PythonPandasCookbook5.1.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Python for Data Analysis Lightning Tutorials\n",
      "## Pandas Cookbook Series\n",
      "\n",
      "**Python for Data Analysis Lightning Tutorials** is a series of tutorials in Data Analysis, Statistics, and Graphics using Python. The **Pandas Cookbook** series of tutorials provides recipes for common tasks and moves on to more advanced topics in statistics and time series analysis. \n",
      "\n",
      "<i>Created by Alfred Essa</i>, June 15th, 2014 \n",
      "\n",
      "Note: IPython Notebook and Data files can be found at my Github Site: http://github/alfredessa\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.1 Introduction"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this tutorial we learn to do some basic descriptive statistics in Pandas"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.11 Preliminaries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Load pandas and numpy libraries\n",
      "import pandas as pd\n",
      "import numpy as np\n",
      "\n",
      " "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set default option for Pandas\n",
      "pd.set_option('display.max_rows',20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plot inline in notebook\n",
      "%pylab inline\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.12 Read Automobile Dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Read dataset\n",
      "auto = pd.read_csv('data/auto.csv')\n",
      "\n",
      " "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Show first lines of data\n",
      "auto.head()\n",
      " "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>make</th>\n",
        "      <th>price</th>\n",
        "      <th>mpg</th>\n",
        "      <th>repairs</th>\n",
        "      <th>weight</th>\n",
        "      <th>length</th>\n",
        "      <th>foreign</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  AMC</td>\n",
        "      <td> 4099</td>\n",
        "      <td> 22</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2930</td>\n",
        "      <td> 186</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  AMC</td>\n",
        "      <td> 4749</td>\n",
        "      <td> 17</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3350</td>\n",
        "      <td> 173</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  AMC</td>\n",
        "      <td> 3799</td>\n",
        "      <td> 22</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2640</td>\n",
        "      <td> 168</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Audi</td>\n",
        "      <td> 9690</td>\n",
        "      <td> 17</td>\n",
        "      <td> 5</td>\n",
        "      <td> 2830</td>\n",
        "      <td> 189</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> Audi</td>\n",
        "      <td> 6295</td>\n",
        "      <td> 23</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2070</td>\n",
        "      <td> 174</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "   make  price  mpg  repairs  weight  length  foreign\n",
        "0   AMC   4099   22        3    2930     186        0\n",
        "1   AMC   4749   17        3    3350     173        0\n",
        "2   AMC   3799   22        3    2640     168        0\n",
        "3  Audi   9690   17        5    2830     189        1\n",
        "4  Audi   6295   23        3    2070     174        1\n",
        "\n",
        "[5 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.13 Basic Statistics and Visualizations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# describe provides basic statistics\n",
      "auto.describe()\n",
      " "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>price</th>\n",
        "      <th>mpg</th>\n",
        "      <th>repairs</th>\n",
        "      <th>weight</th>\n",
        "      <th>length</th>\n",
        "      <th>foreign</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>    26.000000</td>\n",
        "      <td> 26.000000</td>\n",
        "      <td> 26.000000</td>\n",
        "      <td>   26.000000</td>\n",
        "      <td>  26.000000</td>\n",
        "      <td> 26.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>  6651.730769</td>\n",
        "      <td> 20.923077</td>\n",
        "      <td>  3.269231</td>\n",
        "      <td> 3099.230769</td>\n",
        "      <td> 190.076923</td>\n",
        "      <td>  0.269231</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>  3371.119809</td>\n",
        "      <td>  4.757504</td>\n",
        "      <td>  0.777570</td>\n",
        "      <td>  695.079409</td>\n",
        "      <td>  18.170136</td>\n",
        "      <td>  0.452344</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>  3299.000000</td>\n",
        "      <td> 14.000000</td>\n",
        "      <td>  2.000000</td>\n",
        "      <td> 2020.000000</td>\n",
        "      <td> 163.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>  4465.750000</td>\n",
        "      <td> 17.250000</td>\n",
        "      <td>  3.000000</td>\n",
        "      <td> 2642.500000</td>\n",
        "      <td> 173.250000</td>\n",
        "      <td>  0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>  5146.500000</td>\n",
        "      <td> 21.000000</td>\n",
        "      <td>  3.000000</td>\n",
        "      <td> 3200.000000</td>\n",
        "      <td> 191.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>  8053.500000</td>\n",
        "      <td> 23.000000</td>\n",
        "      <td>  4.000000</td>\n",
        "      <td> 3610.000000</td>\n",
        "      <td> 203.000000</td>\n",
        "      <td>  0.750000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 15906.000000</td>\n",
        "      <td> 35.000000</td>\n",
        "      <td>  5.000000</td>\n",
        "      <td> 4330.000000</td>\n",
        "      <td> 222.000000</td>\n",
        "      <td>  1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "              price        mpg    repairs       weight      length    foreign\n",
        "count     26.000000  26.000000  26.000000    26.000000   26.000000  26.000000\n",
        "mean    6651.730769  20.923077   3.269231  3099.230769  190.076923   0.269231\n",
        "std     3371.119809   4.757504   0.777570   695.079409   18.170136   0.452344\n",
        "min     3299.000000  14.000000   2.000000  2020.000000  163.000000   0.000000\n",
        "25%     4465.750000  17.250000   3.000000  2642.500000  173.250000   0.000000\n",
        "50%     5146.500000  21.000000   3.000000  3200.000000  191.000000   0.000000\n",
        "75%     8053.500000  23.000000   4.000000  3610.000000  203.000000   0.750000\n",
        "max    15906.000000  35.000000   5.000000  4330.000000  222.000000   1.000000\n",
        "\n",
        "[8 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# choose an individual column\n",
      "auto.mpg.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "count    26.000000\n",
        "mean     20.923077\n",
        "std       4.757504\n",
        "min      14.000000\n",
        "25%      17.250000\n",
        "50%      21.000000\n",
        "75%      23.000000\n",
        "max      35.000000\n",
        "Name: mpg, dtype: float64"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# variant notation\n",
      "auto['mpg'].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "count    26.000000\n",
        "mean     20.923077\n",
        "std       4.757504\n",
        "min      14.000000\n",
        "25%      17.250000\n",
        "50%      21.000000\n",
        "75%      23.000000\n",
        "max      35.000000\n",
        "Name: mpg, dtype: float64"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# median is not part of describe but is available\n",
      "auto.mpg.std()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "4.7575041937812488"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot a histogram\n",
      "auto.price.hist()\n",
      " "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x110614ad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1103fa890>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#box plot\n",
      "auto.boxplot(column='price')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "{'boxes': [<matplotlib.lines.Line2D at 0x110b6b3d0>],\n",
        " 'caps': [<matplotlib.lines.Line2D at 0x110b69710>,\n",
        "  <matplotlib.lines.Line2D at 0x110b69d50>],\n",
        " 'fliers': [<matplotlib.lines.Line2D at 0x110b6f050>,\n",
        "  <matplotlib.lines.Line2D at 0x110b6f650>],\n",
        " 'medians': [<matplotlib.lines.Line2D at 0x110b6ba10>],\n",
        " 'whiskers': [<matplotlib.lines.Line2D at 0x110b65d90>,\n",
        "  <matplotlib.lines.Line2D at 0x110b69050>]}"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HHmLBggURn59IuGiPQuQU7d27l7/+9a8cOnSIl156iYKCgv/63JSUFJKTk3n9\n9deBo7e7+f7777n11lt5/vnn2bVrFwCffPIJ+/fvPyvzFwmVgkLkFGVmZrJx40aysrKYNm2adVjp\nv1mzZg2vvfYa06dP55prruHQoUPMmTOHW2+9lZtvvpnp06ezePFivvvuu7O0ApHQ6PRYkVPQ29vL\nvHnz+Mc//hHtqYicddqjEDlFujGlnKu0RyEiIkFpj0JERIJSUIiISFAKChERCUpBISIiQSkoREQk\nKAWFiIgE9X/0CSVcaVnwOwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1109b2bd0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "5.14 Groupby"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic = pd.read_csv('data/titanic.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>PassengerId</th>\n",
        "      <th>Survived</th>\n",
        "      <th>Pclass</th>\n",
        "      <th>Name</th>\n",
        "      <th>Sex</th>\n",
        "      <th>Age</th>\n",
        "      <th>SibSp</th>\n",
        "      <th>Parch</th>\n",
        "      <th>Ticket</th>\n",
        "      <th>Fare</th>\n",
        "      <th>Cabin</th>\n",
        "      <th>Embarked</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                           Braund, Mr. Owen Harris</td>\n",
        "      <td>   male</td>\n",
        "      <td> 22</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>        A/5 21171</td>\n",
        "      <td>  7.2500</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
        "      <td> female</td>\n",
        "      <td> 38</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>         PC 17599</td>\n",
        "      <td> 71.2833</td>\n",
        "      <td>  C85</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td>                            Heikkinen, Miss. Laina</td>\n",
        "      <td> female</td>\n",
        "      <td> 26</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> STON/O2. 3101282</td>\n",
        "      <td>  7.9250</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>      Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
        "      <td> female</td>\n",
        "      <td> 35</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>           113803</td>\n",
        "      <td> 53.1000</td>\n",
        "      <td> C123</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td>                          Allen, Mr. William Henry</td>\n",
        "      <td>   male</td>\n",
        "      <td> 35</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>           373450</td>\n",
        "      <td>  8.0500</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "   PassengerId  Survived  Pclass  \\\n",
        "0            1         0       3   \n",
        "1            2         1       1   \n",
        "2            3         1       3   \n",
        "3            4         1       1   \n",
        "4            5         0       3   \n",
        "\n",
        "                                                Name     Sex  Age  SibSp  \\\n",
        "0                            Braund, Mr. Owen Harris    male   22      1   \n",
        "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38      1   \n",
        "2                             Heikkinen, Miss. Laina  female   26      0   \n",
        "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   35      1   \n",
        "4                           Allen, Mr. William Henry    male   35      0   \n",
        "\n",
        "   Parch            Ticket     Fare Cabin Embarked  \n",
        "0      0         A/5 21171   7.2500   NaN        S  \n",
        "1      0          PC 17599  71.2833   C85        C  \n",
        "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
        "3      0            113803  53.1000  C123        S  \n",
        "4      0            373450   8.0500   NaN        S  \n",
        "\n",
        "[5 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic.describe() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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>PassengerId</th>\n",
        "      <th>Survived</th>\n",
        "      <th>Pclass</th>\n",
        "      <th>Age</th>\n",
        "      <th>SibSp</th>\n",
        "      <th>Parch</th>\n",
        "      <th>Fare</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 714.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "      <td> 891.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 446.000000</td>\n",
        "      <td>   0.383838</td>\n",
        "      <td>   2.308642</td>\n",
        "      <td>  29.699118</td>\n",
        "      <td>   0.523008</td>\n",
        "      <td>   0.381594</td>\n",
        "      <td>  32.204208</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 257.353842</td>\n",
        "      <td>   0.486592</td>\n",
        "      <td>   0.836071</td>\n",
        "      <td>  14.526497</td>\n",
        "      <td>   1.102743</td>\n",
        "      <td>   0.806057</td>\n",
        "      <td>  49.693429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   0.420000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 223.500000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   2.000000</td>\n",
        "      <td>  20.125000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   7.910400</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 446.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   3.000000</td>\n",
        "      <td>  28.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  14.454200</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 668.500000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   3.000000</td>\n",
        "      <td>  38.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  31.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 891.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   3.000000</td>\n",
        "      <td>  80.000000</td>\n",
        "      <td>   8.000000</td>\n",
        "      <td>   6.000000</td>\n",
        "      <td> 512.329200</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 7 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
        "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
        "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
        "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
        "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
        "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
        "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
        "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
        "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
        "\n",
        "            Parch        Fare  \n",
        "count  891.000000  891.000000  \n",
        "mean     0.381594   32.204208  \n",
        "std      0.806057   49.693429  \n",
        "min      0.000000    0.000000  \n",
        "25%      0.000000    7.910400  \n",
        "50%      0.000000   14.454200  \n",
        "75%      0.000000   31.000000  \n",
        "max      6.000000  512.329200  \n",
        "\n",
        "[8 rows x 7 columns]"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "titanic.Age.hist(bins=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x110db90d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Pic6voGaxdAcoWLoDFCzdATHMPJ/M7GKTexytaG42bdo0TJvWsT7p378/tm/fnnAUEREl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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1109b0bd0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grouped = titanic.groupby('Sex')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grouped.Age.median()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "Sex\n",
        "female    27\n",
        "male      29\n",
        "Name: Age, dtype: float64"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grouped.Age.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "Sex          \n",
        "female  count    261.000000\n",
        "        mean      27.915709\n",
        "        std       14.110146\n",
        "        min        0.750000\n",
        "        25%       18.000000\n",
        "        50%       27.000000\n",
        "        75%       37.000000\n",
        "        max       63.000000\n",
        "male    count    453.000000\n",
        "        mean      30.726645\n",
        "        std       14.678201\n",
        "        min        0.420000\n",
        "        25%       21.000000\n",
        "        50%       29.000000\n",
        "        75%       39.000000\n",
        "        max       80.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grouped.get_group('male').Age.hist(bins=20)\n",
      "grouped.get_group('female').Age.hist(bins=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1113737d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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13t5e2z+ni61Zs0Z/4YUXdF23//xt2bJFHzVqlD5u3Dh97dq1tmeKx+P6N77x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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11135e4d0>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "grouped.boxplot(column='Age')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "{'female': {'boxes': [<matplotlib.lines.Line2D at 0x111934f90>],\n",
        "  'caps': [<matplotlib.lines.Line2D at 0x111930250>,\n",
        "   <matplotlib.lines.Line2D at 0x111934950>],\n",
        "  'fliers': [<matplotlib.lines.Line2D at 0x111937c50>,\n",
        "   <matplotlib.lines.Line2D at 0x11193b650>],\n",
        "  'medians': [<matplotlib.lines.Line2D at 0x111937610>],\n",
        "  'whiskers': [<matplotlib.lines.Line2D at 0x111934450>,\n",
        "   <matplotlib.lines.Line2D at 0x111930590>]},\n",
        " 'male': {'boxes': [<matplotlib.lines.Line2D at 0x111941450>],\n",
        "  'caps': [<matplotlib.lines.Line2D at 0x11193e790>,\n",
        "   <matplotlib.lines.Line2D at 0x11193edd0>],\n",
        "  'fliers': [<matplotlib.lines.Line2D at 0x111943110>,\n",
        "   <matplotlib.lines.Line2D at 0x111943710>],\n",
        "  'medians': [<matplotlib.lines.Line2D at 0x111941a90>],\n",
        "  'whiskers': [<matplotlib.lines.Line2D at 0x11193bd90>,\n",
        "   <matplotlib.lines.Line2D at 0x11193e0d0>]}}"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7HKK04nhPL165ExHpEDN3IiIdYnMnItIhNnciIh1icyci0iE2dyIiHWJzJyLS\nof8HSDVg4nDZrTEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111388e50>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}