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December 2, 2020 20:46
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select_data.ipynb
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| { | |
| "cells": [ | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "## Select data\n### Functions:\n* iloc\n* sample\n* isin\n* where\n* query\n* not\n* get\n* lookup" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false, | |
| "collapsed": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import pandas as pd ", | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "#### create a test dataset to demo functions" | |
| }, | |
| { | |
| "metadata": { | |
| "slideshow": { | |
| "slide_type": "slide" | |
| }, | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df = pd.DataFrame(data = {'date':['week1','week1','week2','week3','week3','week3'],\n 'sales':[50,20,35,10,20,40],\n 'category':['a','b','a','a','b','a'],\n 'revenue':[14,8,16,None,7,9]\n })\ndf.date = df.date.astype('string')\ndf.category = df.category.astype('string')\ndf", | |
| "execution_count": 2, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>week1</td>\n <td>50</td>\n <td>a</td>\n <td>14.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>week1</td>\n <td>20</td>\n <td>b</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>week2</td>\n <td>35</td>\n <td>a</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>week3</td>\n <td>10</td>\n <td>a</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>week3</td>\n <td>20</td>\n <td>b</td>\n <td>7.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>week3</td>\n <td>40</td>\n <td>a</td>\n <td>9.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 week1 50 a 14.0\n1 week1 20 b 8.0\n2 week2 35 a 16.0\n3 week3 10 a NaN\n4 week3 20 b 7.0\n5 week3 40 a 9.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 2 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.info()", | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 6 entries, 0 to 5\nData columns (total 4 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 date 6 non-null string \n 1 sales 6 non-null int64 \n 2 category 6 non-null string \n 3 revenue 5 non-null float64\ndtypes: float64(1), int64(1), string(2)\nmemory usage: 320.0 bytes\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "### DataFrame.iloc[x]\n\nPurely integer-location based indexing for selection by position\n\nHere you are using square brackets since what you put in [ ] is list-like data, e.g 1:3 means index from 1 to 3\n\n#### example 1: \nselect value by integer index" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.iloc[0]", | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "date week1\nsales 50\ncategory a\nrevenue 14\nName: 0, dtype: object" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 4 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "#### example 2:\nrows from n to m (m is not included)" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.iloc[1:3]", | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>week1</td>\n <td>20</td>\n <td>b</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>week2</td>\n <td>35</td>\n <td>a</td>\n <td>16.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n1 week1 20 b 8.0\n2 week2 35 a 16.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 5 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "#### excercise: \nSelect the third row (index:2) to the last row (not include the last row). The last index can use -1 when you don't know how long the dataset" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.iloc[2:-1]", | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2</th>\n <td>week2</td>\n <td>35</td>\n <td>a</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>week3</td>\n <td>10</td>\n <td>a</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>week3</td>\n <td>20</td>\n <td>b</td>\n <td>7.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n2 week2 35 a 16.0\n3 week3 10 a NaN\n4 week3 20 b 7.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 6 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.iloc[2:]", | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2</th>\n <td>week2</td>\n <td>35</td>\n <td>a</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>week3</td>\n <td>10</td>\n <td>a</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>week3</td>\n <td>20</td>\n <td>b</td>\n <td>7.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>week3</td>\n <td>40</td>\n <td>a</td>\n <td>9.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n2 week2 35 a 16.0\n3 week3 10 a NaN\n4 week3 20 b 7.0\n5 week3 40 a 9.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "### FYI: Using . after a dataframe means you are accessing attributes of a DataFrame object in Pandas.\n* All the following functions are attribute fucntions of DataFrame. \n* All the functions in python are defined in a format: \n * function_name(Parameters)\n * Parameters can be optional" | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "### DataFrame.sample( n, frac, replace, weights, random_state, axis)\n\nReturn a random sample of items from an axis of object \n\nParameters:\n\n* n: int, optional. Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None.\n* frac: float, optioanl. Fraction of axis items to return. Cannot be used with n.\n* replace: bool, default False. Allow or disallow sampling of the same row more than once.\n* weights: str or ndarray-like, optional\n* random_state: int, array-like, BitGenerator, np.random.RandomState, optional. Used for reproducibility.\n* axis: {0 or ‘index’, 1 or ‘columns’, None}, default None\n\n#### example 1: \nGet n random rows" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.sample(n=3, random_state = 1)", | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2</th>\n <td>week2</td>\n <td>35</td>\n <td>a</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>week1</td>\n <td>20</td>\n <td>b</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>week3</td>\n <td>20</td>\n <td>b</td>\n <td>7.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n2 week2 35 a 16.0\n1 week1 20 b 8.0\n4 week3 20 b 7.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 8 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "#### example 2: \nGet 50% sample rows" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.sample(frac=0.5, random_state = 1)", | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2</th>\n <td>week2</td>\n <td>35</td>\n <td>a</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>week1</td>\n <td>20</td>\n <td>b</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>week3</td>\n <td>20</td>\n <td>b</td>\n <td>7.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n2 week2 35 a 16.0\n1 week1 20 b 8.0\n4 week3 20 b 7.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 9 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "#### example 3: \nUsing one column in the dataframe as weights. Rows with larger value in that column are more likely to be sampled." | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.sample(n=3, random_state = 1, weights = 'revenue')", | |
| "execution_count": 10, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2</th>\n <td>week2</td>\n <td>35</td>\n <td>a</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>week3</td>\n <td>20</td>\n <td>b</td>\n <td>7.0</td>\n </tr>\n <tr>\n <th>0</th>\n <td>week1</td>\n <td>50</td>\n <td>a</td>\n <td>14.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n2 week2 35 a 16.0\n4 week3 20 b 7.0\n0 week1 50 a 14.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 10 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "#### excercise: \nuse random_state =1 and get 2 sample rows from the dataset." | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.sample(n=2)", | |
| "execution_count": 11, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>3</th>\n <td>week3</td>\n <td>10</td>\n <td>a</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>5</th>\n <td>week3</td>\n <td>40</td>\n <td>a</td>\n <td>9.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n3 week3 10 a NaN\n5 week3 40 a 9.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 11 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "### DataFrame.isin(values)\n\nTo check whether each element in the dataframe is contained in values\n\nParamters: values\n* series or dict\n\nReturns: \n* dataframe of booleans" | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "#### example 1: values in a series" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.isin([10,20])", | |
| "execution_count": 12, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1</th>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>2</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>3</th>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>4</th>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>5</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 False False False False\n1 False True False False\n2 False False False False\n3 False True False False\n4 False True False False\n5 False False False False" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 12 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.category.isin(['a','b'])", | |
| "execution_count": 13, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "0 True\n1 True\n2 True\n3 True\n4 True\n5 True\nName: category, dtype: bool" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 13 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "pd.DataFrame(df['category'].isin(['a','b']))", | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>category</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>True</td>\n </tr>\n <tr>\n <th>1</th>\n <td>True</td>\n </tr>\n <tr>\n <th>2</th>\n <td>True</td>\n </tr>\n <tr>\n <th>3</th>\n <td>True</td>\n </tr>\n <tr>\n <th>4</th>\n <td>True</td>\n </tr>\n <tr>\n <th>5</th>\n <td>True</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " category\n0 True\n1 True\n2 True\n3 True\n4 True\n5 True" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 14 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "#### example 2: values in a dict" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df", | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>week1</td>\n <td>50</td>\n <td>a</td>\n <td>14.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>week1</td>\n <td>20</td>\n <td>b</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>week2</td>\n <td>35</td>\n <td>a</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>week3</td>\n <td>10</td>\n <td>a</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>week3</td>\n <td>20</td>\n <td>b</td>\n <td>7.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>week3</td>\n <td>40</td>\n <td>a</td>\n <td>9.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 week1 50 a 14.0\n1 week1 20 b 8.0\n2 week2 35 a 16.0\n3 week3 10 a NaN\n4 week3 20 b 7.0\n5 week3 40 a 9.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 15 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.isin({'sales':[10,20],\n 'category':['c','d']\n }\n )", | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1</th>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>2</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>3</th>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>4</th>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>5</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 False False False False\n1 False True False False\n2 False False False False\n3 False True False False\n4 False True False False\n5 False False False False" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 16 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "#### excercise: \ncheck whether the data is before week5. ['week1','week2','week3','week4']" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.isin({'date':['week1','week2','week3','week4']\n }\n )", | |
| "execution_count": 17, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1</th>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>2</th>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>3</th>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>4</th>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>5</th>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 True False False False\n1 True False False False\n2 True False False False\n3 True False False False\n4 True False False False\n5 True False False False" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 17 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "### DataFrame.where( cond, other = nan, ...)\n\nReplace values where the condition is False. This fucntion is an application of the if-then idiom.\n\nParameters:\n\n* cond:bool Series/DataFrame, array-like, or callable. Where cond is True, keep the original value. Where False, replace with corresponding value from other.\n* other: scalar, Series/DataFrame, or callable\n\n#### example 1: \nfind rows meet the condition and replace with 0" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.info()", | |
| "execution_count": 18, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 6 entries, 0 to 5\nData columns (total 4 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 date 6 non-null string \n 1 sales 6 non-null int64 \n 2 category 6 non-null string \n 3 revenue 5 non-null float64\ndtypes: float64(1), int64(1), string(2)\nmemory usage: 320.0 bytes\n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.sales.where(df['sales']>20,0)", | |
| "execution_count": 19, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "0 50\n1 0\n2 35\n3 0\n4 0\n5 40\nName: sales, dtype: int64" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 19 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "#### excercise: \nfind data for 'a' category and set data as None for other categories " | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false, | |
| "collapsed": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df_eg = df.copy()", | |
| "execution_count": 20, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df_eg['category'] = df['category'].where(df['category']=='a',None)\ndf_eg", | |
| "execution_count": 21, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>week1</td>\n <td>50</td>\n <td>a</td>\n <td>14.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>week1</td>\n <td>20</td>\n <td><NA></td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>week2</td>\n <td>35</td>\n <td>a</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>week3</td>\n <td>10</td>\n <td>a</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>week3</td>\n <td>20</td>\n <td><NA></td>\n <td>7.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>week3</td>\n <td>40</td>\n <td>a</td>\n <td>9.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 week1 50 a 14.0\n1 week1 20 <NA> 8.0\n2 week2 35 a 16.0\n3 week3 10 a NaN\n4 week3 20 <NA> 7.0\n5 week3 40 a 9.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 21 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "### DataFrame.query(expr)\nUsed to query the columns of a Dataframe with a boolean expression\n\nParameters: expr\n* query expresion in string format\n\nReturns: \n* DataFrame resulting from the provided query expression\n\n#### example 1:" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.query('sales > revenue')", | |
| "execution_count": 22, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>week1</td>\n <td>50</td>\n <td>a</td>\n <td>14.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>week1</td>\n <td>20</td>\n <td>b</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>week2</td>\n <td>35</td>\n <td>a</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>week3</td>\n <td>20</td>\n <td>b</td>\n <td>7.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>week3</td>\n <td>40</td>\n <td>a</td>\n <td>9.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 week1 50 a 14.0\n1 week1 20 b 8.0\n2 week2 35 a 16.0\n4 week3 20 b 7.0\n5 week3 40 a 9.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 22 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df[df.sales>df.revenue]", | |
| "execution_count": 23, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>week1</td>\n <td>50</td>\n <td>a</td>\n <td>14.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>week1</td>\n <td>20</td>\n <td>b</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>week2</td>\n <td>35</td>\n <td>a</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>week3</td>\n <td>20</td>\n <td>b</td>\n <td>7.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>week3</td>\n <td>40</td>\n <td>a</td>\n <td>9.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 week1 50 a 14.0\n1 week1 20 b 8.0\n2 week2 35 a 16.0\n4 week3 20 b 7.0\n5 week3 40 a 9.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 23 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "#### excercise: \nselect out data for category 'a' in week1. Symbol & is used for 'and' operation. | is for 'or'" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df[(df.category =='a') & (df.date =='week1')]", | |
| "execution_count": 24, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>week1</td>\n <td>50</td>\n <td>a</td>\n <td>14.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 week1 50 a 14.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 24 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "### DataFrame.notna()\n\nDetect existing (non-missing) values. opposite function is .isna()\n\nReturn a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. \n\nIn python, pandas.dataframe.notna() and pandas.dataframe.isna() are doing exactly same thing as pandas.dataframe.notnull() and pandas.dataframe.isnull()" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.notna()", | |
| "execution_count": 25, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n </tr>\n <tr>\n <th>1</th>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n </tr>\n <tr>\n <th>2</th>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n </tr>\n <tr>\n <th>3</th>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n <td>False</td>\n </tr>\n <tr>\n <th>4</th>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n </tr>\n <tr>\n <th>5</th>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 True True True True\n1 True True True True\n2 True True True True\n3 True True True False\n4 True True True True\n5 True True True True" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 25 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.isna()", | |
| "execution_count": 26, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>2</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>3</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>True</td>\n </tr>\n <tr>\n <th>4</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>5</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 False False False False\n1 False False False False\n2 False False False False\n3 False False False True\n4 False False False False\n5 False False False False" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 26 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.isnull()", | |
| "execution_count": 27, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>2</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>3</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>True</td>\n </tr>\n <tr>\n <th>4</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>5</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 False False False False\n1 False False False False\n2 False False False False\n3 False False False True\n4 False False False False\n5 False False False False" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 27 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "### Pandas.notna(obj)\n\nDetect non-missing values for an array-like object. Opposite fucntion pandas.isna(obj)\n\nsimilar function like pandas.isnull(), pandas.notnull()" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "pd.notna(df)", | |
| "execution_count": 28, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n </tr>\n <tr>\n <th>1</th>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n </tr>\n <tr>\n <th>2</th>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n </tr>\n <tr>\n <th>3</th>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n <td>False</td>\n </tr>\n <tr>\n <th>4</th>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n </tr>\n <tr>\n <th>5</th>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n <td>True</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 True True True True\n1 True True True True\n2 True True True True\n3 True True True False\n4 True True True True\n5 True True True True" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 28 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "pd.isna(df)", | |
| "execution_count": 29, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>2</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>3</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>True</td>\n </tr>\n <tr>\n <th>4</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>5</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 False False False False\n1 False False False False\n2 False False False False\n3 False False False True\n4 False False False False\n5 False False False False" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 29 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "### DataFrame.get(key)\nGet item from object for given key, same function as df.['key'] and df.key\n\nParameters: key\n* query expresion in string format\n\nReturns: \n* value same type as items contained in object\n\n#### example :" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.get('sales')", | |
| "execution_count": 30, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "0 50\n1 20\n2 35\n3 10\n4 20\n5 40\nName: sales, dtype: int64" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 30 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.get(['sales','revenue'])", | |
| "execution_count": 31, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>sales</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>50</td>\n <td>14.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>20</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>35</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>10</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>20</td>\n <td>7.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>40</td>\n <td>9.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " sales revenue\n0 50 14.0\n1 20 8.0\n2 35 16.0\n3 10 NaN\n4 20 7.0\n5 40 9.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 31 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df[['sales','revenue','date']]", | |
| "execution_count": 32, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>sales</th>\n <th>revenue</th>\n <th>date</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>50</td>\n <td>14.0</td>\n <td>week1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>20</td>\n <td>8.0</td>\n <td>week1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>35</td>\n <td>16.0</td>\n <td>week2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>10</td>\n <td>NaN</td>\n <td>week3</td>\n </tr>\n <tr>\n <th>4</th>\n <td>20</td>\n <td>7.0</td>\n <td>week3</td>\n </tr>\n <tr>\n <th>5</th>\n <td>40</td>\n <td>9.0</td>\n <td>week3</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " sales revenue date\n0 50 14.0 week1\n1 20 8.0 week1\n2 35 16.0 week2\n3 10 NaN week3\n4 20 7.0 week3\n5 40 9.0 week3" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 32 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "### DataFrame.lookup()\n\n'fancy indexing' function for DataFrame\n\nGiven equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair\n\n#### example" | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df", | |
| "execution_count": 33, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "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>date</th>\n <th>sales</th>\n <th>category</th>\n <th>revenue</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>week1</td>\n <td>50</td>\n <td>a</td>\n <td>14.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>week1</td>\n <td>20</td>\n <td>b</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>week2</td>\n <td>35</td>\n <td>a</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>week3</td>\n <td>10</td>\n <td>a</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>week3</td>\n <td>20</td>\n <td>b</td>\n <td>7.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>week3</td>\n <td>40</td>\n <td>a</td>\n <td>9.0</td>\n </tr>\n </tbody>\n</table>\n</div>", | |
| "text/plain": " date sales category revenue\n0 week1 50 a 14.0\n1 week1 20 b 8.0\n2 week2 35 a 16.0\n3 week3 10 a NaN\n4 week3 20 b 7.0\n5 week3 40 a 9.0" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 33 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": false | |
| }, | |
| "cell_type": "code", | |
| "source": "df.lookup([0,1,2],['sales','category','revenue'])", | |
| "execution_count": 34, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": "array([50, 'b', 16.0], dtype=object)" | |
| }, | |
| "metadata": {}, | |
| "execution_count": 34 | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "## End\n\nThey are only a means to the end of learning how to do good data analysis " | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3", | |
| "language": "python" | |
| }, | |
| "gist": { | |
| "id": "", | |
| "data": { | |
| "description": "select_data.ipynb", | |
| "public": true | |
| } | |
| }, | |
| "language_info": { | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "version": "3.5.4", | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 2 | |
| } |
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