Telemetry tutorial

Jupyter.ipynb

pandas.ipynb

{"nbformat_minor": 0, "cells": [{"source": "# Pandas Tutorial\n\nCredits: The following are notes taken while working through [Python for Data Analysis](http://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1449319793) by Wes McKinney\n\n* Series\n* DataFrame\n* Dropping Entries\n* Indexing, Selecting, Filtering\n* Summarizing and Computing Descriptive Statistics\n* Split-apply-combine", "cell_type": "markdown", "metadata": {}}, {"execution_count": 60, "cell_type": "code", "source": "from pandas import Series, DataFrame\nimport pandas as pd\nimport numpy as np\n%pylab inline", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Populating the interactive namespace from numpy and matplotlib\n"}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "## Series\n\nA Series is a one-dimensional array-like object containing an array of data and an associated array of data labels.  The data can be any NumPy data type and the labels are the Series' index.", "cell_type": "markdown", "metadata": {}}, {"source": "Create a Series:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 4, "cell_type": "code", "source": "ser_1 = Series([1, 1, 2, -3, -5, 8, 13])\nser_1", "outputs": [{"execution_count": 4, "output_type": "execute_result", "data": {"text/plain": "0     1\n1     1\n2     2\n3    -3\n4    -5\n5     8\n6    13\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Get the array (numpy) representation of a Series:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 5, "cell_type": "code", "source": "ser_1.values", "outputs": [{"execution_count": 5, "output_type": "execute_result", "data": {"text/plain": "array([ 1,  1,  2, -3, -5,  8, 13])"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Index objects are immutable and hold the axis labels and metadata such as names and axis names.\n\nGet the index of the Series:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 6, "cell_type": "code", "source": "ser_1.index", "outputs": [{"execution_count": 6, "output_type": "execute_result", "data": {"text/plain": "Int64Index([0, 1, 2, 3, 4, 5, 6], dtype='int64')"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Create a Series with a custom index:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 21, "cell_type": "code", "source": "ser_2 = Series([1, 1, 2, -3, -5], index=['a', 'b', 'c', 'd', 'e'])\nser_2", "outputs": [{"execution_count": 21, "output_type": "execute_result", "data": {"text/plain": "a    1\nb    1\nc    2\nd   -3\ne   -5\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Get a value from a Series:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 8, "cell_type": "code", "source": "ser_2[4] == ser_2['e']", "outputs": [{"execution_count": 8, "output_type": "execute_result", "data": {"text/plain": "True"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Get a set of values from a Series by passing in a list:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 9, "cell_type": "code", "source": "ser_2[['c', 'a', 'b']]", "outputs": [{"execution_count": 9, "output_type": "execute_result", "data": {"text/plain": "c    2\na    1\nb    1\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Get values great than 0:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 10, "cell_type": "code", "source": "ser_2[ser_2 > 0]", "outputs": [{"execution_count": 10, "output_type": "execute_result", "data": {"text/plain": "a    1\nb    1\nc    2\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Get values between 0 and 1", "cell_type": "markdown", "metadata": {}}, {"execution_count": 22, "cell_type": "code", "source": "ser_2[(ser_2 > 0) & (ser_2 <= 1)]", "outputs": [{"execution_count": 22, "output_type": "execute_result", "data": {"text/plain": "a    1\nb    1\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Scalar multiply:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 11, "cell_type": "code", "source": "ser_2 * 2", "outputs": [{"execution_count": 11, "output_type": "execute_result", "data": {"text/plain": "a     2\nb     2\nc     4\nd    -6\ne   -10\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Apply a numpy math function:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 17, "cell_type": "code", "source": "np.exp(ser_2)", "outputs": [{"execution_count": 17, "output_type": "execute_result", "data": {"text/plain": "a    2.718282\nb    2.718282\nc    7.389056\nd    0.049787\ne    0.006738\ndtype: float64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "A Series is like a fixed-length, ordered dict.  \n\nCreate a series by passing in a dict:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 13, "cell_type": "code", "source": "dict_1 = {'foo' : 100, 'bar' : 200, 'baz' : 300}\nser_3 = Series(dict_1)\nser_3", "outputs": [{"execution_count": 13, "output_type": "execute_result", "data": {"text/plain": "bar    200\nbaz    300\nfoo    100\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Series automatically aligns differently indexed data in arithmetic operations:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 15, "cell_type": "code", "source": "ser_3 + ser_4", "outputs": [{"execution_count": 15, "output_type": "execute_result", "data": {"text/plain": "bar    400\nbaz    600\nfoo    200\nqux    NaN\ndtype: float64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Name a Series:", "cell_type": "markdown", "metadata": {}}, {"source": "Rename a Series' index in place:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 16, "cell_type": "code", "source": "ser_4.index = ['fo', 'br', 'bz', 'qx']\nser_4", "outputs": [{"execution_count": 16, "output_type": "execute_result", "data": {"text/plain": "fo    100\nbr    200\nbz    300\nqx    NaN\ndtype: float64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "## DataFrame\n\nA DataFrame is a tabular data structure containing an ordered collection of columns.  Each column can have a different type.  DataFrames have both row and column indices and is analogous to a dict of Series.  Row and column operations are treated roughly symmetrically.  Columns returned when indexing a DataFrame are views of the underlying data, not a copy.  To obtain a copy, use the Series' copy method.\n\nCreate a DataFrame:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 25, "cell_type": "code", "source": "data_1 = {'state' : ['VA', 'VA', 'VA', 'MD', 'MD'],\n          'year' : [2012, 2013, 2014, 2014, 2015],\n          'pop' : [5.0, 5.1, 5.2, 4.0, 4.1]}\ndf_1 = DataFrame(data_1)\ndf_1", "outputs": [{"execution_count": 25, "output_type": "execute_result", "data": {"text/plain": "   pop state  year\n0  5.0    VA  2012\n1  5.1    VA  2013\n2  5.2    VA  2014\n3  4.0    MD  2014\n4  4.1    MD  2015", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>pop</th>\n      <th>state</th>\n      <th>year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 5.0</td>\n      <td> VA</td>\n      <td> 2012</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 5.1</td>\n      <td> VA</td>\n      <td> 2013</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 5.2</td>\n      <td> VA</td>\n      <td> 2014</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 4.0</td>\n      <td> MD</td>\n      <td> 2014</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 4.1</td>\n      <td> MD</td>\n      <td> 2015</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Create a DataFrame specifying a sequence of columns:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 26, "cell_type": "code", "source": "df_2 = DataFrame(data_1, columns=['year', 'state', 'pop'])\ndf_2", "outputs": [{"execution_count": 26, "output_type": "execute_result", "data": {"text/plain": "   year state  pop\n0  2012    VA  5.0\n1  2013    VA  5.1\n2  2014    VA  5.2\n3  2014    MD  4.0\n4  2015    MD  4.1", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 2012</td>\n      <td> VA</td>\n      <td> 5.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 2013</td>\n      <td> VA</td>\n      <td> 5.1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 2014</td>\n      <td> MD</td>\n      <td> 4.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 2015</td>\n      <td> MD</td>\n      <td> 4.1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Like Series, columns that are not present in the data are NaN:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 27, "cell_type": "code", "source": "df_3 = DataFrame(data_1, columns=['year', 'state', 'pop', 'unempl'])\ndf_3", "outputs": [{"execution_count": 27, "output_type": "execute_result", "data": {"text/plain": "   year state  pop unempl\n0  2012    VA  5.0    NaN\n1  2013    VA  5.1    NaN\n2  2014    VA  5.2    NaN\n3  2014    MD  4.0    NaN\n4  2015    MD  4.1    NaN", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 2012</td>\n      <td> VA</td>\n      <td> 5.0</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 2013</td>\n      <td> VA</td>\n      <td> 5.1</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 2014</td>\n      <td> MD</td>\n      <td> 4.0</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 2015</td>\n      <td> MD</td>\n      <td> 4.1</td>\n      <td> NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Retrieve a column by key, returning a Series:\n", "cell_type": "markdown", "metadata": {}}, {"execution_count": 28, "cell_type": "code", "source": "df_3['state']", "outputs": [{"execution_count": 28, "output_type": "execute_result", "data": {"text/plain": "0    VA\n1    VA\n2    VA\n3    MD\n4    MD\nName: state, dtype: object"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Retrive a column by attribute, returning a Series:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 29, "cell_type": "code", "source": "df_3.year", "outputs": [{"execution_count": 29, "output_type": "execute_result", "data": {"text/plain": "0    2012\n1    2013\n2    2014\n3    2014\n4    2015\nName: year, dtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Retrieve a row by position:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 30, "cell_type": "code", "source": "df_3.ix[0]", "outputs": [{"execution_count": 30, "output_type": "execute_result", "data": {"text/plain": "year      2012\nstate       VA\npop          5\nunempl     NaN\nName: 0, dtype: object"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Update a column by assignment:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 31, "cell_type": "code", "source": "df_3['unempl'] = np.arange(5)\ndf_3", "outputs": [{"execution_count": 31, "output_type": "execute_result", "data": {"text/plain": "   year state  pop  unempl\n0  2012    VA  5.0       0\n1  2013    VA  5.1       1\n2  2014    VA  5.2       2\n3  2014    MD  4.0       3\n4  2015    MD  4.1       4", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 2012</td>\n      <td> VA</td>\n      <td> 5.0</td>\n      <td> 0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 2013</td>\n      <td> VA</td>\n      <td> 5.1</td>\n      <td> 1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n      <td> 2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 2014</td>\n      <td> MD</td>\n      <td> 4.0</td>\n      <td> 3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 2015</td>\n      <td> MD</td>\n      <td> 4.1</td>\n      <td> 4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Assign a Series to a column (note if assigning a list or array, the length must match the DataFrame, unlike a Series):", "cell_type": "markdown", "metadata": {}}, {"execution_count": 32, "cell_type": "code", "source": "unempl = Series([6.0, 6.0, 6.1], index=[2, 3, 4])\ndf_3['unempl'] = unempl\ndf_3", "outputs": [{"execution_count": 32, "output_type": "execute_result", "data": {"text/plain": "   year state  pop  unempl\n0  2012    VA  5.0     NaN\n1  2013    VA  5.1     NaN\n2  2014    VA  5.2     6.0\n3  2014    MD  4.0     6.0\n4  2015    MD  4.1     6.1", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 2012</td>\n      <td> VA</td>\n      <td> 5.0</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 2013</td>\n      <td> VA</td>\n      <td> 5.1</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 2014</td>\n      <td> MD</td>\n      <td> 4.0</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 2015</td>\n      <td> MD</td>\n      <td> 4.1</td>\n      <td> 6.1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Assign a new column that doesn't exist to create a new column:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 33, "cell_type": "code", "source": "df_3['state_dup'] = df_3['state']\ndf_3", "outputs": [{"execution_count": 33, "output_type": "execute_result", "data": {"text/plain": "   year state  pop  unempl state_dup\n0  2012    VA  5.0     NaN        VA\n1  2013    VA  5.1     NaN        VA\n2  2014    VA  5.2     6.0        VA\n3  2014    MD  4.0     6.0        MD\n4  2015    MD  4.1     6.1        MD", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n      <th>state_dup</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 2012</td>\n      <td> VA</td>\n      <td> 5.0</td>\n      <td> NaN</td>\n      <td> VA</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 2013</td>\n      <td> VA</td>\n      <td> 5.1</td>\n      <td> NaN</td>\n      <td> VA</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n      <td> 6.0</td>\n      <td> VA</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 2014</td>\n      <td> MD</td>\n      <td> 4.0</td>\n      <td> 6.0</td>\n      <td> MD</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 2015</td>\n      <td> MD</td>\n      <td> 4.1</td>\n      <td> 6.1</td>\n      <td> MD</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Delete a column:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 34, "cell_type": "code", "source": "del df_3['state_dup']\ndf_3", "outputs": [{"execution_count": 34, "output_type": "execute_result", "data": {"text/plain": "   year state  pop  unempl\n0  2012    VA  5.0     NaN\n1  2013    VA  5.1     NaN\n2  2014    VA  5.2     6.0\n3  2014    MD  4.0     6.0\n4  2015    MD  4.1     6.1", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 2012</td>\n      <td> VA</td>\n      <td> 5.0</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 2013</td>\n      <td> VA</td>\n      <td> 5.1</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 2014</td>\n      <td> MD</td>\n      <td> 4.0</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 2015</td>\n      <td> MD</td>\n      <td> 4.1</td>\n      <td> 6.1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "## Indexing, Selecting, Filtering", "cell_type": "markdown", "metadata": {}}, {"source": "Series indexing is similar to NumPy array indexing with the added bonus of being able to use the Series' index values.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 35, "cell_type": "code", "source": "ser_2", "outputs": [{"execution_count": 35, "output_type": "execute_result", "data": {"text/plain": "a    1\nb    1\nc    2\nd   -3\ne   -5\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Select a value from a Series:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 36, "cell_type": "code", "source": "ser_2[0] == ser_2['a']", "outputs": [{"execution_count": 36, "output_type": "execute_result", "data": {"text/plain": "True"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Select a slice from a Series:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 37, "cell_type": "code", "source": "ser_2[1:4]", "outputs": [{"execution_count": 37, "output_type": "execute_result", "data": {"text/plain": "b    1\nc    2\nd   -3\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Select specific values from a Series:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 32, "cell_type": "code", "source": "ser_2[['b', 'c', 'd']]", "outputs": [{"execution_count": 32, "output_type": "execute_result", "data": {"text/plain": "b    1\nc    2\nd   -3\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Select from a Series based on a filter:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 33, "cell_type": "code", "source": "ser_2[ser_2 > 0]", "outputs": [{"execution_count": 33, "output_type": "execute_result", "data": {"text/plain": "a    1\nb    1\nc    2\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Select a slice from a Series with labels (note the end point is inclusive):", "cell_type": "markdown", "metadata": {}}, {"execution_count": 34, "cell_type": "code", "source": "ser_2['a':'b']", "outputs": [{"execution_count": 34, "output_type": "execute_result", "data": {"text/plain": "a    1\nb    1\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Assign to a Series slice (note the end point is inclusive):", "cell_type": "markdown", "metadata": {}}, {"execution_count": 35, "cell_type": "code", "source": "ser_2['a':'b'] = 0\nser_2", "outputs": [{"execution_count": 35, "output_type": "execute_result", "data": {"text/plain": "a    0\nb    0\nc    2\nd   -3\ne   -5\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Pandas supports indexing into a DataFrame.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 39, "cell_type": "code", "source": "df_3", "outputs": [{"execution_count": 39, "output_type": "execute_result", "data": {"text/plain": "   year state  pop  unempl\n0  2012    VA  5.0     NaN\n1  2013    VA  5.1     NaN\n2  2014    VA  5.2     6.0\n3  2014    MD  4.0     6.0\n4  2015    MD  4.1     6.1", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 2012</td>\n      <td> VA</td>\n      <td> 5.0</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 2013</td>\n      <td> VA</td>\n      <td> 5.1</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 2014</td>\n      <td> MD</td>\n      <td> 4.0</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 2015</td>\n      <td> MD</td>\n      <td> 4.1</td>\n      <td> 6.1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Select specified columns from a DataFrame:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 40, "cell_type": "code", "source": "df_3[['pop', 'unempl']]", "outputs": [{"execution_count": 40, "output_type": "execute_result", "data": {"text/plain": "   pop  unempl\n0  5.0     NaN\n1  5.1     NaN\n2  5.2     6.0\n3  4.0     6.0\n4  4.1     6.1", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 5.0</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 5.1</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 5.2</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 4.0</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 4.1</td>\n      <td> 6.1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Select a slice from a DataFrame:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 41, "cell_type": "code", "source": "df_3[:2]", "outputs": [{"execution_count": 41, "output_type": "execute_result", "data": {"text/plain": "   year state  pop  unempl\n0  2012    VA  5.0     NaN\n1  2013    VA  5.1     NaN", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 2012</td>\n      <td> VA</td>\n      <td> 5.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 2013</td>\n      <td> VA</td>\n      <td> 5.1</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Select from a DataFrame based on a filter:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 38, "cell_type": "code", "source": "df_3[df_3['pop'] > 5]", "outputs": [{"execution_count": 38, "output_type": "execute_result", "data": {"text/plain": "   year state  pop  unempl\n1  2013    VA  5.1     NaN\n2  2014    VA  5.2       6", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td> 2013</td>\n      <td> VA</td>\n      <td> 5.1</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n      <td>  6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Perform a scalar comparison on a DataFrame:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 39, "cell_type": "code", "source": "df_3 > 5", "outputs": [{"execution_count": 39, "output_type": "execute_result", "data": {"text/plain": "   year state    pop unempl\n0  True  True  False  False\n1  True  True   True  False\n2  True  True   True   True\n3  True  True  False   True\n4  True  True  False   True", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> True</td>\n      <td> True</td>\n      <td> False</td>\n      <td> False</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> True</td>\n      <td> True</td>\n      <td>  True</td>\n      <td> False</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> False</td>\n      <td>  True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> True</td>\n      <td> True</td>\n      <td> False</td>\n      <td>  True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Perform a scalar comparison on a DataFrame, retain the values that pass the filter:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 43, "cell_type": "code", "source": "df_3[df_3 > 5]", "outputs": [{"execution_count": 43, "output_type": "execute_result", "data": {"text/plain": "   year state  pop  unempl\n0  2012    VA  NaN     NaN\n1  2013    VA  5.1     NaN\n2  2014    VA  5.2     6.0\n3  2014    MD  NaN     6.0\n4  2015    MD  NaN     6.1", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 2012</td>\n      <td> VA</td>\n      <td> NaN</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 2013</td>\n      <td> VA</td>\n      <td> 5.1</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 2014</td>\n      <td> MD</td>\n      <td> NaN</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 2015</td>\n      <td> MD</td>\n      <td> NaN</td>\n      <td> 6.1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Select a slice of rows from a DataFrame (note the end point is inclusive):", "cell_type": "markdown", "metadata": {}}, {"execution_count": 44, "cell_type": "code", "source": "df_3.ix[2:3]", "outputs": [{"execution_count": 44, "output_type": "execute_result", "data": {"text/plain": "   year state  pop  unempl\n2  2014    VA  5.2       6\n3  2014    MD  4.0       6", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n      <td> 6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 2014</td>\n      <td> MD</td>\n      <td> 4.0</td>\n      <td> 6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Select a slice of rows from a specific column of a DataFrame:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 45, "cell_type": "code", "source": "df_3.ix[0:2, 'pop']\ndf_3", "outputs": [{"execution_count": 45, "output_type": "execute_result", "data": {"text/plain": "   year state  pop  unempl\n0  2012    VA  5.0     NaN\n1  2013    VA  5.1     NaN\n2  2014    VA  5.2     6.0\n3  2014    MD  4.0     6.0\n4  2015    MD  4.1     6.1", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 2012</td>\n      <td> VA</td>\n      <td> 5.0</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 2013</td>\n      <td> VA</td>\n      <td> 5.1</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 2014</td>\n      <td> MD</td>\n      <td> 4.0</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 2015</td>\n      <td> MD</td>\n      <td> 4.1</td>\n      <td> 6.1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Select rows based on an arithmetic operation on a specific row:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 46, "cell_type": "code", "source": "df_3.ix[df_3.unempl > 5.0]", "outputs": [{"execution_count": 46, "output_type": "execute_result", "data": {"text/plain": "   year state  pop  unempl\n2  2014    VA  5.2     6.0\n3  2014    MD  4.0     6.0\n4  2015    MD  4.1     6.1", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 2014</td>\n      <td> MD</td>\n      <td> 4.0</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 2015</td>\n      <td> MD</td>\n      <td> 4.1</td>\n      <td> 6.1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "## Summarizing and Computing Descriptive Statistics", "cell_type": "markdown", "metadata": {}}, {"source": "Unlike NumPy arrays, Pandas descriptive statistics automatically exclude missing data.  NaN values are excluded unless the entire row or column is NA.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 47, "cell_type": "code", "source": "df_3", "outputs": [{"execution_count": 47, "output_type": "execute_result", "data": {"text/plain": "   year state  pop  unempl\n0  2012    VA  5.0     NaN\n1  2013    VA  5.1     NaN\n2  2014    VA  5.2     6.0\n3  2014    MD  4.0     6.0\n4  2015    MD  4.1     6.1", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 2012</td>\n      <td> VA</td>\n      <td> 5.0</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 2013</td>\n      <td> VA</td>\n      <td> 5.1</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 2014</td>\n      <td> MD</td>\n      <td> 4.0</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 2015</td>\n      <td> MD</td>\n      <td> 4.1</td>\n      <td> 6.1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 48, "cell_type": "code", "source": "df_3.sum()", "outputs": [{"execution_count": 48, "output_type": "execute_result", "data": {"text/plain": "year           10068\nstate     VAVAVAMDMD\npop             23.4\nunempl          18.1\ndtype: object"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "Sum over the rows:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 49, "cell_type": "code", "source": "df_3.sum(axis=1)", "outputs": [{"execution_count": 49, "output_type": "execute_result", "data": {"text/plain": "0    2017.0\n1    2018.1\n2    2025.2\n3    2024.0\n4    2025.2\ndtype: float64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "## Split-apply-combine", "cell_type": "markdown", "metadata": {}}, {"source": "Many data analysis problems involve the application of a split-apply-combine strategy,\nwhere you break up a big problem into manageable pieces, operate on each piece independently and then put all the pieces back together.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 50, "cell_type": "code", "source": "df_3", "outputs": [{"execution_count": 50, "output_type": "execute_result", "data": {"text/plain": "   year state  pop  unempl\n0  2012    VA  5.0     NaN\n1  2013    VA  5.1     NaN\n2  2014    VA  5.2     6.0\n3  2014    MD  4.0     6.0\n4  2015    MD  4.1     6.1", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td> 2012</td>\n      <td> VA</td>\n      <td> 5.0</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td> 2013</td>\n      <td> VA</td>\n      <td> 5.1</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td> 2014</td>\n      <td> MD</td>\n      <td> 4.0</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td> 2015</td>\n      <td> MD</td>\n      <td> 4.1</td>\n      <td> 6.1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "The groupby operation creates a new dataframe for each group:", "cell_type": "markdown", "metadata": {}}, {"execution_count": 52, "cell_type": "code", "source": "df_3.groupby(\"state\").apply(lambda x: type(x))", "outputs": [{"execution_count": 52, "output_type": "execute_result", "data": {"text/plain": "state\nMD       <class 'pandas.core.frame.DataFrame'>\nVA       <class 'pandas.core.frame.DataFrame'>\ndtype: object"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": false}}, {"execution_count": 53, "cell_type": "code", "source": "df_3.groupby(\"state\").apply(lambda x: x.sum())", "outputs": [{"execution_count": 53, "output_type": "execute_result", "data": {"text/plain": "       year   state   pop  unempl\nstate                            \nMD     4029    MDMD   8.1    12.1\nVA     6039  VAVAVA  15.3     6.0", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n    <tr>\n      <th>state</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>MD</th>\n      <td> 4029</td>\n      <td>   MDMD</td>\n      <td>  8.1</td>\n      <td> 12.1</td>\n    </tr>\n    <tr>\n      <th>VA</th>\n      <td> 6039</td>\n      <td> VAVAVA</td>\n      <td> 15.3</td>\n      <td>  6.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "A pivot table is a spreadsheet feature that allows data tables to be rearranged in many ways for different views of the same data.", "cell_type": "markdown", "metadata": {}}, {"execution_count": 56, "cell_type": "code", "source": "df_3.groupby([\"state\", \"year\"]).apply(lambda x: x.sum())", "outputs": [{"execution_count": 56, "output_type": "execute_result", "data": {"text/plain": "            year state  pop  unempl\nstate year                         \nMD    2014  2014    MD  4.0     6.0\n      2015  2015    MD  4.1     6.1\nVA    2012  2012    VA  5.0     NaN\n      2013  2013    VA  5.1     NaN\n      2014  2014    VA  5.2     6.0", "text/html": "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>year</th>\n      <th>state</th>\n      <th>pop</th>\n      <th>unempl</th>\n    </tr>\n    <tr>\n      <th>state</th>\n      <th>year</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">MD</th>\n      <th>2014</th>\n      <td> 2014</td>\n      <td> MD</td>\n      <td> 4.0</td>\n      <td> 6.0</td>\n    </tr>\n    <tr>\n      <th>2015</th>\n      <td> 2015</td>\n      <td> MD</td>\n      <td> 4.1</td>\n      <td> 6.1</td>\n    </tr>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">VA</th>\n      <th>2012</th>\n      <td> 2012</td>\n      <td> VA</td>\n      <td> 5.0</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2013</th>\n      <td> 2013</td>\n      <td> VA</td>\n      <td> 5.1</td>\n      <td> NaN</td>\n    </tr>\n    <tr>\n      <th>2014</th>\n      <td> 2014</td>\n      <td> VA</td>\n      <td> 5.2</td>\n      <td> 6.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"}, "metadata": {}}], "metadata": {"scrolled": true, "collapsed": false, "trusted": false}}, {"source": "## Plotting", "cell_type": "markdown", "metadata": {}}, {"execution_count": 58, "cell_type": "code", "source": "ser_2", "outputs": [{"execution_count": 58, "output_type": "execute_result", "data": {"text/plain": "a    1\nb    1\nc    2\nd   -3\ne   -5\ndtype: int64"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 86, "cell_type": "code", "source": "ser_2.plot()", "outputs": [{"execution_count": 86, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fee3b489490>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7fee3b401b90>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 62, "cell_type": "code", "source": "help(ser_2.plot)", "outputs": [{"output_type": "stream", "name": "stdout", "text": "Help on method plot_series in module pandas.tools.plotting:\n\nplot_series(data, kind='line', ax=None, figsize=None, use_index=True, title=None, grid=None, legend=False, style=None, logx=False, logy=False, loglog=False, xticks=None, yticks=None, xlim=None, ylim=None, rot=None, fontsize=None, colormap=None, table=False, yerr=None, xerr=None, label=None, secondary_y=False, **kwds) method of pandas.core.series.Series instance\n    Make plots of Series using matplotlib / pylab.\n    \n    Parameters\n    ----------\n    data : Series\n    \n    kind : str\n        - 'line' : line plot (default)\n        - 'bar' : vertical bar plot\n        - 'barh' : horizontal bar plot\n        - 'hist' : histogram\n        - 'box' : boxplot\n        - 'kde' : Kernel Density Estimation plot\n        - 'density' : same as 'kde'\n        - 'area' : area plot\n        - 'pie' : pie plot\n        \n    ax : matplotlib axes object\n        If not passed, uses gca()\n    figsize : a tuple (width, height) in inches\n    use_index : boolean, default True\n        Use index as ticks for x axis\n    title : string\n        Title to use for the plot\n    grid : boolean, default None (matlab style default)\n        Axis grid lines\n    legend : False/True/'reverse'\n        Place legend on axis subplots\n    style : list or dict\n        matplotlib line style per column\n    logx : boolean, default False\n        Use log scaling on x axis\n    logy : boolean, default False\n        Use log scaling on y axis\n    loglog : boolean, default False\n        Use log scaling on both x and y axes\n    xticks : sequence\n        Values to use for the xticks\n    yticks : sequence\n        Values to use for the yticks\n    xlim : 2-tuple/list\n    ylim : 2-tuple/list\n    rot : int, default None\n        Rotation for ticks (xticks for vertical, yticks for horizontal plots)\n    fontsize : int, default None\n        Font size for xticks and yticks\n    colormap : str or matplotlib colormap object, default None\n        Colormap to select colors from. If string, load colormap with that name\n        from matplotlib.\n    colorbar : boolean, optional\n        If True, plot colorbar (only relevant for 'scatter' and 'hexbin' plots)\n    position : float\n        Specify relative alignments for bar plot layout.\n        From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center)\n    layout : tuple (optional)\n        (rows, columns) for the layout of the plot\n    table : boolean, Series or DataFrame, default False\n        If True, draw a table using the data in the DataFrame and the data will\n        be transposed to meet matplotlib's default layout.\n        If a Series or DataFrame is passed, use passed data to draw a table.\n    yerr : DataFrame, Series, array-like, dict and str\n        See :ref:`Plotting with Error Bars <visualization.errorbars>` for detail.\n    xerr : same types as yerr.\n    label : label argument to provide to plot\n    secondary_y : boolean or sequence of ints, default False\n        If True then y-axis will be on the right\n    mark_right : boolean, default True\n        When using a secondary_y axis, automatically mark the column\n        labels with \"(right)\" in the legend\n    kwds : keywords\n        Options to pass to matplotlib plotting method\n    \n    Returns\n    -------\n    axes : matplotlib.AxesSubplot or np.array of them\n    \n    Notes\n    -----\n    \n    - See matplotlib documentation online for more on this subject\n    - If `kind` = 'bar' or 'barh', you can specify relative alignments\n      for bar plot layout by `position` keyword.\n      From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center)\n\n"}], "metadata": {"collapsed": false, "trusted": false}}, {"execution_count": 84, "cell_type": "code", "source": "df_3.groupby(\"state\").mean()[[\"pop\", \"unempl\"]].plot(kind=\"bar\")", "outputs": [{"execution_count": 84, "output_type": "execute_result", "data": {"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fee3b5e99d0>"}, "metadata": {}}, {"output_type": "display_data", "data": {"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7fee3b73ddd0>"}, "metadata": {}}], "metadata": {"collapsed": false, "trusted": false}}, {"source": "#####Exercises", "cell_type": "markdown", "metadata": {"collapsed": true}}, {"source": "1. 1. Create a Series with 100 random numbers from 0 to 10, then multiply by 2 the even numbers that are above or equal 3.", "cell_type": "markdown", "metadata": {}}, {"execution_count": null, "cell_type": "code", "source": "", "outputs": [], "metadata": {"collapsed": true, "trusted": false}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 2", "name": "python2", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "2.7.9", "name": "python", "file_extension": ".py", "pygments_lexer": "ipython2", "codemirror_mode": {"version": 2, "name": "ipython"}}}}

Spark.ipynb