>>> pd.Series(rng).dt.round(\"H\")\n0 2018-01-01 12:00:00\n1 2018-01-01 12:00:00\n2 2018-01-01 12:00:00\ndtype: datetime64[ns]", "deprecated": false, "file": "pandas/core/arrays/datetimelike.py", "file_line": 306, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/arrays/datetimelike.py#L306", "errors": [["PR09", "Parameter \"ambiguous\" description should finish with \".\""], ["PR08", "Parameter \"nonexistent\" description should start with a capital letter"], ["PR09", "Parameter \"nonexistent\" description should finish with \".\""]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "TimedeltaIndex", "subsection": "Conversion", "shared_code_with": ""}, "pandas.TimedeltaIndex.floor": {"type": "function", "docstring": "Perform floor operation on the data to the specified `freq`.\n\nParameters\n----------\nfreq : str or Offset\n The frequency level to floor the index to. Must be a fixed\n frequency like 'S' (second) not 'ME' (month end). See\n :ref:`frequency aliases <timeseries.offset_aliases>` for\n a list of possible `freq` values.\nambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'\n Only relevant for DatetimeIndex:\n\n - 'infer' will attempt to infer fall dst-transition hours based on\n order\n - bool-ndarray where True signifies a DST time, False designates\n a non-DST time (note that this flag is only applicable for\n ambiguous times)\n - 'NaT' will return NaT where there are ambiguous times\n - 'raise' will raise an AmbiguousTimeError if there are ambiguous\n times\n\n .. versionadded:: 0.24.0\n\nnonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta,\n default 'raise'\n A nonexistent time does not exist in a particular timezone\n where clocks moved forward due to DST.\n\n - 'shift_forward' will shift the nonexistent time forward to the\n closest existing time\n - 'shift_backward' will shift the nonexistent time backward to the\n closest existing time\n - 'NaT' will return NaT where there are nonexistent times\n - timedelta objects will shift nonexistent times by the timedelta\n - 'raise' will raise an NonExistentTimeError if there are\n nonexistent times\n\n .. versionadded:: 0.24.0\n\nReturns\n-------\nDatetimeIndex, TimedeltaIndex, or Series\n Index of the same type for a DatetimeIndex or TimedeltaIndex,\n or a Series with the same index for a Series.\n\nRaises\n------\nValueError if the `freq` cannot be converted.\n\nExamples\n--------\n**DatetimeIndex**\n\n>>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')\n>>> rng\nDatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',\n '2018-01-01 12:01:00'],\n dtype='datetime64[ns]', freq='T')\n>>> rng.floor('H')\nDatetimeIndex(['2018-01-01 11:00:00', '2018-01-01 12:00:00',\n '2018-01-01 12:00:00'],\n dtype='datetime64[ns]', freq=None)\n\n**Series**\n\n>>> pd.Series(rng).dt.floor(\"H\")\n0 2018-01-01 11:00:00\n1 2018-01-01 12:00:00\n2 2018-01-01 12:00:00\ndtype: datetime64[ns]", "deprecated": false, "file": "pandas/core/arrays/datetimelike.py", "file_line": 312, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/arrays/datetimelike.py#L312", "errors": [["PR09", "Parameter \"ambiguous\" description should finish with \".\""], ["PR08", "Parameter \"nonexistent\" description should start with a capital letter"], ["PR09", "Parameter \"nonexistent\" description should finish with \".\""]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "TimedeltaIndex", "subsection": "Conversion", "shared_code_with": ""}, "pandas.TimedeltaIndex.ceil": {"type": "function", "docstring": "Perform ceil operation on the data to the specified `freq`.\n\nParameters\n----------\nfreq : str or Offset\n The frequency level to ceil the index to. Must be a fixed\n frequency like 'S' (second) not 'ME' (month end). See\n :ref:`frequency aliases <timeseries.offset_aliases>` for\n a list of possible `freq` values.\nambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'\n Only relevant for DatetimeIndex:\n\n - 'infer' will attempt to infer fall dst-transition hours based on\n order\n - bool-ndarray where True signifies a DST time, False designates\n a non-DST time (note that this flag is only applicable for\n ambiguous times)\n - 'NaT' will return NaT where there are ambiguous times\n - 'raise' will raise an AmbiguousTimeError if there are ambiguous\n times\n\n .. versionadded:: 0.24.0\n\nnonexistent : 'shift_forward', 'shift_backward, 'NaT', timedelta,\n default 'raise'\n A nonexistent time does not exist in a particular timezone\n where clocks moved forward due to DST.\n\n - 'shift_forward' will shift the nonexistent time forward to the\n closest existing time\n - 'shift_backward' will shift the nonexistent time backward to the\n closest existing time\n - 'NaT' will return NaT where there are nonexistent times\n - timedelta objects will shift nonexistent times by the timedelta\n - 'raise' will raise an NonExistentTimeError if there are\n nonexistent times\n\n .. versionadded:: 0.24.0\n\nReturns\n-------\nDatetimeIndex, TimedeltaIndex, or Series\n Index of the same type for a DatetimeIndex or TimedeltaIndex,\n or a Series with the same index for a Series.\n\nRaises\n------\nValueError if the `freq` cannot be converted.\n\nExamples\n--------\n**DatetimeIndex**\n\n>>> rng = pd.date_range('1/1/2018 11:59:00', periods=3, freq='min')\n>>> rng\nDatetimeIndex(['2018-01-01 11:59:00', '2018-01-01 12:00:00',\n '2018-01-01 12:01:00'],\n dtype='datetime64[ns]', freq='T')\n>>> rng.ceil('H')\nDatetimeIndex(['2018-01-01 12:00:00', '2018-01-01 12:00:00',\n '2018-01-01 13:00:00'],\n dtype='datetime64[ns]', freq=None)\n\n**Series**\n\n>>> pd.Series(rng).dt.ceil(\"H\")\n0 2018-01-01 12:00:00\n1 2018-01-01 12:00:00\n2 2018-01-01 13:00:00\ndtype: datetime64[ns]", "deprecated": false, "file": "pandas/core/arrays/datetimelike.py", "file_line": 316, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/arrays/datetimelike.py#L316", "errors": [["PR09", "Parameter \"ambiguous\" description should finish with \".\""], ["PR08", "Parameter \"nonexistent\" description should start with a capital letter"], ["PR09", "Parameter \"nonexistent\" description should finish with \".\""]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "", "in_api": true, "section": "TimedeltaIndex", "subsection": "Conversion", "shared_code_with": ""}, "pandas.TimedeltaIndex.to_frame": {"type": "function", "docstring": "Create a DataFrame with a column containing the Index.\n\n.. versionadded:: 0.24.0\n\nParameters\n----------\nindex : boolean, default True\n Set the index of the returned DataFrame as the original Index.\n\nname : object, default None\n The passed name should substitute for the index name (if it has\n one).\n\nReturns\n-------\nDataFrame\n DataFrame containing the original Index data.\n\nSee Also\n--------\nIndex.to_series : Convert an Index to a Series.\nSeries.to_frame : Convert Series to DataFrame.\n\nExamples\n--------\n>>> idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal')\n>>> idx.to_frame()\n animal\nanimal\nAnt Ant\nBear Bear\nCow Cow\n\nBy default, the original Index is reused. To enforce a new Index:\n\n>>> idx.to_frame(index=False)\n animal\n0 Ant\n1 Bear\n2 Cow\n\nTo override the name of the resulting column, specify `name`:\n\n>>> idx.to_frame(index=False, name='zoo')\n zoo\n0 Ant\n1 Bear\n2 Cow", "deprecated": false, "file": "pandas/core/indexes/base.py", "file_line": 1153, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/base.py#L1153", "errors": [["PR06", "Parameter \"index\" type should use \"bool\" instead of \"boolean\""]], "warnings": [], "examples_errors": "", "in_api": true, "section": "TimedeltaIndex", "subsection": "Conversion", "shared_code_with": "pandas.DatetimeIndex.to_frame"}, "pandas.PeriodIndex": {"type": "type", "docstring": "Immutable ndarray holding ordinal values indicating regular periods in\ntime such as particular years, quarters, months, etc.\n\nIndex keys are boxed to Period objects which carries the metadata (eg,\nfrequency information).\n\nParameters\n----------\ndata : array-like (1-dimensional), optional\n Optional period-like data to construct index with\ncopy : bool\n Make a copy of input ndarray\nfreq : string or period object, optional\n One of pandas period strings or corresponding objects\nstart : starting value, period-like, optional\n If data is None, used as the start point in generating regular\n period data.\n\n .. deprecated:: 0.24.0\n\nperiods : int, optional, > 0\n Number of periods to generate, if generating index. Takes precedence\n over end argument\n\n .. deprecated:: 0.24.0\n\nend : end value, period-like, optional\n If periods is none, generated index will extend to first conforming\n period on or just past end argument\n\n .. deprecated:: 0.24.0\n\nyear : int, array, or Series, default None\nmonth : int, array, or Series, default None\nquarter : int, array, or Series, default None\nday : int, array, or Series, default None\nhour : int, array, or Series, default None\nminute : int, array, or Series, default None\nsecond : int, array, or Series, default None\ntz : object, default None\n Timezone for converting datetime64 data to Periods\ndtype : str or PeriodDtype, default None\n\nAttributes\n----------\nday\ndayofweek\ndayofyear\ndays_in_month\ndaysinmonth\nend_time\nfreq\nfreqstr\nhour\nis_leap_year\nminute\nmonth\nquarter\nqyear\nsecond\nstart_time\nweek\nweekday\nweekofyear\nyear\n\nMethods\n-------\nasfreq\nstrftime\nto_timestamp\n\nNotes\n-----\nCreating a PeriodIndex based on `start`, `periods`, and `end` has\nbeen deprecated in favor of :func:`period_range`.\n\nExamples\n--------\n>>> idx = pd.PeriodIndex(year=year_arr, quarter=q_arr)\n\nSee Also\n---------\nIndex : The base pandas Index type.\nPeriod : Represents a period of time.\nDatetimeIndex : Index with datetime64 data.\nTimedeltaIndex : Index of timedelta64 data.\nperiod_range : Create a fixed-frequency PeriodIndex.", "deprecated": false, "file": "pandas/core/indexes/period.py", "file_line": 75, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/period.py#L75", "errors": [["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {**fields, ordinal, name} not documented"], ["PR02", "Unknown parameters {minute, day, quarter, month, hour, year, second}"], ["PR09", "Parameter \"data\" description should finish with \".\""], ["PR09", "Parameter \"copy\" description should finish with \".\""], ["PR06", "Parameter \"freq\" type should use \"str\" instead of \"string\""], ["PR09", "Parameter \"freq\" description should finish with \".\""], ["PR09", "Parameter \"periods\" description should finish with \".\""], ["PR09", "Parameter \"end\" description should finish with \".\""], ["PR07", "Parameter \"year\" has no description"], ["PR07", "Parameter \"month\" has no description"], ["PR07", "Parameter \"quarter\" has no description"], ["PR07", "Parameter \"day\" has no description"], ["PR07", "Parameter \"hour\" has no description"], ["PR07", "Parameter \"minute\" has no description"], ["PR07", "Parameter \"second\" has no description"], ["PR09", "Parameter \"tz\" description should finish with \".\""], ["PR07", "Parameter \"dtype\" has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 81, in pandas.PeriodIndex\nFailed example:\n idx = pd.PeriodIndex(year=year_arr, quarter=q_arr)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.PeriodIndex[0]>\", line 1, in <module>\n idx = pd.PeriodIndex(year=year_arr, quarter=q_arr)\n NameError: name 'year_arr' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'year_arr' (2 times)"]], "warnings": [], "examples_errors": "**********************************************************************\nLine 81, in pandas.PeriodIndex\nFailed example:\n idx = pd.PeriodIndex(year=year_arr, quarter=q_arr)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.PeriodIndex[0]>\", line 1, in <module>\n idx = pd.PeriodIndex(year=year_arr, quarter=q_arr)\n NameError: name 'year_arr' is not defined\n", "in_api": true, "section": "PeriodIndex", "subsection": "Conversion", "shared_code_with": ""}, "pandas.PeriodIndex.day": {"type": "property", "docstring": "The days of the period", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.TimedeltaIndex.components"}, "pandas.PeriodIndex.dayofweek": {"type": "property", "docstring": "The day of the week with Monday=0, Sunday=6", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.day"}, "pandas.PeriodIndex.dayofyear": {"type": "property", "docstring": "The ordinal day of the year", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.dayofweek"}, "pandas.PeriodIndex.days_in_month": {"type": "property", "docstring": "The number of days in the month", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.dayofyear"}, "pandas.PeriodIndex.daysinmonth": {"type": "property", "docstring": "The number of days in the month", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.days_in_month"}, "pandas.PeriodIndex.end_time": {"type": "property", "docstring": "", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.daysinmonth"}, "pandas.PeriodIndex.freq": {"type": "property", "docstring": "Return the frequency object if it is set, otherwise None.", "deprecated": false, "file": "pandas/core/indexes/period.py", "file_line": 287, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/period.py#L287", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": ""}, "pandas.PeriodIndex.freqstr": {"type": "property", "docstring": "Return the frequency object as a string if it is set, otherwise None.", "deprecated": false, "file": "pandas/core/indexes/datetimelike.py", "file_line": 88, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/indexes/datetimelike.py#L88", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.DatetimeIndex.freqstr"}, "pandas.PeriodIndex.hour": {"type": "property", "docstring": "The hour of the period", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.end_time"}, "pandas.PeriodIndex.is_leap_year": {"type": "property", "docstring": "Logical indicating if the date belongs to a leap year", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.hour"}, "pandas.PeriodIndex.minute": {"type": "property", "docstring": "The minute of the period", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.is_leap_year"}, "pandas.PeriodIndex.month": {"type": "property", "docstring": "The month as January=1, December=12", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.minute"}, "pandas.PeriodIndex.quarter": {"type": "property", "docstring": "The quarter of the date", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.month"}, "pandas.PeriodIndex.qyear": {"type": "property", "docstring": "", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.quarter"}, "pandas.PeriodIndex.second": {"type": "property", "docstring": "The second of the period", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.qyear"}, "pandas.PeriodIndex.start_time": {"type": "property", "docstring": "", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL08", "The object does not have a docstring"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.second"}, "pandas.PeriodIndex.week": {"type": "property", "docstring": "The week ordinal of the year", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.start_time"}, "pandas.PeriodIndex.weekday": {"type": "property", "docstring": "The day of the week with Monday=0, Sunday=6", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.week"}, "pandas.PeriodIndex.weekofyear": {"type": "property", "docstring": "The week ordinal of the year", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.weekday"}, "pandas.PeriodIndex.year": {"type": "property", "docstring": "The year of the period", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 80, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L80", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Properties", "shared_code_with": "pandas.PeriodIndex.weekofyear"}, "pandas.PeriodIndex.asfreq": {"type": "function", "docstring": "Convert the Period Array/Index to the specified frequency `freq`.\n\nParameters\n----------\nfreq : str\n a frequency\nhow : str {'E', 'S'}\n 'E', 'END', or 'FINISH' for end,\n 'S', 'START', or 'BEGIN' for start.\n Whether the elements should be aligned to the end\n or start within pa period. January 31st ('END') vs.\n January 1st ('START') for example.\n\nReturns\n-------\nnew : Period Array/Index with the new frequency\n\nExamples\n--------\n>>> pidx = pd.period_range('2010-01-01', '2015-01-01', freq='A')\n>>> pidx\n<class 'pandas.core.indexes.period.PeriodIndex'>\n[2010, ..., 2015]\nLength: 6, Freq: A-DEC\n\n>>> pidx.asfreq('M')\n<class 'pandas.core.indexes.period.PeriodIndex'>\n[2010-12, ..., 2015-12]\nLength: 6, Freq: M\n\n>>> pidx.asfreq('M', how='S')\n<class 'pandas.core.indexes.period.PeriodIndex'>\n[2010-01, ..., 2015-01]\nLength: 6, Freq: M", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {freq, how}"], ["PR08", "Parameter \"freq\" description should start with a capital letter"], ["PR09", "Parameter \"freq\" description should finish with \".\""], ["PR08", "Parameter \"how\" description should start with a capital letter"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 22, in pandas.PeriodIndex.asfreq\nFailed example:\n pidx\nExpected:\n <class 'pandas.core.indexes.period.PeriodIndex'>\n [2010, ..., 2015]\n Length: 6, Freq: A-DEC\nGot:\n PeriodIndex(['2010', '2011', '2012', '2013', '2014', '2015'], dtype='period[A-DEC]', freq='A-DEC')\n**********************************************************************\nLine 27, in pandas.PeriodIndex.asfreq\nFailed example:\n pidx.asfreq('M')\nExpected:\n <class 'pandas.core.indexes.period.PeriodIndex'>\n [2010-12, ..., 2015-12]\n Length: 6, Freq: M\nGot:\n PeriodIndex(['2010-12', '2011-12', '2012-12', '2013-12', '2014-12', '2015-12'], dtype='period[M]', freq='M')\n**********************************************************************\nLine 32, in pandas.PeriodIndex.asfreq\nFailed example:\n pidx.asfreq('M', how='S')\nExpected:\n <class 'pandas.core.indexes.period.PeriodIndex'>\n [2010-01, ..., 2015-01]\n Length: 6, Freq: M\nGot:\n PeriodIndex(['2010-01', '2011-01', '2012-01', '2013-01', '2014-01', '2015-01'], dtype='period[M]', freq='M')\n"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 22, in pandas.PeriodIndex.asfreq\nFailed example:\n pidx\nExpected:\n <class 'pandas.core.indexes.period.PeriodIndex'>\n [2010, ..., 2015]\n Length: 6, Freq: A-DEC\nGot:\n PeriodIndex(['2010', '2011', '2012', '2013', '2014', '2015'], dtype='period[A-DEC]', freq='A-DEC')\n**********************************************************************\nLine 27, in pandas.PeriodIndex.asfreq\nFailed example:\n pidx.asfreq('M')\nExpected:\n <class 'pandas.core.indexes.period.PeriodIndex'>\n [2010-12, ..., 2015-12]\n Length: 6, Freq: M\nGot:\n PeriodIndex(['2010-12', '2011-12', '2012-12', '2013-12', '2014-12', '2015-12'], dtype='period[M]', freq='M')\n**********************************************************************\nLine 32, in pandas.PeriodIndex.asfreq\nFailed example:\n pidx.asfreq('M', how='S')\nExpected:\n <class 'pandas.core.indexes.period.PeriodIndex'>\n [2010-01, ..., 2015-01]\n Length: 6, Freq: M\nGot:\n PeriodIndex(['2010-01', '2011-01', '2012-01', '2013-01', '2014-01', '2015-01'], dtype='period[M]', freq='M')\n", "in_api": true, "section": "PeriodIndex", "subsection": "Methods", "shared_code_with": "pandas.TimedeltaIndex.to_pytimedelta"}, "pandas.PeriodIndex.strftime": {"type": "function", "docstring": "Convert to Index using specified date_format.\n\nReturn an Index of formatted strings specified by date_format, which\nsupports the same string format as the python standard library. Details\nof the string format can be found in `python string format\ndoc <https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior>`__\n\nParameters\n----------\ndate_format : str\n Date format string (e.g. \"%Y-%m-%d\").\n\nReturns\n-------\nIndex\n Index of formatted strings\n\nSee Also\n--------\nto_datetime : Convert the given argument to datetime.\nDatetimeIndex.normalize : Return DatetimeIndex with times to midnight.\nDatetimeIndex.round : Round the DatetimeIndex to the specified freq.\nDatetimeIndex.floor : Floor the DatetimeIndex to the specified freq.\n\nExamples\n--------\n>>> rng = pd.date_range(pd.Timestamp(\"2018-03-10 09:00\"),\n... periods=3, freq='s')\n>>> rng.strftime('%B %d, %Y, %r')\nIndex(['March 10, 2018, 09:00:00 AM', 'March 10, 2018, 09:00:01 AM',\n 'March 10, 2018, 09:00:02 AM'],\n dtype='object')", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {date_format}"], ["RT05", "Return value description should finish with \".\""]], "warnings": [], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Methods", "shared_code_with": "pandas.PeriodIndex.asfreq"}, "pandas.PeriodIndex.to_timestamp": {"type": "function", "docstring": "Cast to DatetimeArray/Index.\n\nParameters\n----------\nfreq : string or DateOffset, optional\n Target frequency. The default is 'D' for week or longer,\n 'S' otherwise\nhow : {'s', 'e', 'start', 'end'}\n\nReturns\n-------\nDatetimeArray/Index", "deprecated": false, "file": "pandas/core/accessor.py", "file_line": 94, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/accessor.py#L94", "errors": [["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {freq, how}"], ["PR06", "Parameter \"freq\" type should use \"str\" instead of \"string\""], ["PR09", "Parameter \"freq\" description should finish with \".\""], ["PR07", "Parameter \"how\" has no description"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "PeriodIndex", "subsection": "Methods", "shared_code_with": "pandas.PeriodIndex.strftime"}, "pandas.core.resample.Resampler.__iter__": {"type": "function", "docstring": "Resampler iterator.\n\nReturns\n-------\nGenerator yielding sequence of (name, subsetted object)\nfor each group.\n\nSee Also\n--------\nGroupBy.__iter__", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 102, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L102", "errors": [["RT03", "Return value has no description"], ["RT03", "Return value has no description"], ["YD01", "No Yields section found"], ["SA04", "Missing description for See Also \"GroupBy.__iter__\" reference"]], "warnings": [["ES01", "No extended summary found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "", "subsection": "Indexing, iteration", "shared_code_with": ""}, "pandas.core.resample.Resampler.groups": {"type": "property", "docstring": "Dict {group name -> group labels}.", "deprecated": false, "file": "pandas/core/groupby/groupby.py", "file_line": 384, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/groupby.py#L384", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "", "subsection": "Indexing, iteration", "shared_code_with": ""}, "pandas.core.resample.Resampler.indices": {"type": "property", "docstring": "Dict {group name -> group indices}.", "deprecated": false, "file": "pandas/core/groupby/groupby.py", "file_line": 397, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/groupby.py#L397", "errors": [], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "", "subsection": "Indexing, iteration", "shared_code_with": ""}, "pandas.core.resample.Resampler.get_group": {"type": "function", "docstring": "Constructs NDFrame from group with provided name.\n\nParameters\n----------\nname : object\n the name of the group to get as a DataFrame\nobj : NDFrame, default None\n the NDFrame to take the DataFrame out of. If\n it is None, the object groupby was called on will\n be used\n\nReturns\n-------\ngroup : same type as obj", "deprecated": false, "file": "pandas/core/groupby/groupby.py", "file_line": 626, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/groupby/groupby.py#L626", "errors": [["GL04", "Private classes (NDFrame) should not be mentioned in public docstrings"], ["SS05", "Summary must start with infinitive verb, not third person (e.g. use \"Generate\" instead of \"Generates\")"], ["PR08", "Parameter \"name\" description should start with a capital letter"], ["PR09", "Parameter \"name\" description should finish with \".\""], ["PR08", "Parameter \"obj\" description should start with a capital letter"], ["PR09", "Parameter \"obj\" description should finish with \".\""], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "", "subsection": "Indexing, iteration", "shared_code_with": ""}, "pandas.core.resample.Resampler.apply": {"type": "function", "docstring": "Aggregate using one or more operations over the specified axis.\n\n\n\nParameters\n----------\nfunc : function, str, list or dict\n Function to use for aggregating the data. If a function, must either\n work when passed a DataFrame or when passed to DataFrame.apply.\n\n Accepted combinations are:\n\n - function\n - string function name\n - list of functions and/or function names, e.g. ``[np.sum, 'mean']``\n - dict of axis labels -> functions, function names or list of such.\n\n*args\n Positional arguments to pass to `func`.\n**kwargs\n Keyword arguments to pass to `func`.\n\nReturns\n-------\nDataFrame, Series or scalar\n if DataFrame.agg is called with a single function, returns a Series\n if DataFrame.agg is called with several functions, returns a DataFrame\n if Series.agg is called with single function, returns a scalar\n if Series.agg is called with several functions, returns a Series\n\n\nSee Also\n--------\npandas.DataFrame.groupby.aggregate\npandas.DataFrame.resample.transform\npandas.DataFrame.aggregate\n\n\nNotes\n-----\n`agg` is an alias for `aggregate`. Use the alias.\n\nA passed user-defined-function will be passed a Series for evaluation.\n\n\nExamples\n--------\n>>> s = pd.Series([1,2,3,4,5],\n index=pd.date_range('20130101', periods=5,freq='s'))\n2013-01-01 00:00:00 1\n2013-01-01 00:00:01 2\n2013-01-01 00:00:02 3\n2013-01-01 00:00:03 4\n2013-01-01 00:00:04 5\nFreq: S, dtype: int64\n\n>>> r = s.resample('2s')\nDatetimeIndexResampler [freq=<2 * Seconds>, axis=0, closed=left,\n label=left, convention=start, base=0]\n\n>>> r.agg(np.sum)\n2013-01-01 00:00:00 3\n2013-01-01 00:00:02 7\n2013-01-01 00:00:04 5\nFreq: 2S, dtype: int64\n\n>>> r.agg(['sum','mean','max'])\n sum mean max\n2013-01-01 00:00:00 3 1.5 2\n2013-01-01 00:00:02 7 3.5 4\n2013-01-01 00:00:04 5 5.0 5\n\n>>> r.agg({'result' : lambda x: x.mean() / x.std(),\n 'total' : np.sum})\n total result\n2013-01-01 00:00:00 3 2.121320\n2013-01-01 00:00:02 7 4.949747\n2013-01-01 00:00:04 5 NaN", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 257, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L257", "errors": [["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"], ["RT04", "Return value description should start with a capital letter"], ["RT05", "Return value description should finish with \".\""], ["SA04", "Missing description for See Also \"pandas.DataFrame.groupby.aggregate\" reference"], ["SA05", "pandas.DataFrame.groupby.aggregate in `See Also` section does not need `pandas` prefix, use DataFrame.groupby.aggregate instead."], ["SA04", "Missing description for See Also \"pandas.DataFrame.resample.transform\" reference"], ["SA05", "pandas.DataFrame.resample.transform in `See Also` section does not need `pandas` prefix, use DataFrame.resample.transform instead."], ["SA04", "Missing description for See Also \"pandas.DataFrame.aggregate\" reference"], ["SA05", "pandas.DataFrame.aggregate in `See Also` section does not need `pandas` prefix, use DataFrame.aggregate instead."], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 49, in pandas.core.resample.Resampler.apply\nFailed example:\n s = pd.Series([1,2,3,4,5],\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.apply[0]>\", line 1\n s = pd.Series([1,2,3,4,5],\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 58, in pandas.core.resample.Resampler.apply\nFailed example:\n r = s.resample('2s')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.apply[1]>\", line 1, in <module>\n r = s.resample('2s')\n NameError: name 's' is not defined\n**********************************************************************\nLine 62, in pandas.core.resample.Resampler.apply\nFailed example:\n r.agg(np.sum)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.apply[2]>\", line 1, in <module>\n r.agg(np.sum)\n NameError: name 'r' is not defined\n**********************************************************************\nLine 68, in pandas.core.resample.Resampler.apply\nFailed example:\n r.agg(['sum','mean','max'])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.apply[3]>\", line 1, in <module>\n r.agg(['sum','mean','max'])\n NameError: name 'r' is not defined\n**********************************************************************\nLine 74, in pandas.core.resample.Resampler.apply\nFailed example:\n r.agg({'result' : lambda x: x.mean() / x.std(),\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.apply[4]>\", line 1\n r.agg({'result' : lambda x: x.mean() / x.std(),\n ^\n SyntaxError: unexpected EOF while parsing\n"], ["EX03", "flake8 error: E902 TokenError: EOF in multi-line statement"], ["EX03", "flake8 error: E999 SyntaxError: invalid syntax"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "**********************************************************************\nLine 49, in pandas.core.resample.Resampler.apply\nFailed example:\n s = pd.Series([1,2,3,4,5],\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.apply[0]>\", line 1\n s = pd.Series([1,2,3,4,5],\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 58, in pandas.core.resample.Resampler.apply\nFailed example:\n r = s.resample('2s')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.apply[1]>\", line 1, in <module>\n r = s.resample('2s')\n NameError: name 's' is not defined\n**********************************************************************\nLine 62, in pandas.core.resample.Resampler.apply\nFailed example:\n r.agg(np.sum)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.apply[2]>\", line 1, in <module>\n r.agg(np.sum)\n NameError: name 'r' is not defined\n**********************************************************************\nLine 68, in pandas.core.resample.Resampler.apply\nFailed example:\n r.agg(['sum','mean','max'])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.apply[3]>\", line 1, in <module>\n r.agg(['sum','mean','max'])\n NameError: name 'r' is not defined\n**********************************************************************\nLine 74, in pandas.core.resample.Resampler.apply\nFailed example:\n r.agg({'result' : lambda x: x.mean() / x.std(),\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.apply[4]>\", line 1\n r.agg({'result' : lambda x: x.mean() / x.std(),\n ^\n SyntaxError: unexpected EOF while parsing\n", "in_api": true, "section": "", "subsection": "Function application", "shared_code_with": ""}, "pandas.core.resample.Resampler.aggregate": {"type": "function", "docstring": "Aggregate using one or more operations over the specified axis.\n\n\n\nParameters\n----------\nfunc : function, str, list or dict\n Function to use for aggregating the data. If a function, must either\n work when passed a DataFrame or when passed to DataFrame.apply.\n\n Accepted combinations are:\n\n - function\n - string function name\n - list of functions and/or function names, e.g. ``[np.sum, 'mean']``\n - dict of axis labels -> functions, function names or list of such.\n\n*args\n Positional arguments to pass to `func`.\n**kwargs\n Keyword arguments to pass to `func`.\n\nReturns\n-------\nDataFrame, Series or scalar\n if DataFrame.agg is called with a single function, returns a Series\n if DataFrame.agg is called with several functions, returns a DataFrame\n if Series.agg is called with single function, returns a scalar\n if Series.agg is called with several functions, returns a Series\n\n\nSee Also\n--------\npandas.DataFrame.groupby.aggregate\npandas.DataFrame.resample.transform\npandas.DataFrame.aggregate\n\n\nNotes\n-----\n`agg` is an alias for `aggregate`. Use the alias.\n\nA passed user-defined-function will be passed a Series for evaluation.\n\n\nExamples\n--------\n>>> s = pd.Series([1,2,3,4,5],\n index=pd.date_range('20130101', periods=5,freq='s'))\n2013-01-01 00:00:00 1\n2013-01-01 00:00:01 2\n2013-01-01 00:00:02 3\n2013-01-01 00:00:03 4\n2013-01-01 00:00:04 5\nFreq: S, dtype: int64\n\n>>> r = s.resample('2s')\nDatetimeIndexResampler [freq=<2 * Seconds>, axis=0, closed=left,\n label=left, convention=start, base=0]\n\n>>> r.agg(np.sum)\n2013-01-01 00:00:00 3\n2013-01-01 00:00:02 7\n2013-01-01 00:00:04 5\nFreq: 2S, dtype: int64\n\n>>> r.agg(['sum','mean','max'])\n sum mean max\n2013-01-01 00:00:00 3 1.5 2\n2013-01-01 00:00:02 7 3.5 4\n2013-01-01 00:00:04 5 5.0 5\n\n>>> r.agg({'result' : lambda x: x.mean() / x.std(),\n 'total' : np.sum})\n total result\n2013-01-01 00:00:00 3 2.121320\n2013-01-01 00:00:02 7 4.949747\n2013-01-01 00:00:04 5 NaN", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 257, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L257", "errors": [["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"], ["RT04", "Return value description should start with a capital letter"], ["RT05", "Return value description should finish with \".\""], ["SA04", "Missing description for See Also \"pandas.DataFrame.groupby.aggregate\" reference"], ["SA05", "pandas.DataFrame.groupby.aggregate in `See Also` section does not need `pandas` prefix, use DataFrame.groupby.aggregate instead."], ["SA04", "Missing description for See Also \"pandas.DataFrame.resample.transform\" reference"], ["SA05", "pandas.DataFrame.resample.transform in `See Also` section does not need `pandas` prefix, use DataFrame.resample.transform instead."], ["SA04", "Missing description for See Also \"pandas.DataFrame.aggregate\" reference"], ["SA05", "pandas.DataFrame.aggregate in `See Also` section does not need `pandas` prefix, use DataFrame.aggregate instead."], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 49, in pandas.core.resample.Resampler.aggregate\nFailed example:\n s = pd.Series([1,2,3,4,5],\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.aggregate[0]>\", line 1\n s = pd.Series([1,2,3,4,5],\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 58, in pandas.core.resample.Resampler.aggregate\nFailed example:\n r = s.resample('2s')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.aggregate[1]>\", line 1, in <module>\n r = s.resample('2s')\n NameError: name 's' is not defined\n**********************************************************************\nLine 62, in pandas.core.resample.Resampler.aggregate\nFailed example:\n r.agg(np.sum)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.aggregate[2]>\", line 1, in <module>\n r.agg(np.sum)\n NameError: name 'r' is not defined\n**********************************************************************\nLine 68, in pandas.core.resample.Resampler.aggregate\nFailed example:\n r.agg(['sum','mean','max'])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.aggregate[3]>\", line 1, in <module>\n r.agg(['sum','mean','max'])\n NameError: name 'r' is not defined\n**********************************************************************\nLine 74, in pandas.core.resample.Resampler.aggregate\nFailed example:\n r.agg({'result' : lambda x: x.mean() / x.std(),\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.aggregate[4]>\", line 1\n r.agg({'result' : lambda x: x.mean() / x.std(),\n ^\n SyntaxError: unexpected EOF while parsing\n"], ["EX03", "flake8 error: E902 TokenError: EOF in multi-line statement"], ["EX03", "flake8 error: E999 SyntaxError: invalid syntax"]], "warnings": [["ES01", "No extended summary found"]], "examples_errors": "**********************************************************************\nLine 49, in pandas.core.resample.Resampler.aggregate\nFailed example:\n s = pd.Series([1,2,3,4,5],\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.aggregate[0]>\", line 1\n s = pd.Series([1,2,3,4,5],\n ^\n SyntaxError: unexpected EOF while parsing\n**********************************************************************\nLine 58, in pandas.core.resample.Resampler.aggregate\nFailed example:\n r = s.resample('2s')\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.aggregate[1]>\", line 1, in <module>\n r = s.resample('2s')\n NameError: name 's' is not defined\n**********************************************************************\nLine 62, in pandas.core.resample.Resampler.aggregate\nFailed example:\n r.agg(np.sum)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.aggregate[2]>\", line 1, in <module>\n r.agg(np.sum)\n NameError: name 'r' is not defined\n**********************************************************************\nLine 68, in pandas.core.resample.Resampler.aggregate\nFailed example:\n r.agg(['sum','mean','max'])\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.aggregate[3]>\", line 1, in <module>\n r.agg(['sum','mean','max'])\n NameError: name 'r' is not defined\n**********************************************************************\nLine 74, in pandas.core.resample.Resampler.aggregate\nFailed example:\n r.agg({'result' : lambda x: x.mean() / x.std(),\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.aggregate[4]>\", line 1\n r.agg({'result' : lambda x: x.mean() / x.std(),\n ^\n SyntaxError: unexpected EOF while parsing\n", "in_api": true, "section": "", "subsection": "Function application", "shared_code_with": "pandas.core.resample.Resampler.apply"}, "pandas.core.resample.Resampler.transform": {"type": "function", "docstring": "Call function producing a like-indexed Series on each group and return\na Series with the transformed values.\n\nParameters\n----------\narg : function\n To apply to each group. Should return a Series with the same index.\n\nReturns\n-------\ntransformed : Series\n\nExamples\n--------\n>>> resampled.transform(lambda x: (x - x.mean()) / x.std())", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 281, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L281", "errors": [["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {**kwargs, *args} not documented"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 16, in pandas.core.resample.Resampler.transform\nFailed example:\n resampled.transform(lambda x: (x - x.mean()) / x.std())\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.transform[0]>\", line 1, in <module>\n resampled.transform(lambda x: (x - x.mean()) / x.std())\n NameError: name 'resampled' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'resampled'"]], "warnings": [["SA01", "See Also section not found"]], "examples_errors": "**********************************************************************\nLine 16, in pandas.core.resample.Resampler.transform\nFailed example:\n resampled.transform(lambda x: (x - x.mean()) / x.std())\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.transform[0]>\", line 1, in <module>\n resampled.transform(lambda x: (x - x.mean()) / x.std())\n NameError: name 'resampled' is not defined\n", "in_api": true, "section": "", "subsection": "Function application", "shared_code_with": ""}, "pandas.core.resample.Resampler.pipe": {"type": "function", "docstring": "Apply a function `func` with arguments to this Resampler object and return\nthe function's result.\n\n.. versionadded:: 0.23.0\n\nUse `.pipe` when you want to improve readability by chaining together\nfunctions that expect Series, DataFrames, GroupBy or Resampler objects.\nInstead of writing\n\n>>> h(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c)\n\nYou can write\n\n>>> (df.groupby('group')\n... .pipe(f)\n... .pipe(g, arg1=a)\n... .pipe(h, arg2=b, arg3=c))\n\nwhich is much more readable.\n\nParameters\n----------\nfunc : callable or tuple of (callable, string)\n Function to apply to this Resampler object or, alternatively,\n a `(callable, data_keyword)` tuple where `data_keyword` is a\n string indicating the keyword of `callable` that expects the\n Resampler object.\nargs : iterable, optional\n positional arguments passed into `func`.\nkwargs : dict, optional\n a dictionary of keyword arguments passed into `func`.\n\nReturns\n-------\nobject : the return type of `func`.\n\nSee Also\n--------\npandas.Series.pipe : Apply a function with arguments to a series.\npandas.DataFrame.pipe: Apply a function with arguments to a dataframe.\napply : Apply function to each group instead of to the\n full Resampler object.\n\nNotes\n-----\nSee more `here\n<http://pandas.pydata.org/pandas-docs/stable/groupby.html#piping-function-calls>`_\n\nExamples\n--------\n\n >>> df = pd.DataFrame({'A': [1, 2, 3, 4]},\n ... index=pd.date_range('2012-08-02', periods=4))\n >>> df\n A\n 2012-08-02 1\n 2012-08-03 2\n 2012-08-04 3\n 2012-08-05 4\n\n To get the difference between each 2-day period's maximum and minimum\n value in one pass, you can do\n\n >>> df.resample('2D').pipe(lambda x: x.max() - x.min())\n A\n 2012-08-02 1\n 2012-08-04 1", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 189, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L189", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["GL03", "Double line break found; please use only one blank line to separate sections or paragraphs, and do not leave blank lines at the end of docstrings"], ["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {**kwargs, *args} not documented"], ["PR02", "Unknown parameters {args, kwargs}"], ["PR06", "Parameter \"func\" type should use \"str\" instead of \"string\""], ["PR08", "Parameter \"args\" description should start with a capital letter"], ["PR08", "Parameter \"kwargs\" description should start with a capital letter"], ["RT02", "The first line of the Returns section should contain only the type, unless multiple values are being returned"], ["RT03", "Return value has no description"], ["SA05", "pandas.Series.pipe in `See Also` section does not need `pandas` prefix, use Series.pipe instead."], ["SA05", "pandas.DataFrame.pipe in `See Also` section does not need `pandas` prefix, use DataFrame.pipe instead."], ["EX02", "Examples do not pass tests:\n**********************************************************************\nLine 10, in pandas.core.resample.Resampler.pipe\nFailed example:\n h(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.pipe[0]>\", line 1, in <module>\n h(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c)\n NameError: name 'h' is not defined\n**********************************************************************\nLine 14, in pandas.core.resample.Resampler.pipe\nFailed example:\n (df.groupby('group')\n .pipe(f)\n .pipe(g, arg1=a)\n .pipe(h, arg2=b, arg3=c))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.pipe[1]>\", line 1, in <module>\n (df.groupby('group')\n NameError: name 'df' is not defined\n"], ["EX03", "flake8 error: F821 undefined name 'h' (14 times)"]], "warnings": [], "examples_errors": "**********************************************************************\nLine 10, in pandas.core.resample.Resampler.pipe\nFailed example:\n h(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c)\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.pipe[0]>\", line 1, in <module>\n h(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c)\n NameError: name 'h' is not defined\n**********************************************************************\nLine 14, in pandas.core.resample.Resampler.pipe\nFailed example:\n (df.groupby('group')\n .pipe(f)\n .pipe(g, arg1=a)\n .pipe(h, arg2=b, arg3=c))\nException raised:\n Traceback (most recent call last):\n File \"/home/stijn_vanhoey/.pyenv/versions/miniconda3-4.3.11/envs/pandas-dev/lib/python3.7/doctest.py\", line 1329, in __run\n compileflags, 1), test.globs)\n File \"<doctest pandas.core.resample.Resampler.pipe[1]>\", line 1, in <module>\n (df.groupby('group')\n NameError: name 'df' is not defined\n", "in_api": true, "section": "", "subsection": "Function application", "shared_code_with": ""}, "pandas.core.resample.Resampler.ffill": {"type": "function", "docstring": "Forward fill the values.\n\nParameters\n----------\nlimit : integer, optional\n limit of how many values to fill\n\nReturns\n-------\nAn upsampled Series.\n\nSee Also\n--------\nSeries.fillna\nDataFrame.fillna", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 414, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L414", "errors": [["PR06", "Parameter \"limit\" type should use \"int\" instead of \"integer\""], ["PR08", "Parameter \"limit\" description should start with a capital letter"], ["PR09", "Parameter \"limit\" description should finish with \".\""], ["RT03", "Return value has no description"], ["SA04", "Missing description for See Also \"Series.fillna\" reference"], ["SA04", "Missing description for See Also \"DataFrame.fillna\" reference"]], "warnings": [["ES01", "No extended summary found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "", "subsection": "Upsampling", "shared_code_with": ""}, "pandas.core.resample.Resampler.backfill": {"type": "function", "docstring": "Backward fill the new missing values in the resampled data.\n\nIn statistics, imputation is the process of replacing missing data with\nsubstituted values [1]_. When resampling data, missing values may\nappear (e.g., when the resampling frequency is higher than the original\nfrequency). The backward fill will replace NaN values that appeared in\nthe resampled data with the next value in the original sequence.\nMissing values that existed in the original data will not be modified.\n\nParameters\n----------\nlimit : integer, optional\n Limit of how many values to fill.\n\nReturns\n-------\nSeries, DataFrame\n An upsampled Series or DataFrame with backward filled NaN values.\n\nSee Also\n--------\nbfill : Alias of backfill.\nfillna : Fill NaN values using the specified method, which can be\n 'backfill'.\nnearest : Fill NaN values with nearest neighbor starting from center.\npad : Forward fill NaN values.\npandas.Series.fillna : Fill NaN values in the Series using the\n specified method, which can be 'backfill'.\npandas.DataFrame.fillna : Fill NaN values in the DataFrame using the\n specified method, which can be 'backfill'.\n\nReferences\n----------\n.. [1] https://en.wikipedia.org/wiki/Imputation_(statistics)\n\nExamples\n--------\n\nResampling a Series:\n\n>>> s = pd.Series([1, 2, 3],\n... index=pd.date_range('20180101', periods=3, freq='h'))\n>>> s\n2018-01-01 00:00:00 1\n2018-01-01 01:00:00 2\n2018-01-01 02:00:00 3\nFreq: H, dtype: int64\n\n>>> s.resample('30min').backfill()\n2018-01-01 00:00:00 1\n2018-01-01 00:30:00 2\n2018-01-01 01:00:00 2\n2018-01-01 01:30:00 3\n2018-01-01 02:00:00 3\nFreq: 30T, dtype: int64\n\n>>> s.resample('15min').backfill(limit=2)\n2018-01-01 00:00:00 1.0\n2018-01-01 00:15:00 NaN\n2018-01-01 00:30:00 2.0\n2018-01-01 00:45:00 2.0\n2018-01-01 01:00:00 2.0\n2018-01-01 01:15:00 NaN\n2018-01-01 01:30:00 3.0\n2018-01-01 01:45:00 3.0\n2018-01-01 02:00:00 3.0\nFreq: 15T, dtype: float64\n\nResampling a DataFrame that has missing values:\n\n>>> df = pd.DataFrame({'a': [2, np.nan, 6], 'b': [1, 3, 5]},\n... index=pd.date_range('20180101', periods=3,\n... freq='h'))\n>>> df\n a b\n2018-01-01 00:00:00 2.0 1\n2018-01-01 01:00:00 NaN 3\n2018-01-01 02:00:00 6.0 5\n\n>>> df.resample('30min').backfill()\n a b\n2018-01-01 00:00:00 2.0 1\n2018-01-01 00:30:00 NaN 3\n2018-01-01 01:00:00 NaN 3\n2018-01-01 01:30:00 6.0 5\n2018-01-01 02:00:00 6.0 5\n\n>>> df.resample('15min').backfill(limit=2)\n a b\n2018-01-01 00:00:00 2.0 1.0\n2018-01-01 00:15:00 NaN NaN\n2018-01-01 00:30:00 NaN 3.0\n2018-01-01 00:45:00 NaN 3.0\n2018-01-01 01:00:00 NaN 3.0\n2018-01-01 01:15:00 NaN NaN\n2018-01-01 01:30:00 6.0 5.0\n2018-01-01 01:45:00 6.0 5.0\n2018-01-01 02:00:00 6.0 5.0", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 497, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L497", "errors": [["PR06", "Parameter \"limit\" type should use \"int\" instead of \"integer\""], ["SA05", "pandas.Series.fillna in `See Also` section does not need `pandas` prefix, use Series.fillna instead."], ["SA05", "pandas.DataFrame.fillna in `See Also` section does not need `pandas` prefix, use DataFrame.fillna instead."]], "warnings": [], "examples_errors": "", "in_api": true, "section": "", "subsection": "Upsampling", "shared_code_with": ""}, "pandas.core.resample.Resampler.bfill": {"type": "function", "docstring": "Backward fill the new missing values in the resampled data.\n\nIn statistics, imputation is the process of replacing missing data with\nsubstituted values [1]_. When resampling data, missing values may\nappear (e.g., when the resampling frequency is higher than the original\nfrequency). The backward fill will replace NaN values that appeared in\nthe resampled data with the next value in the original sequence.\nMissing values that existed in the original data will not be modified.\n\nParameters\n----------\nlimit : integer, optional\n Limit of how many values to fill.\n\nReturns\n-------\nSeries, DataFrame\n An upsampled Series or DataFrame with backward filled NaN values.\n\nSee Also\n--------\nbfill : Alias of backfill.\nfillna : Fill NaN values using the specified method, which can be\n 'backfill'.\nnearest : Fill NaN values with nearest neighbor starting from center.\npad : Forward fill NaN values.\npandas.Series.fillna : Fill NaN values in the Series using the\n specified method, which can be 'backfill'.\npandas.DataFrame.fillna : Fill NaN values in the DataFrame using the\n specified method, which can be 'backfill'.\n\nReferences\n----------\n.. [1] https://en.wikipedia.org/wiki/Imputation_(statistics)\n\nExamples\n--------\n\nResampling a Series:\n\n>>> s = pd.Series([1, 2, 3],\n... index=pd.date_range('20180101', periods=3, freq='h'))\n>>> s\n2018-01-01 00:00:00 1\n2018-01-01 01:00:00 2\n2018-01-01 02:00:00 3\nFreq: H, dtype: int64\n\n>>> s.resample('30min').backfill()\n2018-01-01 00:00:00 1\n2018-01-01 00:30:00 2\n2018-01-01 01:00:00 2\n2018-01-01 01:30:00 3\n2018-01-01 02:00:00 3\nFreq: 30T, dtype: int64\n\n>>> s.resample('15min').backfill(limit=2)\n2018-01-01 00:00:00 1.0\n2018-01-01 00:15:00 NaN\n2018-01-01 00:30:00 2.0\n2018-01-01 00:45:00 2.0\n2018-01-01 01:00:00 2.0\n2018-01-01 01:15:00 NaN\n2018-01-01 01:30:00 3.0\n2018-01-01 01:45:00 3.0\n2018-01-01 02:00:00 3.0\nFreq: 15T, dtype: float64\n\nResampling a DataFrame that has missing values:\n\n>>> df = pd.DataFrame({'a': [2, np.nan, 6], 'b': [1, 3, 5]},\n... index=pd.date_range('20180101', periods=3,\n... freq='h'))\n>>> df\n a b\n2018-01-01 00:00:00 2.0 1\n2018-01-01 01:00:00 NaN 3\n2018-01-01 02:00:00 6.0 5\n\n>>> df.resample('30min').backfill()\n a b\n2018-01-01 00:00:00 2.0 1\n2018-01-01 00:30:00 NaN 3\n2018-01-01 01:00:00 NaN 3\n2018-01-01 01:30:00 6.0 5\n2018-01-01 02:00:00 6.0 5\n\n>>> df.resample('15min').backfill(limit=2)\n a b\n2018-01-01 00:00:00 2.0 1.0\n2018-01-01 00:15:00 NaN NaN\n2018-01-01 00:30:00 NaN 3.0\n2018-01-01 00:45:00 NaN 3.0\n2018-01-01 01:00:00 NaN 3.0\n2018-01-01 01:15:00 NaN NaN\n2018-01-01 01:30:00 6.0 5.0\n2018-01-01 01:45:00 6.0 5.0\n2018-01-01 02:00:00 6.0 5.0", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 497, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L497", "errors": [["PR06", "Parameter \"limit\" type should use \"int\" instead of \"integer\""], ["SA05", "pandas.Series.fillna in `See Also` section does not need `pandas` prefix, use Series.fillna instead."], ["SA05", "pandas.DataFrame.fillna in `See Also` section does not need `pandas` prefix, use DataFrame.fillna instead."]], "warnings": [], "examples_errors": "", "in_api": true, "section": "", "subsection": "Upsampling", "shared_code_with": "pandas.core.resample.Resampler.backfill"}, "pandas.core.resample.Resampler.pad": {"type": "function", "docstring": "Forward fill the values.\n\nParameters\n----------\nlimit : integer, optional\n limit of how many values to fill\n\nReturns\n-------\nAn upsampled Series.\n\nSee Also\n--------\nSeries.fillna\nDataFrame.fillna", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 414, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L414", "errors": [["PR06", "Parameter \"limit\" type should use \"int\" instead of \"integer\""], ["PR08", "Parameter \"limit\" description should start with a capital letter"], ["PR09", "Parameter \"limit\" description should finish with \".\""], ["RT03", "Return value has no description"], ["SA04", "Missing description for See Also \"Series.fillna\" reference"], ["SA04", "Missing description for See Also \"DataFrame.fillna\" reference"]], "warnings": [["ES01", "No extended summary found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "", "subsection": "Upsampling", "shared_code_with": "pandas.core.resample.Resampler.ffill"}, "pandas.core.resample.Resampler.nearest": {"type": "function", "docstring": "Resample by using the nearest value.\n\nWhen resampling data, missing values may appear (e.g., when the\nresampling frequency is higher than the original frequency).\nThe `nearest` method will replace ``NaN`` values that appeared in\nthe resampled data with the value from the nearest member of the\nsequence, based on the index value.\nMissing values that existed in the original data will not be modified.\nIf `limit` is given, fill only this many values in each direction for\neach of the original values.\n\nParameters\n----------\nlimit : int, optional\n Limit of how many values to fill.\n\n .. versionadded:: 0.21.0\n\nReturns\n-------\nSeries or DataFrame\n An upsampled Series or DataFrame with ``NaN`` values filled with\n their nearest value.\n\nSee Also\n--------\nbackfill : Backward fill the new missing values in the resampled data.\npad : Forward fill ``NaN`` values.\n\nExamples\n--------\n>>> s = pd.Series([1, 2],\n... index=pd.date_range('20180101',\n... periods=2,\n... freq='1h'))\n>>> s\n2018-01-01 00:00:00 1\n2018-01-01 01:00:00 2\nFreq: H, dtype: int64\n\n>>> s.resample('15min').nearest()\n2018-01-01 00:00:00 1\n2018-01-01 00:15:00 1\n2018-01-01 00:30:00 2\n2018-01-01 00:45:00 2\n2018-01-01 01:00:00 2\nFreq: 15T, dtype: int64\n\nLimit the number of upsampled values imputed by the nearest:\n\n>>> s.resample('15min').nearest(limit=1)\n2018-01-01 00:00:00 1.0\n2018-01-01 00:15:00 1.0\n2018-01-01 00:30:00 NaN\n2018-01-01 00:45:00 2.0\n2018-01-01 01:00:00 2.0\nFreq: 15T, dtype: float64", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 435, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L435", "errors": [], "warnings": [], "examples_errors": "", "in_api": true, "section": "", "subsection": "Upsampling", "shared_code_with": ""}, "pandas.core.resample.Resampler.fillna": {"type": "function", "docstring": "Fill missing values introduced by upsampling.\n\nIn statistics, imputation is the process of replacing missing data with\nsubstituted values [1]_. When resampling data, missing values may\nappear (e.g., when the resampling frequency is higher than the original\nfrequency).\n\nMissing values that existed in the original data will\nnot be modified.\n\nParameters\n----------\nmethod : {'pad', 'backfill', 'ffill', 'bfill', 'nearest'}\n Method to use for filling holes in resampled data\n\n * 'pad' or 'ffill': use previous valid observation to fill gap\n (forward fill).\n * 'backfill' or 'bfill': use next valid observation to fill gap.\n * 'nearest': use nearest valid observation to fill gap.\n\nlimit : integer, optional\n Limit of how many consecutive missing values to fill.\n\nReturns\n-------\nSeries or DataFrame\n An upsampled Series or DataFrame with missing values filled.\n\nSee Also\n--------\nbackfill : Backward fill NaN values in the resampled data.\npad : Forward fill NaN values in the resampled data.\nnearest : Fill NaN values in the resampled data\n with nearest neighbor starting from center.\ninterpolate : Fill NaN values using interpolation.\npandas.Series.fillna : Fill NaN values in the Series using the\n specified method, which can be 'bfill' and 'ffill'.\npandas.DataFrame.fillna : Fill NaN values in the DataFrame using the\n specified method, which can be 'bfill' and 'ffill'.\n\nReferences\n----------\n.. [1] https://en.wikipedia.org/wiki/Imputation_(statistics)\n\nExamples\n--------\nResampling a Series:\n\n>>> s = pd.Series([1, 2, 3],\n... index=pd.date_range('20180101', periods=3, freq='h'))\n>>> s\n2018-01-01 00:00:00 1\n2018-01-01 01:00:00 2\n2018-01-01 02:00:00 3\nFreq: H, dtype: int64\n\nWithout filling the missing values you get:\n\n>>> s.resample(\"30min\").asfreq()\n2018-01-01 00:00:00 1.0\n2018-01-01 00:30:00 NaN\n2018-01-01 01:00:00 2.0\n2018-01-01 01:30:00 NaN\n2018-01-01 02:00:00 3.0\nFreq: 30T, dtype: float64\n\n>>> s.resample('30min').fillna(\"backfill\")\n2018-01-01 00:00:00 1\n2018-01-01 00:30:00 2\n2018-01-01 01:00:00 2\n2018-01-01 01:30:00 3\n2018-01-01 02:00:00 3\nFreq: 30T, dtype: int64\n\n>>> s.resample('15min').fillna(\"backfill\", limit=2)\n2018-01-01 00:00:00 1.0\n2018-01-01 00:15:00 NaN\n2018-01-01 00:30:00 2.0\n2018-01-01 00:45:00 2.0\n2018-01-01 01:00:00 2.0\n2018-01-01 01:15:00 NaN\n2018-01-01 01:30:00 3.0\n2018-01-01 01:45:00 3.0\n2018-01-01 02:00:00 3.0\nFreq: 15T, dtype: float64\n\n>>> s.resample('30min').fillna(\"pad\")\n2018-01-01 00:00:00 1\n2018-01-01 00:30:00 1\n2018-01-01 01:00:00 2\n2018-01-01 01:30:00 2\n2018-01-01 02:00:00 3\nFreq: 30T, dtype: int64\n\n>>> s.resample('30min').fillna(\"nearest\")\n2018-01-01 00:00:00 1\n2018-01-01 00:30:00 2\n2018-01-01 01:00:00 2\n2018-01-01 01:30:00 3\n2018-01-01 02:00:00 3\nFreq: 30T, dtype: int64\n\nMissing values present before the upsampling are not affected.\n\n>>> sm = pd.Series([1, None, 3],\n... index=pd.date_range('20180101', periods=3, freq='h'))\n>>> sm\n2018-01-01 00:00:00 1.0\n2018-01-01 01:00:00 NaN\n2018-01-01 02:00:00 3.0\nFreq: H, dtype: float64\n\n>>> sm.resample('30min').fillna('backfill')\n2018-01-01 00:00:00 1.0\n2018-01-01 00:30:00 NaN\n2018-01-01 01:00:00 NaN\n2018-01-01 01:30:00 3.0\n2018-01-01 02:00:00 3.0\nFreq: 30T, dtype: float64\n\n>>> sm.resample('30min').fillna('pad')\n2018-01-01 00:00:00 1.0\n2018-01-01 00:30:00 1.0\n2018-01-01 01:00:00 NaN\n2018-01-01 01:30:00 NaN\n2018-01-01 02:00:00 3.0\nFreq: 30T, dtype: float64\n\n>>> sm.resample('30min').fillna('nearest')\n2018-01-01 00:00:00 1.0\n2018-01-01 00:30:00 NaN\n2018-01-01 01:00:00 NaN\n2018-01-01 01:30:00 3.0\n2018-01-01 02:00:00 3.0\nFreq: 30T, dtype: float64\n\nDataFrame resampling is done column-wise. All the same options are\navailable.\n\n>>> df = pd.DataFrame({'a': [2, np.nan, 6], 'b': [1, 3, 5]},\n... index=pd.date_range('20180101', periods=3,\n... freq='h'))\n>>> df\n a b\n2018-01-01 00:00:00 2.0 1\n2018-01-01 01:00:00 NaN 3\n2018-01-01 02:00:00 6.0 5\n\n>>> df.resample('30min').fillna(\"bfill\")\n a b\n2018-01-01 00:00:00 2.0 1\n2018-01-01 00:30:00 NaN 3\n2018-01-01 01:00:00 NaN 3\n2018-01-01 01:30:00 6.0 5\n2018-01-01 02:00:00 6.0 5", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 601, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L601", "errors": [["PR06", "Parameter \"limit\" type should use \"int\" instead of \"integer\""], ["SA05", "pandas.Series.fillna in `See Also` section does not need `pandas` prefix, use Series.fillna instead."], ["SA05", "pandas.DataFrame.fillna in `See Also` section does not need `pandas` prefix, use DataFrame.fillna instead."], ["EX03", "flake8 error: E128 continuation line under-indented for visual indent"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "", "subsection": "Upsampling", "shared_code_with": ""}, "pandas.core.resample.Resampler.asfreq": {"type": "function", "docstring": "Return the values at the new freq, essentially a reindex.\n\nParameters\n----------\nfill_value : scalar, optional\n Value to use for missing values, applied during upsampling (note\n this does not fill NaNs that already were present).\n\n .. versionadded:: 0.20.0\n\nSee Also\n--------\nSeries.asfreq\nDataFrame.asfreq", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 777, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L777", "errors": [["RT01", "No Returns section found"], ["SA04", "Missing description for See Also \"Series.asfreq\" reference"], ["SA04", "Missing description for See Also \"DataFrame.asfreq\" reference"]], "warnings": [["ES01", "No extended summary found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "", "subsection": "Upsampling", "shared_code_with": ""}, "pandas.core.resample.Resampler.interpolate": {"type": "function", "docstring": "Interpolate values according to different methods.\n\n.. versionadded:: 0.18.1\n\nPlease note that only ``method='linear'`` is supported for\nDataFrame/Series with a MultiIndex.\n\nParameters\n----------\nmethod : str, default 'linear'\n Interpolation technique to use. One of:\n\n * 'linear': Ignore the index and treat the values as equally\n spaced. This is the only method supported on MultiIndexes.\n * 'time': Works on daily and higher resolution data to interpolate\n given length of interval.\n * 'index', 'values': use the actual numerical values of the index.\n * 'pad': Fill in NaNs using existing values.\n * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'spline',\n 'barycentric', 'polynomial': Passed to\n `scipy.interpolate.interp1d`. Both 'polynomial' and 'spline'\n require that you also specify an `order` (int),\n e.g. ``df.interpolate(method='polynomial', order=5)``.\n These use the numerical values of the index.\n * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima':\n Wrappers around the SciPy interpolation methods of similar\n names. See `Notes`.\n * 'from_derivatives': Refers to\n `scipy.interpolate.BPoly.from_derivatives` which\n replaces 'piecewise_polynomial' interpolation method in\n scipy 0.18.\n\n .. versionadded:: 0.18.1\n\n Added support for the 'akima' method.\n Added interpolate method 'from_derivatives' which replaces\n 'piecewise_polynomial' in SciPy 0.18; backwards-compatible with\n SciPy < 0.18\n\naxis : {0 or 'index', 1 or 'columns', None}, default None\n Axis to interpolate along.\nlimit : int, optional\n Maximum number of consecutive NaNs to fill. Must be greater than\n 0.\ninplace : bool, default False\n Update the data in place if possible.\nlimit_direction : {'forward', 'backward', 'both'}, default 'forward'\n If limit is specified, consecutive NaNs will be filled in this\n direction.\nlimit_area : {`None`, 'inside', 'outside'}, default None\n If limit is specified, consecutive NaNs will be filled with this\n restriction.\n\n * ``None``: No fill restriction.\n * 'inside': Only fill NaNs surrounded by valid values\n (interpolate).\n * 'outside': Only fill NaNs outside valid values (extrapolate).\n\n .. versionadded:: 0.21.0\n\ndowncast : optional, 'infer' or None, defaults to None\n Downcast dtypes if possible.\n**kwargs\n Keyword arguments to pass on to the interpolating function.\n\nReturns\n-------\nSeries or DataFrame\n Returns the same object type as the caller, interpolated at\n some or all ``NaN`` values\n\nSee Also\n--------\nfillna : Fill missing values using different methods.\nscipy.interpolate.Akima1DInterpolator : Piecewise cubic polynomials\n (Akima interpolator).\nscipy.interpolate.BPoly.from_derivatives : Piecewise polynomial in the\n Bernstein basis.\nscipy.interpolate.interp1d : Interpolate a 1-D function.\nscipy.interpolate.KroghInterpolator : Interpolate polynomial (Krogh\n interpolator).\nscipy.interpolate.PchipInterpolator : PCHIP 1-d monotonic cubic\n interpolation.\nscipy.interpolate.CubicSpline : Cubic spline data interpolator.\n\nNotes\n-----\nThe 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima'\nmethods are wrappers around the respective SciPy implementations of\nsimilar names. These use the actual numerical values of the index.\nFor more information on their behavior, see the\n`SciPy documentation\n<http://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__\nand `SciPy tutorial\n<http://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html>`__.\n\nExamples\n--------\nFilling in ``NaN`` in a :class:`~pandas.Series` via linear\ninterpolation.\n\n>>> s = pd.Series([0, 1, np.nan, 3])\n>>> s\n0 0.0\n1 1.0\n2 NaN\n3 3.0\ndtype: float64\n>>> s.interpolate()\n0 0.0\n1 1.0\n2 2.0\n3 3.0\ndtype: float64\n\nFilling in ``NaN`` in a Series by padding, but filling at most two\nconsecutive ``NaN`` at a time.\n\n>>> s = pd.Series([np.nan, \"single_one\", np.nan,\n... \"fill_two_more\", np.nan, np.nan, np.nan,\n... 4.71, np.nan])\n>>> s\n0 NaN\n1 single_one\n2 NaN\n3 fill_two_more\n4 NaN\n5 NaN\n6 NaN\n7 4.71\n8 NaN\ndtype: object\n>>> s.interpolate(method='pad', limit=2)\n0 NaN\n1 single_one\n2 single_one\n3 fill_two_more\n4 fill_two_more\n5 fill_two_more\n6 NaN\n7 4.71\n8 4.71\ndtype: object\n\nFilling in ``NaN`` in a Series via polynomial interpolation or splines:\nBoth 'polynomial' and 'spline' methods require that you also specify\nan ``order`` (int).\n\n>>> s = pd.Series([0, 2, np.nan, 8])\n>>> s.interpolate(method='polynomial', order=2)\n0 0.000000\n1 2.000000\n2 4.666667\n3 8.000000\ndtype: float64\n\nFill the DataFrame forward (that is, going down) along each column\nusing linear interpolation.\n\nNote how the last entry in column 'a' is interpolated differently,\nbecause there is no entry after it to use for interpolation.\nNote how the first entry in column 'b' remains ``NaN``, because there\nis no entry befofe it to use for interpolation.\n\n>>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0),\n... (np.nan, 2.0, np.nan, np.nan),\n... (2.0, 3.0, np.nan, 9.0),\n... (np.nan, 4.0, -4.0, 16.0)],\n... columns=list('abcd'))\n>>> df\n a b c d\n0 0.0 NaN -1.0 1.0\n1 NaN 2.0 NaN NaN\n2 2.0 3.0 NaN 9.0\n3 NaN 4.0 -4.0 16.0\n>>> df.interpolate(method='linear', limit_direction='forward', axis=0)\n a b c d\n0 0.0 NaN -1.0 1.0\n1 1.0 2.0 -2.0 5.0\n2 2.0 3.0 -3.0 9.0\n3 2.0 4.0 -4.0 16.0\n\nUsing polynomial interpolation.\n\n>>> df['d'].interpolate(method='polynomial', order=2)\n0 1.0\n1 4.0\n2 9.0\n3 16.0\nName: d, dtype: float64", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 761, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L761", "errors": [["RT05", "Return value description should finish with \".\""], ["EX03", "flake8 error: E241 multiple spaces after ','"]], "warnings": [], "examples_errors": "", "in_api": true, "section": "", "subsection": "Upsampling", "shared_code_with": ""}, "pandas.core.resample.Resampler.count": {"type": "function", "docstring": "Compute count of group, excluding missing values.\n\nSee Also\n--------\npandas.Series.groupby\npandas.DataFrame.groupby\npandas.Panel.groupby", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 870, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L870", "errors": [["PR01", "Parameters {_method} not documented"], ["RT01", "No Returns section found"], ["SA04", "Missing description for See Also \"pandas.Series.groupby\" reference"], ["SA05", "pandas.Series.groupby in `See Also` section does not need `pandas` prefix, use Series.groupby instead."], ["SA04", "Missing description for See Also \"pandas.DataFrame.groupby\" reference"], ["SA05", "pandas.DataFrame.groupby in `See Also` section does not need `pandas` prefix, use DataFrame.groupby instead."], ["SA04", "Missing description for See Also \"pandas.Panel.groupby\" reference"], ["SA05", "pandas.Panel.groupby in `See Also` section does not need `pandas` prefix, use Panel.groupby instead."]], "warnings": [["ES01", "No extended summary found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "", "subsection": "Computations / Descriptive Stats", "shared_code_with": ""}, "pandas.core.resample.Resampler.nunique": {"type": "function", "docstring": "Returns number of unique elements in the group", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 877, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L877", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["GL02", "Closing quotes should be placed in the line after the last text in the docstring (do not close the quotes in the same line as the text, or leave a blank line between the last text and the quotes)"], ["SS03", "Summary does not end with a period"], ["SS05", "Summary must start with infinitive verb, not third person (e.g. use \"Generate\" instead of \"Generates\")"], ["PR01", "Parameters {_method} not documented"], ["RT01", "No Returns section found"]], "warnings": [["ES01", "No extended summary found"], ["SA01", "See Also section not found"], ["EX01", "No examples section found"]], "examples_errors": "", "in_api": true, "section": "", "subsection": "Computations / Descriptive Stats", "shared_code_with": ""}, "pandas.core.resample.Resampler.first": {"type": "function", "docstring": "Compute first of group values\nSee Also\n--------\npandas.Series.groupby\npandas.DataFrame.groupby\npandas.Panel.groupby", "deprecated": false, "file": "pandas/core/resample.py", "file_line": 862, "github_link": "https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py#L862", "errors": [["GL01", "Docstring text (summary) should start in the line immediately after the opening quotes (not in the same line, or leaving a blank line in between)"], ["SS03", "Summary does not end with a period"], ["SS06", "Summary should fit in a single line"], ["PR01", "Parameters {_method, **kwargs, *args} not documented"], ["RT01", "No Returns section found"]], "warnings": [["SA01", "See Also section not found"], ["EX01", "No examples se |
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