Data Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>\n",
    "           Data Exploration\n",
    "</h1>\n",
    "\n",
    "<p>\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PassengerId  Survived  Pclass  \\\n",
      "0              1         0       3   \n",
      "1              2         1       1   \n",
      "2              3         1       3   \n",
      "3              4         1       1   \n",
      "4              5         0       3   \n",
      "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",
      "10            11         1       3   \n",
      "11            12         1       1   \n",
      "12            13         0       3   \n",
      "13            14         0       3   \n",
      "14            15         0       3   \n",
      "15            16         1       2   \n",
      "16            17         0       3   \n",
      "17            18         1       2   \n",
      "18            19         0       3   \n",
      "19            20         1       3   \n",
      "20            21         0       2   \n",
      "21            22         1       2   \n",
      "22            23         1       3   \n",
      "23            24         1       1   \n",
      "24            25         0       3   \n",
      "25            26         1       3   \n",
      "26            27         0       3   \n",
      "27            28         0       1   \n",
      "28            29         1       3   \n",
      "29            30         0       3   \n",
      "..           ...       ...     ...   \n",
      "861          862         0       2   \n",
      "862          863         1       1   \n",
      "863          864         0       3   \n",
      "864          865         0       2   \n",
      "865          866         1       2   \n",
      "866          867         1       2   \n",
      "867          868         0       1   \n",
      "868          869         0       3   \n",
      "869          870         1       3   \n",
      "870          871         0       3   \n",
      "871          872         1       1   \n",
      "872          873         0       1   \n",
      "873          874         0       3   \n",
      "874          875         1       2   \n",
      "875          876         1       3   \n",
      "876          877         0       3   \n",
      "877          878         0       3   \n",
      "878          879         0       3   \n",
      "879          880         1       1   \n",
      "880          881         1       2   \n",
      "881          882         0       3   \n",
      "882          883         0       3   \n",
      "883          884         0       2   \n",
      "884          885         0       3   \n",
      "885          886         0       3   \n",
      "886          887         0       2   \n",
      "887          888         1       1   \n",
      "888          889         0       3   \n",
      "889          890         1       1   \n",
      "890          891         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",
      "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",
      "10                     Sandstrom, Miss. Marguerite Rut  female    4      1   \n",
      "11                            Bonnell, Miss. Elizabeth  female   58      0   \n",
      "12                      Saundercock, Mr. William Henry    male   20      0   \n",
      "13                         Andersson, Mr. Anders Johan    male   39      1   \n",
      "14                Vestrom, Miss. Hulda Amanda Adolfina  female   14      0   \n",
      "15                    Hewlett, Mrs. (Mary D Kingcome)   female   55      0   \n",
      "16                                Rice, Master. Eugene    male    2      4   \n",
      "17                        Williams, Mr. Charles Eugene    male  NaN      0   \n",
      "18   Vander Planke, Mrs. Julius (Emelia Maria Vande...  female   31      1   \n",
      "19                             Masselmani, Mrs. Fatima  female  NaN      0   \n",
      "20                                Fynney, Mr. Joseph J    male   35      0   \n",
      "21                               Beesley, Mr. Lawrence    male   34      0   \n",
      "22                         McGowan, Miss. Anna \"Annie\"  female   15      0   \n",
      "23                        Sloper, Mr. William Thompson    male   28      0   \n",
      "24                       Palsson, Miss. Torborg Danira  female    8      3   \n",
      "25   Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female   38      1   \n",
      "26                             Emir, Mr. Farred Chehab    male  NaN      0   \n",
      "27                      Fortune, Mr. Charles Alexander    male   19      3   \n",
      "28                       O'Dwyer, Miss. Ellen \"Nellie\"  female  NaN      0   \n",
      "29                                 Todoroff, Mr. Lalio    male  NaN      0   \n",
      "..                                                 ...     ...  ...    ...   \n",
      "861                        Giles, Mr. Frederick Edward    male   21      1   \n",
      "862  Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female   48      0   \n",
      "863                  Sage, Miss. Dorothy Edith \"Dolly\"  female  NaN      8   \n",
      "864                             Gill, Mr. John William    male   24      0   \n",
      "865                           Bystrom, Mrs. (Karolina)  female   42      0   \n",
      "866                       Duran y More, Miss. Asuncion  female   27      1   \n",
      "867               Roebling, Mr. Washington Augustus II    male   31      0   \n",
      "868                        van Melkebeke, Mr. Philemon    male  NaN      0   \n",
      "869                    Johnson, Master. Harold Theodor    male    4      1   \n",
      "870                                  Balkic, Mr. Cerin    male   26      0   \n",
      "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female   47      1   \n",
      "872                           Carlsson, Mr. Frans Olof    male   33      0   \n",
      "873                        Vander Cruyssen, Mr. Victor    male   47      0   \n",
      "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female   28      1   \n",
      "875                   Najib, Miss. Adele Kiamie \"Jane\"  female   15      0   \n",
      "876                      Gustafsson, Mr. Alfred Ossian    male   20      0   \n",
      "877                               Petroff, Mr. Nedelio    male   19      0   \n",
      "878                                 Laleff, Mr. Kristo    male  NaN      0   \n",
      "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female   56      0   \n",
      "880       Shelley, Mrs. William (Imanita Parrish Hall)  female   25      0   \n",
      "881                                 Markun, Mr. Johann    male   33      0   \n",
      "882                       Dahlberg, Miss. Gerda Ulrika  female   22      0   \n",
      "883                      Banfield, Mr. Frederick James    male   28      0   \n",
      "884                             Sutehall, Mr. Henry Jr    male   25      0   \n",
      "885               Rice, Mrs. William (Margaret Norton)  female   39      0   \n",
      "886                              Montvila, Rev. Juozas    male   27      0   \n",
      "887                       Graham, Miss. Margaret Edith  female   19      0   \n",
      "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  NaN      1   \n",
      "889                              Behr, Mr. Karl Howell    male   26      0   \n",
      "890                                Dooley, Mr. Patrick    male   32      0   \n",
      "\n",
      "     Parch            Ticket      Fare        Cabin Embarked  \n",
      "0        0         A/5 21171    7.2500          NaN        S  \n",
      "1        0          PC 17599   71.2833          C85        C  \n",
      "2        0  STON/O2. 3101282    7.9250          NaN        S  \n",
      "3        0            113803   53.1000         C123        S  \n",
      "4        0            373450    8.0500          NaN        S  \n",
      "5        0            330877    8.4583          NaN        Q  \n",
      "6        0             17463   51.8625          E46        S  \n",
      "7        1            349909   21.0750          NaN        S  \n",
      "8        2            347742   11.1333          NaN        S  \n",
      "9        0            237736   30.0708          NaN        C  \n",
      "10       1           PP 9549   16.7000           G6        S  \n",
      "11       0            113783   26.5500         C103        S  \n",
      "12       0         A/5. 2151    8.0500          NaN        S  \n",
      "13       5            347082   31.2750          NaN        S  \n",
      "14       0            350406    7.8542          NaN        S  \n",
      "15       0            248706   16.0000          NaN        S  \n",
      "16       1            382652   29.1250          NaN        Q  \n",
      "17       0            244373   13.0000          NaN        S  \n",
      "18       0            345763   18.0000          NaN        S  \n",
      "19       0              2649    7.2250          NaN        C  \n",
      "20       0            239865   26.0000          NaN        S  \n",
      "21       0            248698   13.0000          D56        S  \n",
      "22       0            330923    8.0292          NaN        Q  \n",
      "23       0            113788   35.5000           A6        S  \n",
      "24       1            349909   21.0750          NaN        S  \n",
      "25       5            347077   31.3875          NaN        S  \n",
      "26       0              2631    7.2250          NaN        C  \n",
      "27       2             19950  263.0000  C23 C25 C27        S  \n",
      "28       0            330959    7.8792          NaN        Q  \n",
      "29       0            349216    7.8958          NaN        S  \n",
      "..     ...               ...       ...          ...      ...  \n",
      "861      0             28134   11.5000          NaN        S  \n",
      "862      0             17466   25.9292          D17        S  \n",
      "863      2          CA. 2343   69.5500          NaN        S  \n",
      "864      0            233866   13.0000          NaN        S  \n",
      "865      0            236852   13.0000          NaN        S  \n",
      "866      0     SC/PARIS 2149   13.8583          NaN        C  \n",
      "867      0          PC 17590   50.4958          A24        S  \n",
      "868      0            345777    9.5000          NaN        S  \n",
      "869      1            347742   11.1333          NaN        S  \n",
      "870      0            349248    7.8958          NaN        S  \n",
      "871      1             11751   52.5542          D35        S  \n",
      "872      0               695    5.0000  B51 B53 B55        S  \n",
      "873      0            345765    9.0000          NaN        S  \n",
      "874      0         P/PP 3381   24.0000          NaN        C  \n",
      "875      0              2667    7.2250          NaN        C  \n",
      "876      0              7534    9.8458          NaN        S  \n",
      "877      0            349212    7.8958          NaN        S  \n",
      "878      0            349217    7.8958          NaN        S  \n",
      "879      1             11767   83.1583          C50        C  \n",
      "880      1            230433   26.0000          NaN        S  \n",
      "881      0            349257    7.8958          NaN        S  \n",
      "882      0              7552   10.5167          NaN        S  \n",
      "883      0  C.A./SOTON 34068   10.5000          NaN        S  \n",
      "884      0   SOTON/OQ 392076    7.0500          NaN        S  \n",
      "885      5            382652   29.1250          NaN        Q  \n",
      "886      0            211536   13.0000          NaN        S  \n",
      "887      0            112053   30.0000          B42        S  \n",
      "888      2        W./C. 6607   23.4500          NaN        S  \n",
      "889      0            111369   30.0000         C148        C  \n",
      "890      0            370376    7.7500          NaN        Q  \n",
      "\n",
      "[891 rows x 12 columns]\n",
      "Index([u'PassengerId', u'Survived', u'Pclass', u'Name', u'Sex', u'Age',\n",
      "       u'SibSp', u'Parch', u'Ticket', u'Fare', u'Cabin', u'Embarked'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import csv \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import thinkstats2\n",
    "import thinkplot\n",
    "%matplotlib inline\n",
    "#import all necessary libraries\n",
    "\n",
    "train = pd.read_csv('train.csv')\n",
    "print train \n",
    "\n",
    "print train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    From the last output we see the different categories we could compare and choose to compare for our model.\n",
    "    Next we will define a method that will compare the percentages of two groups that automatically creates a \n",
    "    automated statement and a histogram of each group\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "male = train[train['Sex'] == 'male']\n",
    "female = train[train['Sex'] == 'female']\n",
    "class_1 = train[train['Pclass'] == 1]\n",
    "class_2 = train[train['Pclass'] == 2]\n",
    "class_3 = train[train['Pclass'] == 3]\n",
    "\n",
    "\n",
    "def calc_survival_percent(group1, group2,name1,name2):\n",
    "    import thinkplot\n",
    "    #creates a histogram of each group and comparative statement comparing two groups\n",
    "    #group 1 represents the first input and group 2 represents the second input\n",
    "    tot_1 = len(group1[group1['Survived'] == 1].Survived) / float(len(group1.Survived))\n",
    "    tot_2 = len(group2[group2['Survived'] == 1].Survived) / float(len(group2.Survived))\n",
    "    \n",
    "    hist = thinkstats2.Hist(group1.Survived)\n",
    "    thinkplot.Hist(hist,label = name1 + \"Survival Rate\")\n",
    "    thinkplot.Show()\n",
    "    hist2 = thinkstats2.Hist(group2.Survived,label = name2 + \"Survival Rate\")\n",
    "    thinkplot.Hist(hist2,label = name2 + \"Survival Rate\")\n",
    "    thinkplot.Show()\n",
    "    \n",
    "    if tot_1 > tot_2:\n",
    "        print name1 + ': ' + str(tot_1) + ' has a higher survival rate(%) than ' + name2 + ': ' + str(tot_2)\n",
    "    elif tot_2 > tot_1:\n",
    "        print name2 + ': ' + str(tot_2) + ' has a higher survival rate(%) than ' + name1 + ': ' + str(tot_1)\n",
    "    else:\n",
    "        print name1 + ': ' + str(tot_1) + ' has the same survival rate(%) as ' + name2 + ': ' + str(tot_2)\n",
    "\n",
    "        \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "Next we will compare a few categories against each other for survival rate\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male vs Females\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f656ea849d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65456a6350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "female: 0.742038216561 has a higher survival rate(%) than male: 0.188908145581\n",
      "1st class vs 2nd class\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65600c3250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65687ef890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1st Class: 0.62962962963 has a higher survival rate(%) than 2nd Class: 0.472826086957\n",
      "1st class vs 3rd class\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6568797f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WimoebjMZ+CFAZv4SGBARQ/ZvmWpGtQ8mavNdqep6mbkU2LqPLn7uClbF+wdt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\n3P8HidUqXpZw0U0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65584ac150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1st Class: 0.62962962963 has a higher survival rate(%) than 3rd Class: 0.242362525458\n",
      "2nd class vs 3rd class\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f655853a910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WimoebjMZ+CFAZv4SGBARQ/ZvmWpGtQ8mavNdqep6mbkU2LqPLn7uClbF+wdt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\n3P8HidUqXpZw0U0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65454b6310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2nd Class: 0.472826086957 has a higher survival rate(%) than 3rd Class: 0.242362525458\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65454ab790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print 'Male vs Females'\n",
    "calc_survival_percent(male,female,\"male\",\"female\")\n",
    "print '1st class vs 2nd class'\n",
    "calc_survival_percent(class_1,class_2,\"1st Class\",\"2nd Class\")\n",
    "print '1st class vs 3rd class'\n",
    "calc_survival_percent(class_1,class_3,\"1st Class\",\"3rd Class\")\n",
    "print '2nd class vs 3rd class'\n",
    "calc_survival_percent(class_2,class_3,\"2nd Class\",\"3rd Class\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    From the previous comparisons females are favored vs males. In order of favorability: 1st class > 2nd class > 3rd class\n",
    "    To investigate relationships of conjunction of features we will explore a few cases:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adult Males vs Young Males\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6545886b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f655849ead0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "young male: 0.338028169014 has a higher survival rate(%) than  old male: 0.180628272251\n",
      "Males w/families vs Females w/families\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6545a4e290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AKaUVQN8mlkvA4xHxTERcvMdGp8Y05+I22y+zvJFltOc198JEnygcRn0kIj6y\nZ4amNuD7bu/Xovdem54NHhGPAw1/ugvq4tvY8fimzmw7IaX0p4g4gLpov1z4KUVS23oWOCSltKFw\nWPV+4LB2HpPUEbT4vdemsU4pndzUvMKH7f1SSisj4kBgVRPr+FPhv29HxH3UHc4z1u1jOXBIg/sD\nCtO2X2bgLpbRnrfL1y6ltL7B7Ucj4taI6JNSWruHxqjW8323F2vNe29PHgZ/EPhi4fYXgAe2XyAi\nukdEj8LtDwKnAC/sqQFqB88AH46IsojoBoyj7nVs6EHgAqi/ct0773/coXa1y9eu4WecETGcuq9y\nGup8BE1/run7Ln9Nvn6tee/tlouiNOFG4O6I+BKwFDgXICL6A9NSSqdRdwj9vsKlSLsAM1JKs/fg\nGNVAUxe3iYhL6manH6eUqiKiIiKWAO8CF7bnmFWnOa8d8L8j4ivAJuAvwOfab8RqKCJmAuXAfhGx\nDPgm0A3fd3uFXb1+tOK950VRJEnKnF+RkiQpc8ZakqTMGWtJkjJnrCVJypyxliQpc8ZakqTMGWtJ\nkjJnrCVJytz/By5qHO4xCZ2oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6545892e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Males w/families: 0.251748251748 has the same survival rate(%) as Females w/families: 0.251748251748\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6545845050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "young_male = male[male['Age'] <= 18]\n",
    "old_male = male[male['Age'] > 18]\n",
    "male_fam = male[male['SibSp'] > 0]\n",
    "female_fam = male[male['SibSp'] > 0]\n",
    "\n",
    "print 'Adult Males vs Young Males'\n",
    "calc_survival_percent(young_male, old_male,\"young male\",\" old male\")\n",
    "print 'Males w/families vs Females w/families'\n",
    "calc_survival_percent(male_fam,female_fam,\"Males w/families\",\"Females w/families\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "If we were to interpret the results from our two comparisons Age seems to be a significant factor for survivability. The family result is to be expected b/c families is defined by at least 1 immediate family on board and in that time frame there should be an equal amount of males and females (from being spouses). \n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AfmOMGQIAEXkRwP0Ajq9GYH7atk2T+tiYtqPfcot97XJEYXTlinaMOvNK6uqA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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6545851710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6545667050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivors = train.Survived\n",
    "age = train.Age\n",
    "thinkplot.Scatter(survivors,age,alpha=0.05)\n",
    "thinkplot.Show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    From the visualization we see that the distribution of survivors is slightly younger and smaller for survivors.\n",
    "</p>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}