Titanic Intro Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import Series,DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic_df=pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\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",
       "</div>"
      ],
      "text/plain": [
       "   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  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex  Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male   27      0      0      211536  13.00   NaN        S  \n",
       "887  female   19      0      0      112053  30.00   B42        S  \n",
       "888  female  NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male   26      0      0      111369  30.00  C148        C  \n",
       "890    male   32      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 90.5+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x109c545f8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x109c54fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Sex',data=titanic_df,kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10a11be10>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a11b978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Sex',data=titanic_df,hue='Pclass',kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10a4ef6d8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a46c128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Pclass',data=titanic_df,hue='Sex',kind='count',order=[1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def male_female_child(passenger):\n",
    "    age,sex = passenger\n",
    "    \n",
    "    if age < 16:\n",
    "        return 'child'\n",
    "    else:\n",
    "        return sex\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "titanic_df['person']=titanic_df[['Age','Sex']].apply(male_female_child,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>person</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",
       "      <td>male</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",
       "      <td>female</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",
       "      <td>female</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",
       "      <td>female</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",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>child</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",
       "      <td>female</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",
       "      <td>child</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \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",
       "5                                   Moran, Mr. James    male  NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male   54      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male    2      3   \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  person  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
       "1      0          PC 17599  71.2833   C85        C  female  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
       "3      0            113803  53.1000  C123        S  female  \n",
       "4      0            373450   8.0500   NaN        S    male  \n",
       "5      0            330877   8.4583   NaN        Q    male  \n",
       "6      0             17463  51.8625   E46        S    male  \n",
       "7      1            349909  21.0750   NaN        S   child  \n",
       "8      2            347742  11.1333   NaN        S  female  \n",
       "9      0            237736  30.0708   NaN        C   child  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10a257a90>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V798H+Djwe1Tra1yRmW8CDo+ILuDVwPmZeTTVqnanbdb+NcCH6rU3bgYuGEtRjfecM/Nx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\nD23ZhZkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a71c278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Pclass',data=titanic_df,kind='count',hue='person')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a7b8780>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAECCAYAAAAMxDf2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0nyBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aa3fe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "titanic_df['Age'].hist(bins=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    446.000000\n",
       "Survived         0.383838\n",
       "Pclass           2.308642\n",
       "Age             29.699118\n",
       "SibSp            0.523008\n",
       "Parch            0.381594\n",
       "Fare            32.204208\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      537\n",
       "female    271\n",
       "child      83\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['person'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.00    30\n",
       "22.00    27\n",
       "18.00    26\n",
       "28.00    25\n",
       "30.00    25\n",
       "19.00    25\n",
       "21.00    24\n",
       "25.00    23\n",
       "36.00    22\n",
       "29.00    20\n",
       "35.00    18\n",
       "27.00    18\n",
       "32.00    18\n",
       "26.00    18\n",
       "16.00    17\n",
       "31.00    17\n",
       "34.00    15\n",
       "20.00    15\n",
       "23.00    15\n",
       "33.00    15\n",
       "39.00    14\n",
       "40.00    13\n",
       "42.00    13\n",
       "17.00    13\n",
       "45.00    12\n",
       "38.00    11\n",
       "50.00    10\n",
       "2.00     10\n",
       "4.00     10\n",
       "48.00     9\n",
       "         ..\n",
       "0.75      2\n",
       "28.50     2\n",
       "0.83      2\n",
       "63.00     2\n",
       "30.50     2\n",
       "40.50     2\n",
       "71.00     2\n",
       "70.00     2\n",
       "64.00     2\n",
       "45.50     2\n",
       "59.00     2\n",
       "57.00     2\n",
       "55.00     2\n",
       "32.50     2\n",
       "20.50     1\n",
       "14.50     1\n",
       "55.50     1\n",
       "0.67      1\n",
       "0.92      1\n",
       "24.50     1\n",
       "36.50     1\n",
       "23.50     1\n",
       "80.00     1\n",
       "12.00     1\n",
       "74.00     1\n",
       "70.50     1\n",
       "0.42      1\n",
       "66.00     1\n",
       "53.00     1\n",
       "34.50     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10af00c88>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p45MVt1Dgy/vAdi+8MUpbZ5SCXAc76mStTbH62A07ZWmVlHorGJ4ZpDncSG+s\ni2cGnuC14ReoC+5gU/o2vPa53+/askzK8wO8eKKbhrYR/s+fHOWWnSV8dn8lbpeMWxZCCCGuVZJw\nCnGJWsba+VHD/YzPTFCeXsKtZTfidXyw5fLtc5McOz1JesDG/p0BbJJsilXMMAxy3LnkuHOZSoRp\nmdJ0RFp4Y+QVjo4eQvk3sTW4k2x3CK/bwSf3lLGhPJODb3Zy8M1OjjYO8IefUNRVyRIqQgghxLXo\nopMGKaVswA+AOiAGfF1r3TKv/A7g20ACuFdr/UOllBO4FygD3MB/01o/oZRaB9wHJIEG4E+01gvN\nWCSTBlnMtThw3TRNXuo6xCPNv8Y0Ta4r2El93tYPbbXs6Inyi1/343Aa3HZ9On7f8rfsrMYJata6\ntR6TeDLOhUgrzVONRGanACjxlrM1uIuytEoMwyCeSHL4TB9vnusnaUK9CvGlm2vIDKzMWOZr8dpl\ndRITa5K4WI/ExHpk0qDFWaiF87OAS2u9Vym1G/hu6jVSieVdQD0QAQ4ppR4HPgkMaq2/opTKBN4G\nnkht+y2t9StKqbuBO4HHluOkhFgq0USMf2l8kOMDp/A6vHyy/GaKA4Ufuu3oeJxHnxkA4MAu/1VJ\nNoVYCU6bkyq/otJXTW+0h5apRjqn2+mcbifDmcXW4E7WBzZxYEshtWVzrZ3H9CANbSP87g1V3LCt\nSFr+hRBCiGvEQgnnPuBpAK31EaVU/byyWqBZaz0OoJR6DTgAPAg8lNrGBsRTj7drrV9JPX4KuBVJ\nOIWF9U8N8M+nf0pfZIACXx6frLgFv9P3odtGY3Mz0kZjJru3+sjNcX7odkKsJYZho9BbTKG3mLH4\nCC1hTed0By8NHeTwyEtsSt9OXXA7X76lmpMtw7z8djf3P9PE6w19/Nvb1lOS61/pUxBCCCHEMlso\n4UwHJuY9n1VK2bTWyVTZ+LyySSCotZ4CUEoFmEs8/yZVPv92dhgIXkoFQyGZ5dBqroWYHOk6wfff\n+gnRRIxdxVu5teoAdtuHt1jOJk0efrCTkfEEdbU+tmy6pF/tJeX3yyy4VnOtxcRPIcWZhUwnIuix\nRprGzvHW2GFOjB2hNnMje2r3smPDDp481Map5iH+jx8f5XM3VPFvblF43FdnOoFr4dq12khMrEni\nYj0SE7GaLfQpPwHM/w1/J9mEuWRzflkAGAVQSpUAjwDf11o/kCpPvm/bsUupoPRZt5a1Po5gNjnL\n461P89zg3kpiAAAgAElEQVSFl3HYHNxWdhMqax2TE7GP3OfZQyOcb5+iMM/JhhrXVR+7t9bHC65G\n13ZMbKzzbKAiT9EZaad5qpEzo6c5M3qafHcR22p3sa6wnOfe6uHhF5t54VgnX7qpmh0qtKyzOa/1\na9dqJDGxJomL9UhMrEduACzOQgnnIeAO4EGl1B7g1LyyRqA6NU5zirnutN9RSuUBzwD/Xmv94rzt\nTyilrtdavwzcDjy/VCchxFKYnAnzo4b7OT/WSoYrnU9VfoIcb9ZF9zlxdpK3GiYJBmzsq/fLuDQh\nUuyGnXJfFWVplQzO9NMcbqQv1s1T/Y8ScKRTv38H4a583jo3xg8ea2BDeSZfvqWGguwP77YuhBBC\niNVpoVlqDd6bpRbga8AOwK+1vkcp9Wngb5kbq/kjrfXdSqnvAV8A9LxD3Q6UAPcALuAs8O9kltrV\nZ63eZWsb7+Ce0z9jfGaCymA5t5bdgNt+8dk0O7qj/OLJfpxOg09cpRlpP8y13ZpmTRKTDzcZH6dl\nSnMh0sYsszgNJ5WejQy1FNDVZWK3Gdy2u5RPX1e+5Gt3rtVr12omMbEmiYv1SEysR2apXZyLJpwW\nIAmnxay1i55pmrzafZiHzj9O0jTZW7iLHblbFuzaNzoe575HeonHTW7cF1jRSYIkubEeicnFzSRj\ntE010zrVRDQ5DUDIKGekpYDwUDqZAc+Sd7Nda9eutUBiYk0SF+uRmFiPJJyLc3VmahDCgmZmZ3hA\nP8qRvrfw2D3cXnETpYHiBfeLxpI8+PQAsRmT3dtkRlohFstlc6MCG6n219I9fYHmqUYG4+1Q2U5m\nWSbhCyX84FfTbCzL5velm60QQgixqknCKa5JQ9PD/POpn9I91UteWohPVtxCumvhAeDJpMnjzw8y\nMpZAVbmpKluZReyFWAtsho2StHKKvWWMxIdoDjfSE+3CUTGKs1Sje0v49n19fHxzFZ/ZX04gzbXS\nVRZCCCHEIknCKa45DUPnuO/sz5lORNmUXcv1xftwfMSSJ+/38ptjtHZGyc91sG1j2jLXVIhrg2EY\nZLtCZGeFiCSmaJlqoj3SjLO4GQpbeWW4idd/Uslndmzhph3FOOy2la6yEEIIsSKUUnbgH4FqwAs0\nAd/UWs+saMUuQhJOcc1Imkl+0/YsT7U/j92wc3Pp9WzMXn/J+59uCnPk5AQBn4399X5sNum+L8RS\nS3P42BzcRm1gExem22gOa6ZC3RDq5tGeBp7RNfybnfupV7nLuoyKEEIIYVG3AWitbwVQSv3fzE3s\n+v+tZKUuRhJOcU2YnAnz4zP/ih5tJuDy86mKW8lLC13y/t39MZ56eRin0+D66wK4XNLCIsRyctic\nVPpqqEirpj/WQ9NkI8Pp/UTTj3Bv6ykea1T84c5bUEWX/ncshBBCrAFdwAGl1B3Ai8C3gKRS6j8D\nnwYM4L8CR5lb4vIW5pavvE1r/dWVqLAknGLNax1v54en72d8ZoLy9FI+UXYjHselj72cCCd4+OAA\nZhL27/GT7l+Z5U+EuBYZhkG+p4h8TxET8THOjTXSk2xnzPM23zt7mlBDNV/ccgsb8ktWuqpCCCHE\nstNan1RK/SXwDeDHwGHg/wH2a633K6X8wKta621Kqb9IbZMB3LhSdZaEU6xZpmnyYuerPNryG0zT\nZG/BLurzti6qG148nuThgwNEppNs35xGQa7MSCvESkl3ZrA7tIdYchsNA01ciJ9nyNnI9882Emwo\n4s71N7GzaAM2Q3ogCCGEWJuUUpuA41rrzymlbMB/Ae4DTKXUi6nN3EqpLK31M0qp7wK/1FpPrVCV\nkU9lsSZNJ6L8sOFnPNz8azx2N59f92l25m9bVLJpmiZPvjRM/1CcqjI3qlJmpBXCCtw2NzvyN/OZ\nkjspT+6GqUzGbd38tOmn/PULf8/B1leIJmQdVCGEEGvSLcDfAWitk0ADoIE3tNYfB24HfgGMKaW+\nCTwLfEopVblC9ZUWTrH2dId7uef0TxmcHqbQV8AnK27C51z8On6vHx+nsTVCKMtB/ZY0maBECIux\n2+xsK65iS7KSM519NIebiAR7eLz91zzZfpA9+fXcVL5/UeO1hRBCCIv7J+B7SqkTwBQwCPwB8A2l\n1CtAAPghUA58HbgO2M5c19rrV6LChmmaK/G+l8ocHJxc6TqIeUKhAFaNiWmavNp9mIfPP0HCnGVH\n7hb2Fu66rO51ja1TPPbsEGleG7fdkI7Hbd3OAH6/h3BYWnOsRGKyMmZnTRrbx2kcO4+RfQHDFQOg\nJqOaz2++hSJHqXS3tRArf55cyyQu1iMxsZ5QKCCtEIsgLZxiTYjEI9x/7kFODp3BY3dze9nNVAbL\nL+tYPQMxnnhhCIcdrt/jt3SyKYR4j91usLEqg5pEPbp1A43dHZDTQRPn+YdXz5PlzuTjJfvYU1BP\nmlPW0RVCCCGuBkk4xarXMtbOj8/8C6OxcQp9BdxWfiMBl/+yjjU+meChpwdIzsKBPX4yg/InIsRq\n43QYbKrxUVO+nqa2CrTuJ5nVwXB2Lw83/5pftTzNrvztHCjeS0mgcKWrK4QQQqxp8m1arFpJM8kz\nHS/yZOszmMDu/B3syt9+2V3mYjNJHnz6vRlpi/JdS1thIcRV5XLZ2KS8rK8qo6uvmBONI8T9nZi5\nF3i9901e732TivQyri/ey9bczTht8pEohBBCLDX5dBWr0lhsnJ+ceYCmsRZ8Th+3l99Ikf/yWyqS\nSZPHnx9kaCROdYXMSCvEWuJwGGxa76e00E57V5AzTZVM2ftx5F2gzeygbaID//nH2Ve4m/1Fu8ny\nZK50lYUQQog1QxJOseocHzjFzxsfIZKIUJFexi1lN+B1eK7omC8cHqXlQpT8kIMdm2VGWiHWIpvN\noLLUTUWJi65eH2ebChjpmMSee4FwbjcHO17gmY4X2Zi9no8V7WFDtpJJhoQQQogrdNGEM7WY6A+A\nOiAGfF1r3TKv/A7g20ACuFdr/cN5ZbuBf0itB4NSahvwBHA+tcndWutfLuG5iDVuKh7hl02Pcaz/\nbeyGneuL97ElZ+MVJ4fHz0xyrGGS9ICN/bv82GySbAqxlhmGQUmhi+ICJ4PDaTS1Bel8uxpbZi/O\nvE4aOEfD8DmyPJnsL9zNdYU7SXcFVrraQgghxKq0UAvnZwGX1npvKoH8buo1lFJO4C6gHogAh5RS\nj2utB5RSf83cejDhecfaAdyltb5rqU9CrH1nhhu5/9yDTMxMkpeWyyfKPk6mJ+OKj9vaOc2zh0Zw\nuwxu2BPA5ZTWDCGuFYZhkJvjJDfHSWTaR3O7j+bmEqL2MRy5nYzm9PJ469M82fYMW0Kb2F+4h+rM\nSmn1FEIIsWYopb4KKK31f1mu91go4dwHPA2gtT6ilKqfV1YLNGutxwGUUq8BB4CHgGbg88DP5m2/\nfW4zdSdzrZx/rrWen5AK8QHRRJRHmp/kUM8RbIaN6wp2Up+3dUm+8A2OzPDYs4MYBhzY7cfvsy9B\njYUQq1Ga10ZdbRoblZfOnjSaWrMY6lTYs3tx5XdyfOAUxwdOkePJYl/hbnYX1BN0S6unEEKIVc9c\n7jdYKOFMBybmPZ9VStm01slU2fi8skkgCKC1fkQpVf6+Y70J3KO1PqGU+hbwd8BfLVTBUEg+0K3m\nasXk7MB5vn/sJwxGhsn15fC5DbeR7w8tybHDUwkePtjNTNzkxn0ZVJSt7jX5/P4rG8Mqlp7ExHou\nNSbBdC+b1gcZGolztilIy9lSZj2j2EOdDGX386vWp3ii7SD1hXXcXLWfurxabDZp9bwc8hlvTRIX\n65GYrD53/MdffQf4whIf9sEnvnvnR+ZPqdbKOwAPUAB8D7gT2AT8JVAKfA7wAUOpx8a8/f8U+BJz\nSegDWuv/uRSVXijhnADm/4a/k2zCXLI5vywAjF7kWI++0xoKPAb846VUcHBw8lI2E1dJKBRY9phE\n4hF+1fIUh3qOAAY787axK38HjoSdsbHIFR8/NpPk50/0MzaRYJPykB+yEQ5Hr7ziK8Tv96zq+q9F\nEhPruZyYeFywfZOHuvVuOnt9tF4I0X9hGnt2D87cLt7sfps3u98m0x1kb+EudufXk+2VGW4v1dX4\nPBGLJ3GxHomJ9Vj8BoBPa32bUuqLwF9orfcopW4A/gI4BtystTaVUk8DO0m1cCqlNgC/x1wPVxvw\njFLqoNa66UortFDCeYi5LPlBpdQe4NS8skagWimVCUwx1532Oxc51tNKqT/TWh8FbmLuhIV4l2ma\nHO0/wcPnnyAcnyLLk8nNpddT4MtbsveYnTV59JlB+oZmqCx1sXm9d8mOLYRYmxwOg4oSNxUlbqYi\nPto702lpK2eKMRyhTkaze3my7VmebHuWmsx17CvYSV1oEy67c6WrLoQQYoWkWiIX7M25xEzg7dTj\nceBc6vEY4ALiwM+VUmGgGJj/QbURKANeSD3PANYBy55wPgrcopQ6lHr+NaXUlwC/1voepdR/AA4y\nlwX/SGvd+7795/cJ/gbwfaVUHOgF/vhKKy/Wjv6pAX6uH+H8WCsOw86+wt1sC23Gblu6cZWmafLr\nF4do745SmOdk11afLH8ihFgUX5qdjcrLhhoPQyN+2jpzudAQYTbQiz3URRPNNI0247K52Z2/nesK\n6ykNFMu1RgghxNXyUWMy3cBnUy2eacw1/s3/cNLAGa317QCpPO/UBw+zeBdNOLXWJvDN973cNK/8\n18CvP2LfdmDvvOcngf2XW1GxNs3Mxnmm4wWe6XiJWXOW8vRSPl68n/QlnozDNE2ee32Ucy0RcrLs\n7N8py58IIS6fYRiEsp2Esp3U16XRNxiks6eKzs4RkhldmDk9vNpzmFd7DpPtCrG/eCe7CraR4Q6u\ndNWFEEKsbea8f+c/jgNhpdQrzI3fPA4UvlOutT6llHo+NRGsB3gD6FmKChmmuewTE10JU/qsW8tS\njSMwTZPTQ2d5+PwTDEVH8Dt93FC8j8pg+bK0BBw+Mc7Lb44RDNi4+WPpuF1rZ4IPGS9oPRIT67la\nMUkmTfqHEnR0R+me6sHM6MKWMYBhM8E0KHCXcn3pbnYX1eGyu5a9PlYm49KsSeJiPRIT6wmFAtJq\nsQgLdakVYsm1jXfwaPOTtIy3Y2CwPbeO3fn1yzbe6WTjJC+/OUaa1+Dje9dWsimEsBabzaAg10lB\nrpNksoaB4Urae8P0xjpIBrvpNTp4oLmDXzQ9QrFzHQdKd7G7rHZJhw8IIYQQViIJp7hq+qcG+FXr\n05wcbACgIr2MfYW7yPZmLdt7nm+P8PQrI7icc8lmmleSTSHE1WGzGeSHnOSHMjHNDMYmNtE+MEJ3\nrJ2Yr4tOWyP/0tbIvzZ5yLdVsyt/G/vWKXxemWxICCHE2iEJp1h247FJftP+LK93HyGJSX5aLvuL\n9lDkL1jW9+3sjfLYc4PYbHDDdQGCAWlBEEKsDMMwyAw6yAzmso1cpqM7OD/YS3e0jWl3L7320/xq\n8DSPXvARnCmnLruOnZUVVBSk47DLjTIhhBCrlyScYtlMzoR5sfM1Xux8lZlknAx3kH2Fu6gKViz7\njI2DIzM89PQAySRcv8dPTpb8qgshrMPrsVNXUkwdxcRnEzQPd9E53c6Uu49J7xkOJc7w6vEgxngR\nVT7F1rISassyKchOkxlvhRBCrCryLVwsucHIMM91vswbvcdIJBOkObzsK9rDxmyF3Vj+Vsah0Rl+\n/ut+YjMm1+3wUZh3bU/MIYSwNqfdQW1uObWUE0/G6Zy6QNtkGxO+AfCP08pZzndlMHsqn7RoCRuK\nCqkty2R9aQahDK8koEIIISxNEk6xZDomOnm24yXeHmzAxCTdFWB7bh0bshVO29UZkzQ4MsPPn+gn\nEk1SX5dGRYn7qryvEEIsBafNSWWgispAFdHZaXqiXVwItzPqH8QeGCNBIycmMzl2PJ/Z5/LI8gSp\nLc9kfWkmtWWZZKV7VvoUhBBCiN8iCae4IkkzybmR8zzX8RJNYy0AhLzZ1OdtZV1GJTbj6o09Ghie\na9mcjibZuSWN6gr54iWEWL08di+VvmoqfdWp5LOTrkgHw4FB7IFRKD1HZCqTI4O5vN6UhxlLIyfo\nYX2q9XN9qSSgQghxLVFK2YHnACfwKa31+BIdt09rnX+5+0vCKS7L8PQIb/Qe43DvMUZjYwCUBIqo\nz91KSaDoqnfx6h+aSzajsSS7tqaxrly+ZAkh1o655LOGSl8N0dlpuqcv0BPtZMgYxOkfxVmqccxk\nMDmUy6HzIV475QcMSUCFEOLaUgQEtNb1S3xc80p2loRTXLKZ2TivdRzlaf0KTaPNwFz3r43Z66nL\n2UBuWmhF6tU3GOOBJ/uJxkx2b/NRVSbdaIUQa5fH7qXKr6jyK2KzUXqj3fREOxmgD1vhGJ7CJjxm\nOo5wARO9mbx2aprXTvUCEMrwsL40M5WEZpIZkOulEEIsh9/7xTe/A3xhiQ/74C+/ePdfXaT8fwHV\nSql7gQCQnXr9z7TWDUqpZuAQUAM8DwSBXYDWWv+hUmoT8F3ADuQA39RaH37n4EqpzcD3AAMYBv5I\naz2xUKUl4RQXNZucpXW8neMDpzjad4Lp2SgAhb58NmavZ11GJS77yq0Z1zsY44HUBEF7tvuoLJUv\nT0KIa4fb7qHcV0W5r4p4coa+aA890U76Yz1EAxpbAIKGh+BsCYmRXIa7k7x6KsqrqQQ0N9ObmoBo\nrhU06JdrqBBCrGLfBB4ABoA3tdb/SylVDdwLfAwoA24A+oARYJfW+k+VUq1KqSCwAfiPqeT0S8DX\ngMPzjn8P8FWtdaNS6n8D/hr4m4UqJQmn+IBoIsa5kSZODZ2hYegckcQ0AD5HGvtLd1LpqyTTk7HC\ntYSegblkMx6fm41WJggSQlzLnDYXJWnllKSVM2vOMhjrozfaTW+0i0Hbecg5jzPHTqGjBE+0kMhA\nFr19MV5+u4eX3+4BoDAnjQ1lWdSWZaJKM0nzyNcEIYS4HKmWyIu1Ri6Hd8a0bQZuVEp9MfU8M/Xv\nsNa6C0ApNaW1bky9Pg64gR7g20qpaeZaSN8/BrQWuFspBXPjRJsupVLySSIAGI2OcWa4kVODZ9Gj\n50mYswD4nGlsztlAVbCckkARWZl+xsYiK1xb6O6L8YvfpJLNeh/lxZJsCiHEO+yGnXxPEfmeIraa\nOxmND9OXSj77Eu3gaIdCyC3LJWQrxZjMY7jPS/fgFD1DXTz3VheGAeX5ATaUzyWg64qCuJzLv7SV\nEEKIK3YOuF9r/XOlVBHwpdTrFxuLaTDXXfbLqRbM/wqUv2+bRuArWusupdQB3uuye1GScF6DTNNk\ncHqI5rE2msfaOD/awkhq4h+AbE8WVcFyKjPKyfXmWG6Nt5YL0zz27CCJWZO9O32UFUmyKYQQH8Uw\nDLJcOWS5ctiQvoWpRJi+aDd9sW6GYgMMMwAucJd72FhbRfpsMbGRTLr74nT0TdLWO8mThztw2A2q\nizPYVJHFxoosinP92Cz2+SCEEAIT+L+AHyml/hhIB/5uXhkXeXw/8KBSqhM4BhS8r/ybwM+UUo7U\na390KRUyTPOjE12llA34AVAHxICva61b5pXfAXwbSAD3aq1/OK9sN/APWuuPp56vA+4DkkAD8Cda\n64VmPDIHBycv5TzERUQTUbrCvXRN9tAy3sb5sVYmZ8Lvlrvtbop8+RQHiqgMlhF0p3/ksTIy0la0\nhfNYwwTPvz6KzYC99X5KCl0rVher8Ps9hMPRla6GmEdiYj0Skw+XSMYZiPXTH+umL9pDNDk3hMLA\nINddQLGnAnc0n7EBLxf6wgyOv/d/GEhzsrEii43lWWwoz1r0BEShUAD5jLceiYv1SEysJxQKyN22\nRViohfOzgEtrvTeVQH439RpKKSdwF1APRIBDSqnHtdYDSqm/Bv4ACM871l3At7TWryil7gbuBB5b\n2tO5tpmmycRMmK5wD12T3XSFe+ic7GZoevi3bmGkOdKoyaii0F9Akb+AbE+m5Vox3y+ZNHnu9VGO\nn5nE4zY4sDtATpY00AshxJVw2JwUeosp9BZjmibjiVH6oz30RXvoj/XSH5sb2+nJ9FJWVMkORxmz\nEzn09CVo75vgjTP9vHGmH4DCHB+bKrLYXJVNTXEGTsfVW4dZCCGEdS30jX0f8DSA1vqIUmr+mi61\nQPM7C4oqpV4DDgAPAc3A54Gfzdt+u9b6ldTjp4BbkYTzsswmZxmcHqY/MkB/ZJD+qUH6IwP0RQaZ\nTk3w8w633UWRv5CQN4dQWjYFvjyCrnTLJ5jzRWNJfvXcIG1dUYLpdm7Y48eXJuOIhBBiKRmGQYYz\niwxnFiqwiZnkDIOxvlTy2YMOn0FzBoBQcR7baioJJosID/vp6J2iczBMz9AUzxztxOW0UVuWSV1l\nNpsqswlleFf47IQQQqyUhRLOdGD+2iqzSimb1jqZKps/c9Ekc2u5oLV+RClV/r5jzc9wwu9sKz5c\nNBFjODrC4PQwQ9PDDE2PMBgZYmh6mJHYGEkz+Vvb2zBId6dT4Msjx5tFrjeHkDeHgMu/qpLL9xub\niPPgUwMMjyUozHOyr96P07l6z0cIIVYLl81FkbeUIm/pXA+axBh90R4GYr0MzQwyODPXsul0OSla\nX8ZNWyuwR3Lp7zdo75nkZPMwJ5uHAcjL8lJXmcPmqixUSQZOh9w0FEKIa8VCCecEc1PivuOdZBPm\nks35ZQFg9CLHmp8hBYCxj9pwvlAosPBGq1DSTDIWnaA/PEh/eIiBqSH6wkOp54NMxMIful+a00th\nII8cXxY53kyy07LI8WWS6Qlit12dD/CMjLSr8j4dXRF++lg/kelZNq33sWd7OjabJJsfxu/3rHQV\nxPtITKxHYnJlAngpSs0fEU/G6Y/00hPppmeqm/ZIM+2RZgDSc4Ksr6om11FKbCSDtq5pWrvGefZY\nJ88e68TtsrO1OsTODXnU1+aRHZTWT6tZq9+9VjOJiVjNFko4DwF3MDdb0R7g1LyyRqBaKZUJTDHX\nnfY7FznWCaXU9Vrrl4HbgecvpYKrfZD0VDzCQGSQ/sggA5Ghdx8PTg8RTyY+sL2N/7+9O4+R5Drs\nO/6ts6uv6e7pOfckd5f7eImURMqSLYWW49uxYMEIAtjKISJ2ZNhwHNixECu2AweBY0SwAAuxFUeH\naQHOYTuxLCXW4ciSaFEWKVGkSPGo5XK55JK7O7Nz9Uz39F2VP6qnZ2avWZI7070zv4/Qqqququ43\nfNvHr9+r9yzyfo5D+QMUUiMU/JFk2Vv3He/SJ2nBSqu5A3/Nzg0a9NRzNf7qy3NEMbzl7gy33Jxi\ndXVn/sYbjQZDGT6qk+GjOrn+ikxQzExwe+ZN1DpVZpvnmG2e50LzPI/NfZNkgEOYODjNW265iaA5\nyeJMhtNnazz81Hkefuo8AAcnctx9rMxdR8c4Mq0fFgdNA9QMH9XJ8NEPAK/OVoHzL4AfNMY81Nu+\n3xjzU0AuDMOPGmN+Gfg8YAMfD8Pw3EXnbxyr5leAjxpjfOBpkms9d41Gp8HZ2gyvVM9xtnqes9Vz\nnK3NsNq5NJy5tksxVaCYKvQCZb4fKPN+DtvauwMttDsRX354iUe/s4Lnwn3flWd64jIhW0REhkbW\nzXGzews3Z28hjiMW2wvMNs8z2zzHhd4SwC267Js6xG3OQdzGJKdPRbw8W+PMbJX/87UXyQYudx0d\n4+5jZe68uUwm0OBwIiI3uqtOizIEhnJalGqrxunllzi9fIaXq2d5ZeXspnksIRlSfsTPMxoUKaaK\nlIIkYJaCIlk3c8NeV7mdLZznLzT59BfnWKh0yOds7vuuPIURXeezFbXcDB/VyfBRnQxOO2oz15rp\nBc/zrHTWh4ZI2xn2B4cJWlNULxR46UyHWiPp/WPbFscPFnjj0THuPjbG5OjOXM6x16k1bfioToaP\npkV5dRQ4t9COOry8crYXMF/idOUl5hoLm45JuwFjQZmx9Cjl9Chj6TKjQRHP3n0tc9sROKMo5u8e\nq/DQoxWiGI4fSfHGOzK4jl7L10JfpIeP6mT4qE6GR727yoXmeRa7F3il9grNaL1eCm6JMecA0XKZ\nuZezzM53+/smR9O88dgYdx8d49iBAq6zd3sDbSeFm+GjOhk+CpyvjvqqXKQbdXlx5WVOLJ4kXDjJ\nqcppOvH6B17KSXEof4Cp7ARTmUkmMmNkPf3q+lotLLX5zJfmODfbIhNYvO3NOabUhVZEZNdKOxkO\nZY5we+52VlbqrHQqvWs/Z5hrzVDpPAkp4CgcuHWSTHuK1bki51+K+fwjZ/j8I2dI+w53Hilz97Ey\nbzhSJp/xB/1niYjIFez5wBnFEa9Uz/cD5nOVU7S6rf7+sWCUfbnpXsCcoJgq3LDdYYdJHMc89nSV\nv/n6Ip1OzOEDPm+5K4Pv6xdrEZG9wrIsRrwiI16RY7lbieKIxfZ8v/vtQusC88zAGKTGbCbtKZza\nOAtnc3zj2TbfeHYWCziyf4Q3HhvjrqNjHBjP6nNaRGSI7MnA2eq2CRef48m5Z3hy7mmWW+vdFIqp\nAqZ0jIP5/RzITZN2NVz79bZS6/BXX5nnhTMNfM/i7W/Jcnh/atDFEhGRAbMtm7I/Ttkf59b8G+hE\nHeZbs8w2Z7jQPMdC5yykz8JRyB/1yXUnaS2WeOH8Cs9/pcL/+sopSvkUdx0tc9eRMrfdVCLw9+RX\nHRGRobFn3oWXWyt8pxcwn1k40Z+SJHACbhs9zqH8fg7k9pPzswMu6e7VaEY88u0Kjzy5QqcTMzXh\n8rY35cik1aopIiKXcm2XyWAfk8E+4E00uw3mWrP96VeWOANjZ0iNgUeA35hgZbbAg88s8pXHz+LY\nFscPFrnraNL1drp84w7aJyJyo9rVgXO5tcK3Zp/g0ZnHeaHyYn+OllKqwJHCTRwp3MRUdmJPT0Oy\nEzrdmG89tcLXvlWh0YwIUhZvvjPD0cMpffCLiMg1SzkB+9OH2J8+BMBqp8aFVnL952zzPLXgJexD\nEF7/PCYAABx+SURBVBwCL8rCyhgn5go887ej/M+/CSiPBNx1tMydN49y6+ES6dSu/hokIjIUdt07\n7Wp7lccvfIdvzDzOc4vPE/di5r7sNEeLN3HzyGFKQWHApdwboijmqZM1HnxkiZVaF8+Fu29LY44G\nuK6CpoiIvD4ZN8th9yiHM0eJ45iVzjJzrSR8zjVnaBdexO995LvtEWpLJb5yepQvPTGKHXkc2Vfg\njptHuePmUW6ezuPY+gFaROR62xWBs9Vt80QvZD6zENKNIwCmMhMcLx3jltIRcp66yu6UOI55/qU6\nX354ibnFNrYNtx4LuON4QEqDAomIyDZIBiAqMOIVOJI9ThxHLLWX+i2g89Ys1vgyqfEXIQanWeTF\nxRKnnhzlL79WIu2muO2mUe64qcRtN40yWUqrF46IyHVwwwbOOI45vXyGr5/7Bt+ceZxGtwkko8oe\nLx3jeOkohdTIgEu5t7Q7ESdeWOVbT63wykwy0u+RQz5vuDVNNuMMuHQiIrKXWJZNyR+l5I9yPHc7\nUdxloTXfawGdYdGaw51ewp1+AWILq17kicUSj/9dmeivixQyaW47XMIcKnLroRITCqAiIq/JDRc4\nK80VHjn/KH937pvMrM4CkPUy3Dv2Rm4bPc5oUBpwCfeemfkW336mylPPVWm2ki7M+6c87r49TXHk\nhvsnJiIiu5BtOYylJhhLTfRHwF1oz3GhOZMMQGQt4GUWYf8piG1atSLfXBrlkTOjRJ8vUsgE/QBq\nDpXUAioico1uiDQQxRFPzT/LV195mKfnnyUixrZsbike4fay4VD+gAb+2WHNVsTTJ2t8+5kVzs+1\nAQhSFrffEnDkcIqRnFo0RURkeLm2y0RqionUFHA37ajNfGu2F0BnqOQW8HILycGxTataSgLoS6NE\ntQLZlM+xAwWO7U9uN02PkPL02ScicrGhDpwL9SU++8KX+OrZh1lqVgCYSI9xe9lgSscI3GDAJdxb\n6o0uZ8Jlvv30Es+eWqXTibFIWjOPHk6xb9LDtvVrr4iI3Hg822Mq2M9UsB+AVtRkrjnLXGuGC81Z\nlvPzePl5AKzIIV4t8dRiiScfHSV6sIBtORwcz3LsQJGj+0e4eWqE8VIaW62gIrLHDXXg/PnP/Fui\nOMKzPd4wdjtvKN/GeGZs0MXaM1rtiDPnmrz4SoPTr9SZnW/392UzNnfcEnDzoZTm0RQRkV3Ht1Ps\nSx9kX/ogQH8O0LnmDBdas6zYc3i5OQCs2MGplzm7WOBMOMoXv1WA2CbwHQ5P5blpKt9bjjChECoi\ne8xQB87xbJk7SrdiSsfwHX/QxdnVut2YykqHhUqbs7MtXnylwbnZJlFv8lLbhokxl0P705QKMFZy\nde2KiIjsGRfPAdoPoL0QumzN4mZmcfcnATTVLtOplHhuboTwTBHipLtt4DscmsxzaDLHgfEc+8ez\n7B/LEvhD/ZVMROQ1G+p3t/fd+x4qlfqOPmccx3S7yTWKzXaULFsRzVZM1E1m9Yzj5Lj1dYAYsHBs\ncB0Lx7H6S6e/Da5r47sWrmvhudaOhbY4jmm1YxrNiKWVDotLbRYqHeaX2ixU2lSWO/1wCWABoyWH\nyXGPqXGPsVEX17HI5QKq1caOlFlERGRYXUsAZXyW1DhY2OSicezVMeoLI5x4pcmJM0ubHm+sEHBg\nPMeBiSz7x3LsH8syOZrGc3VdqIjc2K4aOI0xNvAHwF1AE/iZMAyf37D/XcBvAB3gE2EYfuxK5xhj\n3gR8Bniud/pHwjD806s9//UOY612xHK1y3K107v11lc6LNe6/XAZRdf1aa/KdZLguRZAXTcJqv1l\nb92xk6VtW72AC/RCb28VgChOwnKjGdFodmm24v4yji9XAvA9i9Giw0jeIZ9zKOQdJsZcfE9dZUVE\nRK7FxQE0uQb0AnOtGeZas1Tas5CbgRxkDlkUnHFy3UmojVKfH2F+scPjJ+d4/ORc/zEtoFwImC5n\nmBrNMlXOMD2aYaqcoZD11dNIRG4IW7VwvhvwwzD8HmPMW4Hf7d2HMcYDPgTcC6wCDxljPg28A0hd\n5px7gA+FYfih7flT1jVbERcWWszOt3vLFvNLbRrNKyQukhFWPc8ik3bwvCT8+Z6N64Lv2XiuhdP7\nkXHt/d2yLCwAK/lQiIEoiulGEHVjogi60fqy2026rna6yXqnk6x3OjHdbkytHiXHd9nU2vhaOTb4\nvoXvWeSyNr5n43sWmYzNSM4hn0uWKV/BUkRE5HpKrgE9wL70AQDaUYv51gXmWxeYa86y2J5jiVnI\nAlkoHS1jvANkuxNQLVFd9phfbrCw0uTJUws8eWph0+MHvsN4Mc1kKc14Kc1EMc1EKcNEMU1pJKXr\nREVkaGwVON8OfA4gDMOHjTH3bth3G3AyDMMKgDHmq8B9wHcDn73MOfcAx40xP0HSyvmvwjCsvt4/\noFbv8vL5JjNzSbCcnW+xXO1uOsayIJexKRYcsmmHbMYmk7bJpm2yGZt0YOM4w/XGHMVJ8FwLqv0g\n20uiVv//wFpLvCR/axKWraH7m0RERPYqz/Y3jYLbiTostueZb80y15xloT3HYnu+dzDkJvLsO3yQ\nO4MDlJ1pqI+wWG2ysNxkYaXJwnKDc/M1zsxe+lXKsS3GigETxQxjxYDxQpqxQsB4MU25EJANNA6D\niOycrQLnCLC8YbtrjLHDMIx6+yob9q0AhSuc4wAPA/81DMPHjDEfAP4d8KuvprBxnAxsc+ZckzPn\nm5w512Cx0tl0TJCymBp3KRZciiMOpZGkq+iNFr5sy8J2weXGKreIiIhszbVdxlOTjKcmIZ/MOV5p\nLzLXmu23hJ6oPs2J6tMA+JbPVHCAfYcOcG9wgMnUUVzLo9bosLjSZKma3JL1FkvVJjMLlx8HI/Cd\nfgAd64XRsUJAuRCQzmnKORG5vrYKnMtAfsP2WtiEJGxu3JcHlq5wTtcY86kwDNeukP8U8OFrKWAn\ncnnudJUXztR54UyNldp666XnWhyYTjE96TNe9hgtemTSurh+u+X0YTR0VCfDR3UyfFQnw0d1stkI\nGQ6StIDGccxKe5nZ+kzvdp6X6qd4qX4KSHo3TWamOJQ7zIGxQ9x90yFG/MKmx2s0kzC6uNJgYbmR\nrC8n6zOLdV6+ULtsOTKBy0Qpw+RocpsY3bBeypBNe9v7H0IuMT6e3/ogkSG1VeB8CHgX8GfGmLcB\nT2zY9yxwizGmBNRIutN+kORSxsud81ljzL8Mw/AbwPcD39yqcP/pD0+ysLQ+92OQsji0z2e87DJe\ndikWnE3XKETdNtVq+3IPJdeJRqkdPqqT4aM6GT6qk+GjOtmaTYop5xBTuUOQS0bCTVo/51hozTK7\nOsP51XM8wtcByDo5poODTAf7mAoOMJ6aJO06pEtp9pXSmx47jmPqrS6VaovlWpNKrUWl1mK11WVu\nsc4rF6qcPrd8uWKRSblJq2hxvavueHGtxTTQyLrX2fh4ngsXVgZdDNlAPwC8OlsFzr8AftAY81Bv\n+35jzE8BuTAMP2qM+WXg84ANfDwMw3PGmEvO6S1/Dvh9Y0wbOAf8i60Kt1LrcHDaY3rSY3LMI5e1\ndc2BiIiI7EkpJ2Bf+iD70gcB6MZdFlvzLLTmWGjPMd+6wMnaM5ysPQOAYzlM+NO9a0f3MRXsJ+cm\nX5QtyyKTcsmkXKbLmf5zFIsZlpZWk0Da7FCptan0AulyrUWl2mKp1uTsfI2XLnP9KEAh6/dC6HoQ\nnSxlGC+lGcl4+i4nssdY8ZXmyhgCf/noI/HqanPQxZAN9Iv08FGdDB/VyfBRnQwf1cn1F8cxtW41\nCaCtORZaF6h0KsD6d72kFfRAP4SO+1O49nr7w1rgvJbnWm10qNSS60X7y971oyv19mWnY/M9e9OI\nuuOlNBOlZLTd0XyAbSuMXkwtnMNnfDyvf6ivwlYtnAOlNx0RERGRa2NZFjk3T87NcyhzMwDtqM1S\ne6HfCrrQmuNk7VlO1p5NzsFmzJ9gKtjHZGqaW4KjOHFmy1ZIy7LIpj2yaY99Y9lL9nejuNci2mSx\nF0KXqk2WVppXvH7Usa3+VC8TpUw/iE6MZiiPpHBsTeMmciMa6sApIiIiIq+dZ3vro+HSa5ns1lho\nzbHYnmOhlUzNcqF1nieB/3chGRF3MtjHZGofk8E0k6l9ZN3cq3pex7Yo5VOU8iluumhfHMesNjvJ\naLorTRZ7QXShN9ru+YVVYP6SxxsrBL2Bi5IuugqjIjcGBU4RERGRPcKyLLJujqyb42AvCnbjLsvt\nJRbac6xES8yuznKmfpoz9dP987JOjslgHxOpaSZT00ykpgic9OWf5FrKEHhkA4/9l2kdrTc7/Sle\nFteWvdvM4qVTvVwxjJaSeUcVRkUGS4FTREREZA9zLIeSX6bkl/vX1raiJoutBZba8yy051lszXOq\ndoJTtRP98wpukYlgmoleAB33J0k5r3+qm3TKJZ1ymS5fGkYbrc6mANq/VS8fRu1eGE2uG02vXzfa\nG9TI9zSirsh2U+AUERERkU18O5V0pw2m+/fVu6sstuZZ7AXQpfYiz1Wf4bnqM/1jil6JidQ046mp\n6xpC1wS+y3T58mH04pbRjd10Zxfr8MKlj1fI+utBtJhmrBgwVkjWCzl/0/R7IvLaKHCKiIiIyJbS\nToZ0OtOflmXtetCl9gKL7XmWWgsstRc40X6aE9Wn++eNuAXGU1O9EDrJuD9Fxr00ML7u8l2lZbTZ\n7rLUC59L1VY/kC5Vm5x8pcJzL1cuOcd1LMqFgPFCuj/naHkkoNxbKpCKXBsFThERERF51TZeD7o/\nfQhYn5ql0l5kqb3QC6GLPF8Leb4W9s/NOFnG/GQwo7HUBOP+JAWvhG1tz/WWKc9hcjTD5Gjmkn2d\nbsTyajLH6MXTu1SqLWYWLu2qC8m1o6MjqX4AHc0HjI6kGB0JGM0ny3RKX7VF9CoQERERketi49Qs\nG0NoPVpNQmgvgFY6i7xUP8VL9VP9c13LpeyPM5aaTJb+BGV//DUPTnStXMdOwmL+8l1/m60ulVqL\n5dVWMtVLbcNytcWFpaUrPnbgO5TyKcojQX/U3mI+RSm3vp5Pe1tOQyNyI1PgFBEREZFtY1kWGSdL\nxskyHRzo39+KmlTaS73W0EUq7UVmm+eZaZ7bdH7WyTHmJy2hZX+MUX+cklfGtXfma2zKd5jwk0GH\nLmethXSl1mZltcXyapuVeouV1TbLtRYLy03Oza9e8fEd26K4FkBzPsVcEkSLOZ9CLsWRbkzc7pBO\nuQqmckNS4BQRERGRHefbqU1zhEIyRUu1s0ylvcRyZylZtpd4sf48L9af7x9nYTHiFSn744x6Y/0g\nWvRGdyyIrtmqhRSg1e6yUm9TXW1TrSeBtLraZqXeZqV33/NnK8TxlZ/Hc2wKOZ9CL5QWskkgLa4t\newE1n/awbQVTGR4KnCIiIiIyFBzLoeCVKHilTfc3oybLvRC60q6w3Kmw3K5QaS9yihObjh1xC5S8\nsf5UL6Nesgzs9MBaCH3Poew5lEeuHEqjKKbe7FCttzfdWlHMQqWehNVGm/mzjasGU9uCfObiYOpT\nyPZCaTbFSM6nkPVJaVoY2QEKnCIiIiIy1FKXaQ2N45hm1GC5U+mH0JXOMiudyiUtopC0qBa9UUre\nKAWvRNEbpdhbXs+pW14r27bIpj2yaY/JDfcXixmWlta75PaDaaNNrd6h1kiCaa3eptboBdZGm7Nz\nq7w0U73qc6Z8p9dCmgTRQtZnJOuvL3M+I5lk3XW2Z0An2f0UOEVERETkhmNZFoGTJnDSTKSmNu1r\nRU1WOstUO8tJCG0n63PNGWYvukYUIGUHFLwSI26RgldkxCtScIuMeAVy7giONTwtgRuDKaUrHxfH\nMa1OtCmI1i4OqY2kW+/M4uVH4t0oG7ibwuhaEN287jGS8fHVciobKHCKiIiIyK7i2ynK/jhlf3zT\n/XEcsdpdpdpZodZdodpJbrXOyhXDqIVF1smRdwvkvZFk6Y6Qc0d6yzwpOxi6AX0syyLlOaQ8h9GR\nqx8bRTGrve68q40koG4Mp2vbCytXHwBpje/Z5NM++YzHSNYnn/bIZ5PtfNonl/bIpT2yaTdZBrru\ndDdT4BQRERGRPcGy7P7coTC9aV8cxzSiOrVOlVq3ympvWetUqXdXOdd8hXPNly/7uI7lknVy5Nw8\nWTdHzsmTdfNknSwZN0fGyZJ2MgO9jvRqbNvqh8CtdLsRq80OtUaH1Uab1UZvvZmE0tVGJ7k1OyzN\nNulGV7ngtMcC0oHbL0Mu7ZFJuWQCl0zgkQ2S9Wywfn86ldwC31F33yF31cBpjLGBPwDuAprAz4Rh\n+PyG/e8CfgPoAJ8Iw/BjVzrHGHMMeACIgO8AvxCG4db/AkVEREREtpllWaSdDGknwxgTl+yP4ohG\nt85qt0a9u9pb1qhHderdVRrdOsuNK8/JCWBjk3YyyTQxbpbATidB1EmTtjP9LsKBnSZwAlJ2QBxv\n7zykr5bj2OQzPvmMv+Wxa916Vxsd6s0khK42OtRbHRrNDvVWl3qz07t1qdbbzC3VuYaMuonn2qR9\nl3TK2RREA98h5bsEnkOqv+0k256D7zn4nr2+7tqkfAffdXAdayh/HLgRbdXC+W7AD8Pwe4wxbwV+\nt3cfxhgP+BBwL7AKPGSM+TTwDiB1mXM+BHwgDMMHjTEfAX4C+NR2/FEiIiIiIteTbdlk3CQoXkkU\nRzSiOo1unXq3TjOq0+w2aEQNmlGDRrdOM2qw0JrjQmvm2p73tI1vpUjZASknILADfDuFb/v4dgqv\nt/RtH9/y8Wwf1/LwbA/P8nFtD8/y+sudDFEbu/WW8qlrOieOY9qdiEarS7PdpdHq0mh1esvk1mon\n+5rtLs3W+vrKapu55Qbd7utv07JIguzazXedZOnZfPhf//3X/fh7yVaB8+3A5wDCMHzYGHPvhn23\nASfDMKwAGGO+CtwHfDfw2cuc8+YwDB/srX8W+CEUOEVERERkl7AtO2m9dK4cSiEJVd24Sytq0oqa\nNHvLjevtuEU7atG1OjTaDRpRnZVOhYjodZXRwcGxHFzbS5aWh2u5OJaLYzkblptvtuVgYydLy+6v\nO5aDjYVl2diWjYW9vo2NZVlYvaWNBVjYlkXyv6QrrNXbZu3/LQscsBwLLwAPizxJCFz7K5Lblf4D\nW4zYZdqdmFanS7sT0WpHtDpdWr31Tjei3Vlfti/e7q13ujGdbkSt0e6vy6uzVeAcAZY3bHeNMXYY\nhlFvX2XDvhWgcIVzHDb+G4Fq79ir6kYR3UiVOkxUJ8NHdTJ8VCfDR3UyfFQnw0n1snMsbFJ2mpSd\nJn+V43K5gGq10d/uxh3aUYdO3O7dOnSiDetxh+6GZTfq0Im7yXrcoUuXbtwliru0ozbNuNG/bzd5\nR/n7ubvwFmDr61Jle20VOJdh02tgLWxCEjY37ssDS1c4p2uMiS5z7FW9+563quO0iIiIiIjIDWqr\nIZ0eAn4MwBjzNuCJDfueBW4xxpSMMT5Jd9qvXeWcx4wx39tb/1HgQURERERERGTXsuL4yhfVGmMs\n1kecBbgfuAfIhWH4UWPMjwO/SRJcPx6G4Ucud04YhieMMbcAHwV84GngZzVKrYiIiIiIyO511cAp\nIiIiIiIi8lppllQRERERERHZFgqcIiIiIiIisi0UOEVERERERGRbKHCKiIiIiIjItthqHs4dZ4yx\nWR/ltgn8TBiGzw+2VHuXMeatwO+EYfh9xphjwANABHwH+AWNNLyzjDEe8AngMJAC/gPwDKqXgTHG\nOCQjcB8HYuDnSN67HkB1MlDGmAngUeD7SeriAVQnA2OM+RbJHN4Ap4D/iOpkoIwxvwa8C/CA/0wy\ntd0DqE4Gxhjzz4D39jbTwN3AO4DfQ/UyEL1s8jGSz/kI+Fmgi14r12wYWzjfDfhhGH4P8G+A3x1w\nefYsY8z7Sb5Ip3p3fQj4QBiG9wEW8BODKtse9h7gQq8OfgT4fZLXiOplcH4ciMIwfAfw68BvozoZ\nuN6PM38I1EjqQO9fA2SMCQDCMPy+3u2fozoZKGPMO4Hv7n3feidwBL13DVwYhn+89joBvgn8IskU\nhKqXwfkhINv7nP/36HP+VRvGwPl24HMAYRg+DNw72OLsaSeBnyR5IQG8OQzDB3vrnwV+YCCl2tv+\njOSDB5LXbxvVy0CFYfiXwPt6mzcBi8A9qpOB+yDwEeBcb1uvk8G6G8gYYz5vjPmiMeZtqE4G7YeA\nJ40xnwI+A3wavXcNDWPMvcDtYRh+DNXLoNWBgjHGAgpAC9XJqzKMgXMEWN6w3e01ZcsOC8PwfwOd\nDXdZG9arJC862UFhGNbCMKwaY/Ik4fPX2fw6Vr0MQBiGXWPMAyRdnv4EvVYGyhjzXpKeAF/o3WWh\nOhm0GvDBMAx/mKTb+Z9ctF91svPGgXuAf0hSJ/8NvU6GyQeA3+qtq14G6yEgAJ4l6TnzYVQnr8ow\nBrllIL9h2w7DMBpUYWSTjfWQB5YGVZC9zBhzEPgb4JNhGP53VC9DIQzD9wKG5DqPYMMu1cnOux/4\nQWPMl4A3An9M8uV6jepk552gFzLDMHwOmAcmN+xXney8OeALYRh2wjA8ATTY/KVZdTIgxpgicDwM\nw6/07tLn/GC9H3goDEND8pnySZLrnteoTrYwjIHzIeDHAHpdbp4YbHFkg8eMMd/bW/9R4MGrHSzX\nnzFmEvgC8P4wDB/o3a16GSBjzD/pDbwBSbebLvBN1cnghGH4vWEYvrN3DdTjwD8FPqc6Gaj76Y3J\nYIzZR/IF7Quqk4H6KslYAGt1kgG+qDoZCvcBX9ywrc/5wcqy3vtykWTQVdXJqzB0o9QCf0Hyy/RD\nve37B1kYAZKRNwF+BfioMcYHngb+fHBF2rM+QPIL9G8aY9au5fwl4MOql4H5c+ABY8xXSH7x/CWS\nbjd6rQyPGL1/DdrHgT8yxqx9KbufpJVTdTIgYRj+X2PMfcaYR0gaIH4eOI3qZBgcBzbO0KD3r8H6\nIMn719+SfM7/GskI6KqTa2TFsUbwFRERERERketvGLvUioiIiIiIyC6gwCkiIiIiIiLbQoFTRERE\nREREtoUCp4iIiIiIiGwLBU4RERERERHZFgqcIiIiIiIisi0UOEVEZNcwxtxpjImMMT856LKIiIiI\nAqeIiOwu95NMwP1zgy6IiIiIgBXH8aDLICIi8roZY1zgZeDvAV8D3hqG4SljzDuBDwMd4OvAbWEY\nfp8x5hjwB0AZWAV+MQzDxwdSeBERkV1KLZwiIrJb/APgdBiGzwGfAt7XC6GfBH46DMM3Ay1g7ZfW\nPwbeH4bhPcD7gP8xgDKLiIjsagqcIiKyW9zPemj8U+C9wJuA2TAMv9O7/xOAZYzJAm8B/sgY8xjw\nJ0DWGFPa2SKLiIjsbu6gCyAiIvJ6GWMmgB8D7jHG/BJgAUXgR9n846rVWzpAPQzDN214jINhGC7u\nUJFFRET2BLVwiojIbvCPgb8Ow/BgGIY3h2F4E/DbwI8ARWPMnb3jfhqIwjBcBp4zxrwHwBjzA8CX\nd77YIiIiu5taOEVEZDd4L/BrF933EeBXgR8GPmmMiYAQaPT2vwf4L8aY9wNN4B/tTFFFRET2Do1S\nKyIiu5YxxgJ+B/itMAxXjTG/DEyHYfirAy6aiIjInqAutSIismuFYRgDC8A3eoMDvYOkq62IiIjs\nALVwioiIiIiIyLZQC6eIiIiIiIhsCwVOERERERER2RYKnCIiIiIiIrItFDhFRERERERkWyhwioiI\niIiIyLb4/52RlVLd2It7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10af00940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.FacetGrid(titanic_df,hue='Sex',aspect=4)\n",
    "fig.map(sns.kdeplot,'Age',shade=True)\n",
    "\n",
    "oldest = titanic_df['Age'].max()\n",
    "\n",
    "fig.set(xlim=(0,oldest))\n",
    "\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10aae2cf8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5KU/KS/lRTsqT8iJzwWRF5wAw8W/6wYITCgXnxLYY0A/0AFuttVlgmzFm1BhTa63tPtaJ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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b0e2898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.FacetGrid(titanic_df,hue='person',aspect=4)\n",
    "\n",
    "fig.map(sns.kdeplot,'Age',shade=True)\n",
    "\n",
    "oldest = titanic_df['Age'].max()\n",
    "\n",
    "fig.set(xlim=(0,oldest))\n",
    "\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10afe3080>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MV+fj645gPflDrOaDmBlzMVfdCA77DE0Ux1iWxRUVXu6e5cMYeGRDJ2/s6bHV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\nQiGEEEIIIYQ4wP5/3YgaepTdyXMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10afdfc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = sns.FacetGrid(titanic_df,hue='Pclass',aspect=4)\n",
    "\n",
    "fig.map(sns.kdeplot,'Age',shade=True)\n",
    "\n",
    "oldest = titanic_df['Age'].max()\n",
    "\n",
    "fig.set(xlim=(0,oldest))\n",
    "\n",
    "fig.add_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>person</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",
       "      <td>male</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",
       "      <td>female</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",
       "      <td>female</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",
       "      <td>female</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",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  person  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
       "1      0          PC 17599  71.2833   C85        C  female  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
       "3      0            113803  53.1000  C123        S  female  \n",
       "4      0            373450   8.0500   NaN        S    male  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "deck = titanic_df['Cabin'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      C85\n",
       "3     C123\n",
       "6      E46\n",
       "10      G6\n",
       "11    C103\n",
       "Name: Cabin, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10a16bb38>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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znQ308j7gKcDjaP3b+Q4VvTdzIpyAVwAfA14G3NRQD+PAv2XmrwNExHOBdwKX1N1IRCyi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\nqUj/D5MPtoz6B2NiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ae074e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels = []\n",
    "\n",
    "for level in deck:\n",
    "    levels.append(level[0])\n",
    "    \n",
    "cabin_df = DataFrame(levels)\n",
    "cabin_df.columns = ['Cabin']\n",
    "sns.factorplot('Cabin',data=cabin_df,palette='winter_d',kind='count',\n",
    "               order=['A','B','C','D','E','F','T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10b5b0c50>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a131748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cabin_df = cabin_df[cabin_df.Cabin != 'T']\n",
    "sns.factorplot('Cabin',data=cabin_df,palette='summer',kind='count',\n",
    "               order=['A','B','C','D','E','F','T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>person</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",
       "      <td>male</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",
       "      <td>female</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",
       "      <td>female</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",
       "      <td>female</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",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  person  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
       "1      0          PC 17599  71.2833   C85        C  female  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
       "3      0            113803  53.1000  C123        S  female  \n",
       "4      0            373450   8.0500   NaN        S    male  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jspang/anaconda/lib/python3.4/site-packages/seaborn/categorical.py:2653: UserWarning: The `x_order` parameter has been renamed `order`\n",
      "  UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10bbc75c0>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bbc7588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Embarked',data=titanic_df,hue='Pclass',\n",
    "               x_order=['C','Q','S'],kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>person</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",
       "      <td>male</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",
       "      <td>female</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",
       "      <td>female</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",
       "      <td>female</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",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  person  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  \n",
       "1      0          PC 17599  71.2833   C85        C  female  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female  \n",
       "3      0            113803  53.1000  C123        S  female  \n",
       "4      0            373450   8.0500   NaN        S    male  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Who was alone and who was with family?\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "titanic_df['Alone'] = titanic_df.SibSp + titanic_df.Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       1\n",
       "2       0\n",
       "3       1\n",
       "4       0\n",
       "5       0\n",
       "6       0\n",
       "7       4\n",
       "8       2\n",
       "9       1\n",
       "10      2\n",
       "11      0\n",
       "12      0\n",
       "13      6\n",
       "14      0\n",
       "15      0\n",
       "16      5\n",
       "17      0\n",
       "18      1\n",
       "19      0\n",
       "20      0\n",
       "21      0\n",
       "22      0\n",
       "23      0\n",
       "24      4\n",
       "25      6\n",
       "26      0\n",
       "27      5\n",
       "28      0\n",
       "29      0\n",
       "       ..\n",
       "861     1\n",
       "862     0\n",
       "863    10\n",
       "864     0\n",
       "865     0\n",
       "866     1\n",
       "867     0\n",
       "868     0\n",
       "869     2\n",
       "870     0\n",
       "871     2\n",
       "872     0\n",
       "873     0\n",
       "874     1\n",
       "875     0\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     5\n",
       "886     0\n",
       "887     0\n",
       "888     3\n",
       "889     0\n",
       "890     0\n",
       "Name: Alone, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Alone']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jspang/anaconda/lib/python3.4/site-packages/pandas/core/indexing.py:115: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n"
     ]
    }
   ],
   "source": [
    "titanic_df['Alone'].loc[titanic_df['Alone'] > 0] = 'With Family'\n",
    "\n",
    "titanic_df['Alone'].loc[titanic_df['Alone'] == 0] = 'Alone'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url_info = 'http://stackoverflow.com/questions/20625582/how-to-deal-with-this-pandas-warning'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>person</th>\n",
       "      <th>Alone</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",
       "      <td>male</td>\n",
       "      <td>With Family</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",
       "      <td>female</td>\n",
       "      <td>With Family</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",
       "      <td>female</td>\n",
       "      <td>Alone</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",
       "      <td>female</td>\n",
       "      <td>With Family</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",
       "      <td>male</td>\n",
       "      <td>Alone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  person        Alone  \n",
       "0      0         A/5 21171   7.2500   NaN        S    male  With Family  \n",
       "1      0          PC 17599  71.2833   C85        C  female  With Family  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female        Alone  \n",
       "3      0            113803  53.1000  C123        S  female  With Family  \n",
       "4      0            373450   8.0500   NaN        S    male        Alone  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10bbbfd68>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J4bQV6Gt7PmIw9ff39RxKx/39cznjjLkPpzYVrr9/Lrd5jI8oJU3r1gPPBYiIM4Dbu1uO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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b8e71d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Alone',data=titanic_df,palette='Blues',kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic_df['Survivor'] = titanic_df.Survived.map({0:'no',1:'yes'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "      <th>person</th>\n",
       "      <th>Alone</th>\n",
       "      <th>Survivor</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",
       "      <td>male</td>\n",
       "      <td>With Family</td>\n",
       "      <td>no</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",
       "      <td>female</td>\n",
       "      <td>With Family</td>\n",
       "      <td>yes</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",
       "      <td>female</td>\n",
       "      <td>Alone</td>\n",
       "      <td>yes</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",
       "      <td>female</td>\n",
       "      <td>With Family</td>\n",
       "      <td>yes</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",
       "      <td>male</td>\n",
       "      <td>Alone</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  person        Alone  \\\n",
       "0      0         A/5 21171   7.2500   NaN        S    male  With Family   \n",
       "1      0          PC 17599  71.2833   C85        C  female  With Family   \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  female        Alone   \n",
       "3      0            113803  53.1000  C123        S  female  With Family   \n",
       "4      0            373450   8.0500   NaN        S    male        Alone   \n",
       "\n",
       "  Survivor  \n",
       "0       no  \n",
       "1      yes  \n",
       "2      yes  \n",
       "3      yes  \n",
       "4       no  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10a2f2be0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a2f2320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Survivor',data=titanic_df,palette='Set1',kind='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10bcb5fd0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E6oUekBSRuFDIiIivFDIi4iuFjIj4SiEjIr5SyIiIrxQyIuIrhYyI+EohIyK+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\nVwoZEfGVQkZEfKWQERFfKWRExFf/DxO1PZRhK+F4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ae08160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Pclass','Survived',data=titanic_df,order=[1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10b5878d0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J3i/IOrjO5xq7TuwjkEZQK9YmPlwgk/+fA7KB+4EvgWFa69taOjAh2prkMeNI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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b1374a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Pclass','Survived',hue='person',data=titanic_df,order=[1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10b418438>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DkbGhQm/djMkyw6Xcrug/l3QohGVXcxyHYiFLMVFIPghCak2Plhd0huL1q7UM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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d0a7978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Age','Survived',data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10d2a10b8>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dxc6sPUgJ9MIFZEvBiexljiQneSE7jah7/Hb/IIejY9wb2tyRvSsc3L0whg0f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\n4pkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d29b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Age','Survived',hue='Pclass',\n",
    "           data=titanic_df,palette='winter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10adf5d30>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R1nP5SMs5PFcYY0+6l8dzJ3GQZFyb76ZP8N30Cd4aSHBXvJtbop2EtPn9yPUZ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\nIYQQoiIJCiGEEBX9f2e9IjvGqp4hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bdc0e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "generations = [10,20,40,60,80]\n",
    "\n",
    "sns.lmplot('Age','Survived',hue='Pclass',data=titanic_df,\n",
    "           palette='winter',x_bins=generations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10d3bf240>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l1F8AzyLsDP3XwI8Jizs/DbgaeIbW+neaccILWXCwH3is1vpUo08mCeVotVp/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d3bf828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('Age','Survived',hue='Sex',data=titanic_df,\n",
    "           palette='winter',x_bins=generations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10d3aaa90>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EJFIKGRGJlEJGRCKlkBGRSClkRCRSChkRiZRCRkQipZARkUgpZEQkUgoZEYmU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\nISMikVLIiEik/j9xe1h56KTPEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dd70438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Alone','Survived',data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x10e23d320>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e61c3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot('Alone','Survived',hue='person',data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not interperet input 'deck'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-74-c651f8639729>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfactorplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Alone'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Survived'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'deck'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtitanic_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/jspang/anaconda/lib/python3.4/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mfactorplot\u001b[0;34m(x, y, hue, data, row, col, col_wrap, estimator, ci, n_boot, units, order, hue_order, row_order, col_order, kind, size, aspect, orient, color, palette, legend, legend_out, sharex, sharey, margin_titles, facet_kws, **kwargs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m     \u001b[0;31m# facets to ensure representation of all data in the final plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m     \u001b[0mp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_CategoricalPlotter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m     \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m     \u001b[0morder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroup_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m     \u001b[0mhue_order\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhue_names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/jspang/anaconda/lib/python3.4/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[0;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[1;32m    143\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m                     \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Could not interperet input '{}'\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m             \u001b[0;31m# Figure out the plotting orientation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Could not interperet input 'deck'"
     ]
    }
   ],
   "source": [
    "sns.factorplot('Alone','Survived',hue='deck',data=titanic_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": []
  }
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