{
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  "name": ""
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figsize(7, 4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv('titanic.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "<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",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 97,
       "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  "
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Survived']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 98,
       "text": [
        "0     0\n",
        "1     1\n",
        "2     1\n",
        "3     1\n",
        "4     0\n",
        "5     0\n",
        "6     0\n",
        "7     0\n",
        "8     1\n",
        "9     1\n",
        "10    1\n",
        "11    1\n",
        "12    0\n",
        "13    0\n",
        "14    0\n",
        "...\n",
        "876    0\n",
        "877    0\n",
        "878    0\n",
        "879    1\n",
        "880    1\n",
        "881    0\n",
        "882    0\n",
        "883    0\n",
        "884    0\n",
        "885    0\n",
        "886    0\n",
        "887    1\n",
        "888    0\n",
        "889    1\n",
        "890    0\n",
        "Name: Survived, Length: 891, dtype: int64"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 99,
       "text": [
        "891"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Sex'].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "male      577\n",
        "female    314\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Survived'].head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "0    0\n",
        "1    1\n",
        "2    1\n",
        "3    1\n",
        "4    0\n",
        "5    0\n",
        "6    0\n",
        "7    0\n",
        "8    1\n",
        "9    1\n",
        "Name: Survived, dtype: int64"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Sex'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 102,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10cca2050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10cc7b950>"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_survived = df[ df['Survived'] == 1 ]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_survived.head(5)"
     ],
     "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>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>8</th>\n",
        "      <td>  9</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
        "      <td> female</td>\n",
        "      <td> 27</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td>           347742</td>\n",
        "      <td> 11.1333</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> S</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td> 10</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>               Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
        "      <td> female</td>\n",
        "      <td> 14</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td>           237736</td>\n",
        "      <td> 30.0708</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> C</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 104,
       "text": [
        "   PassengerId  Survived  Pclass  \\\n",
        "1            2         1       1   \n",
        "2            3         1       3   \n",
        "3            4         1       1   \n",
        "8            9         1       3   \n",
        "9           10         1       2   \n",
        "\n",
        "                                                Name     Sex  Age  SibSp  \\\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",
        "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female   27      0   \n",
        "9                Nasser, Mrs. Nicholas (Adele Achem)  female   14      1   \n",
        "\n",
        "   Parch            Ticket     Fare Cabin Embarked  \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",
        "8      2            347742  11.1333   NaN        S  \n",
        "9      0            237736  30.0708   NaN        C  "
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df0 = df[ df['Age'] < 10 ]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len([1, 4, 5, 6, 6])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 106,
       "text": [
        "5"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(df_survived['Pclass'].value_counts() / df['Pclass'].value_counts().astype(float)).plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10cca9450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lFErGdQBYZVwHgFXGdQAvrKicCoWCWlpalMlkNDQ0tOjxn/zkJ2pra9N9992n+++/X6dP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\ntfx6zVdtykIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c2bc1d0>"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(df_survived['Sex'].value_counts() / df['Sex'].value_counts().astype(float)).plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10ce8fd10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10ce95b10>"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Age'].value_counts().plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 109,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10ce46f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qkJKSYvb5o1KpxHBWVhbi4+Nx/fp1VFdXw8HBAbW1tYiNjcXFixeRlpZm8bih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\nViv2AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c072c50>"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Age'].hist(bins=30)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10d61a410>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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220bV8MEHH6ChoWHiMnv2bGzZssWoGgBg8+bNqKmpQW1tLe655x589tlnrmuQ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\nO0fxhI4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d61e1d0>"
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Age'].dropna().plot(kind='kde')\n",
      "df_survived['Age'].dropna().plot(kind='kde')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 111,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10d698750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mYcOGcpedMLQjR0xnljtO7KBnK2POB1uSatVMZ5cNfpdxSyEqU5nFMjAwEHd3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\nhgcRhvd5MJZylAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d6cda10>"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[ df['Sex'] == 'male']['Age'].dropna().plot(kind='kde')\n",
      "df_survived[df_survived['Sex'] == 'male']['Age'].dropna().plot(kind='kde')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 112,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10dc09890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zv//+O/n5+YZOOh3PPPOM2YMzhtavhhXmc/kyNGsGFy9CnTqlt0nOSmbg1wM5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\nwIQNE3jrgbeoZ1+vyn1PnYL//hemTy9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udXFy6z9hRVwcXBVBhzoqEgtWKAh+gBQHJ0Ve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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10dc0c310>"
       ]
      }
     ],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
    }
   ],
   "metadata": {}
  }
 ]
}