smowden/DA Project.ipynb

## DA Project.ipynb

      
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## gistfile1.txt
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Titanic Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "In this notebook we analyse titanic data. Specifically we look at whether the saying _\"woman and children\"_ first rings true.\n",
    "Furthermore we will explore if there was a difference in survival accross passengers of diffent socioeconomic status.\n",
    "\n",
    "### Preperation\n",
    "The first step should of course be to read the data from the CSV into a DataFrame, also we can also seperate all survivors from the passengers into a new DataFrame as that will make further comparisons easier. Also lets print the DataFrame to take a brief look at the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Ticket</th>\n",
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       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
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       "      <td>Andersson, Mr. Anders Johan</td>\n",
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       "      <td>7.8542</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248706</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Rice, Master. Eugene</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
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       "      <td>Williams, Mr. Charles Eugene</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
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       "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
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       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>345763</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>20</th>\n",
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       "      <td>Masselmani, Mrs. Fatima</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2649</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
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       "      <td>Fynney, Mr. Joseph J</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239865</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
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       "      <td>Beesley, Mr. Lawrence</td>\n",
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       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248698</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>D56</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McGowan, Miss. Anna \"Annie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330923</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
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       "      <td>Palsson, Miss. Torborg Danira</td>\n",
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       "      <td>8.0</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",
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       "      <td>Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...</td>\n",
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       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>347077</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Emir, Mr. Farred Chehab</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>28</th>\n",
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       "      <td>Fortune, Mr. Charles Alexander</td>\n",
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       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>O'Dwyer, Miss. Ellen \"Nellie\"</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330959</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>30</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Todoroff, Mr. Lalio</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349216</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>862</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Giles, Mr. Frederick Edward</td>\n",
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       "      <td>21.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28134</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>863</th>\n",
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       "      <td>Swift, Mrs. Frederick Joel (Margaret Welles Ba...</td>\n",
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       "      <td>48.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17466</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>D17</td>\n",
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       "      <th>864</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sage, Miss. Dorothy Edith \"Dolly\"</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Gill, Mr. John William</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>233866</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Bystrom, Mrs. (Karolina)</td>\n",
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       "      <td>42.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>236852</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Duran y More, Miss. Asuncion</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2149</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Roebling, Mr. Washington Augustus II</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17590</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>A24</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>van Melkebeke, Mr. Philemon</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345777</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Balkic, Mr. Cerin</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349248</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11751</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>D35</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Carlsson, Mr. Frans Olof</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>695</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vander Cruyssen, Mr. Victor</td>\n",
       "      <td>male</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345765</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>P/PP 3381</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gustafsson, Mr. Alfred Ossian</td>\n",
       "      <td>male</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7534</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Petroff, Mr. Nedelio</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349212</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Laleff, Mr. Kristo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349217</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
       "      <td>female</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\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>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "6                   0       3   \n",
       "7                   0       1   \n",
       "8                   0       3   \n",
       "9                   1       3   \n",
       "10                  1       2   \n",
       "11                  1       3   \n",
       "12                  1       1   \n",
       "13                  0       3   \n",
       "14                  0       3   \n",
       "15                  0       3   \n",
       "16                  1       2   \n",
       "17                  0       3   \n",
       "18                  1       2   \n",
       "19                  0       3   \n",
       "20                  1       3   \n",
       "21                  0       2   \n",
       "22                  1       2   \n",
       "23                  1       3   \n",
       "24                  1       1   \n",
       "25                  0       3   \n",
       "26                  1       3   \n",
       "27                  0       3   \n",
       "28                  0       1   \n",
       "29                  1       3   \n",
       "30                  0       3   \n",
       "...               ...     ...   \n",
       "862                 0       2   \n",
       "863                 1       1   \n",
       "864                 0       3   \n",
       "865                 0       2   \n",
       "866                 1       2   \n",
       "867                 1       2   \n",
       "868                 0       1   \n",
       "869                 0       3   \n",
       "870                 1       3   \n",
       "871                 0       3   \n",
       "872                 1       1   \n",
       "873                 0       1   \n",
       "874                 0       3   \n",
       "875                 1       2   \n",
       "876                 1       3   \n",
       "877                 0       3   \n",
       "878                 0       3   \n",
       "879                 0       3   \n",
       "880                 1       1   \n",
       "881                 1       2   \n",
       "882                 0       3   \n",
       "883                 0       3   \n",
       "884                 0       2   \n",
       "885                 0       3   \n",
       "886                 0       3   \n",
       "887                 0       2   \n",
       "888                 1       1   \n",
       "889                 0       3   \n",
       "890                 1       1   \n",
       "891                 0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "6                                             Moran, Mr. James    male   NaN   \n",
       "7                                      McCarthy, Mr. Timothy J    male  54.0   \n",
       "8                               Palsson, Master. Gosta Leonard    male   2.0   \n",
       "9            Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0   \n",
       "10                         Nasser, Mrs. Nicholas (Adele Achem)  female  14.0   \n",
       "11                             Sandstrom, Miss. Marguerite Rut  female   4.0   \n",
       "12                                    Bonnell, Miss. Elizabeth  female  58.0   \n",
       "13                              Saundercock, Mr. William Henry    male  20.0   \n",
       "14                                 Andersson, Mr. Anders Johan    male  39.0   \n",
       "15                        Vestrom, Miss. Hulda Amanda Adolfina  female  14.0   \n",
       "16                            Hewlett, Mrs. (Mary D Kingcome)   female  55.0   \n",
       "17                                        Rice, Master. Eugene    male   2.0   \n",
       "18                                Williams, Mr. Charles Eugene    male   NaN   \n",
       "19           Vander Planke, Mrs. Julius (Emelia Maria Vande...  female  31.0   \n",
       "20                                     Masselmani, Mrs. Fatima  female   NaN   \n",
       "21                                        Fynney, Mr. Joseph J    male  35.0   \n",
       "22                                       Beesley, Mr. Lawrence    male  34.0   \n",
       "23                                 McGowan, Miss. Anna \"Annie\"  female  15.0   \n",
       "24                                Sloper, Mr. William Thompson    male  28.0   \n",
       "25                               Palsson, Miss. Torborg Danira  female   8.0   \n",
       "26           Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...  female  38.0   \n",
       "27                                     Emir, Mr. Farred Chehab    male   NaN   \n",
       "28                              Fortune, Mr. Charles Alexander    male  19.0   \n",
       "29                               O'Dwyer, Miss. Ellen \"Nellie\"  female   NaN   \n",
       "30                                         Todoroff, Mr. Lalio    male   NaN   \n",
       "...                                                        ...     ...   ...   \n",
       "862                                Giles, Mr. Frederick Edward    male  21.0   \n",
       "863          Swift, Mrs. Frederick Joel (Margaret Welles Ba...  female  48.0   \n",
       "864                          Sage, Miss. Dorothy Edith \"Dolly\"  female   NaN   \n",
       "865                                     Gill, Mr. John William    male  24.0   \n",
       "866                                   Bystrom, Mrs. (Karolina)  female  42.0   \n",
       "867                               Duran y More, Miss. Asuncion  female  27.0   \n",
       "868                       Roebling, Mr. Washington Augustus II    male  31.0   \n",
       "869                                van Melkebeke, Mr. Philemon    male   NaN   \n",
       "870                            Johnson, Master. Harold Theodor    male   4.0   \n",
       "871                                          Balkic, Mr. Cerin    male  26.0   \n",
       "872           Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  47.0   \n",
       "873                                   Carlsson, Mr. Frans Olof    male  33.0   \n",
       "874                                Vander Cruyssen, Mr. Victor    male  47.0   \n",
       "875                      Abelson, Mrs. Samuel (Hannah Wizosky)  female  28.0   \n",
       "876                           Najib, Miss. Adele Kiamie \"Jane\"  female  15.0   \n",
       "877                              Gustafsson, Mr. Alfred Ossian    male  20.0   \n",
       "878                                       Petroff, Mr. Nedelio    male  19.0   \n",
       "879                                         Laleff, Mr. Kristo    male   NaN   \n",
       "880              Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0   \n",
       "881               Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0   \n",
       "882                                         Markun, Mr. Johann    male  33.0   \n",
       "883                               Dahlberg, Miss. Gerda Ulrika  female  22.0   \n",
       "884                              Banfield, Mr. Frederick James    male  28.0   \n",
       "885                                     Sutehall, Mr. Henry Jr    male  25.0   \n",
       "886                       Rice, Mrs. William (Margaret Norton)  female  39.0   \n",
       "887                                      Montvila, Rev. Juozas    male  27.0   \n",
       "888                               Graham, Miss. Margaret Edith  female  19.0   \n",
       "889                   Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN   \n",
       "890                                      Behr, Mr. Karl Howell    male  26.0   \n",
       "891                                        Dooley, Mr. Patrick    male  32.0   \n",
       "\n",
       "             SibSp  Parch            Ticket      Fare        Cabin Embarked  \n",
       "PassengerId                                                                  \n",
       "1                1      0         A/5 21171    7.2500          NaN        S  \n",
       "2                1      0          PC 17599   71.2833          C85        C  \n",
       "3                0      0  STON/O2. 3101282    7.9250          NaN        S  \n",
       "4                1      0            113803   53.1000         C123        S  \n",
       "5                0      0            373450    8.0500          NaN        S  \n",
       "6                0      0            330877    8.4583          NaN        Q  \n",
       "7                0      0             17463   51.8625          E46        S  \n",
       "8                3      1            349909   21.0750          NaN        S  \n",
       "9                0      2            347742   11.1333          NaN        S  \n",
       "10               1      0            237736   30.0708          NaN        C  \n",
       "11               1      1           PP 9549   16.7000           G6        S  \n",
       "12               0      0            113783   26.5500         C103        S  \n",
       "13               0      0         A/5. 2151    8.0500          NaN        S  \n",
       "14               1      5            347082   31.2750          NaN        S  \n",
       "15               0      0            350406    7.8542          NaN        S  \n",
       "16               0      0            248706   16.0000          NaN        S  \n",
       "17               4      1            382652   29.1250          NaN        Q  \n",
       "18               0      0            244373   13.0000          NaN        S  \n",
       "19               1      0            345763   18.0000          NaN        S  \n",
       "20               0      0              2649    7.2250          NaN        C  \n",
       "21               0      0            239865   26.0000          NaN        S  \n",
       "22               0      0            248698   13.0000          D56        S  \n",
       "23               0      0            330923    8.0292          NaN        Q  \n",
       "24               0      0            113788   35.5000           A6        S  \n",
       "25               3      1            349909   21.0750          NaN        S  \n",
       "26               1      5            347077   31.3875          NaN        S  \n",
       "27               0      0              2631    7.2250          NaN        C  \n",
       "28               3      2             19950  263.0000  C23 C25 C27        S  \n",
       "29               0      0            330959    7.8792          NaN        Q  \n",
       "30               0      0            349216    7.8958          NaN        S  \n",
       "...            ...    ...               ...       ...          ...      ...  \n",
       "862              1      0             28134   11.5000          NaN        S  \n",
       "863              0      0             17466   25.9292          D17        S  \n",
       "864              8      2          CA. 2343   69.5500          NaN        S  \n",
       "865              0      0            233866   13.0000          NaN        S  \n",
       "866              0      0            236852   13.0000          NaN        S  \n",
       "867              1      0     SC/PARIS 2149   13.8583          NaN        C  \n",
       "868              0      0          PC 17590   50.4958          A24        S  \n",
       "869              0      0            345777    9.5000          NaN        S  \n",
       "870              1      1            347742   11.1333          NaN        S  \n",
       "871              0      0            349248    7.8958          NaN        S  \n",
       "872              1      1             11751   52.5542          D35        S  \n",
       "873              0      0               695    5.0000  B51 B53 B55        S  \n",
       "874              0      0            345765    9.0000          NaN        S  \n",
       "875              1      0         P/PP 3381   24.0000          NaN        C  \n",
       "876              0      0              2667    7.2250          NaN        C  \n",
       "877              0      0              7534    9.8458          NaN        S  \n",
       "878              0      0            349212    7.8958          NaN        S  \n",
       "879              0      0            349217    7.8958          NaN        S  \n",
       "880              0      1             11767   83.1583          C50        C  \n",
       "881              0      1            230433   26.0000          NaN        S  \n",
       "882              0      0            349257    7.8958          NaN        S  \n",
       "883              0      0              7552   10.5167          NaN        S  \n",
       "884              0      0  C.A./SOTON 34068   10.5000          NaN        S  \n",
       "885              0      0   SOTON/OQ 392076    7.0500          NaN        S  \n",
       "886              0      5            382652   29.1250          NaN        Q  \n",
       "887              0      0            211536   13.0000          NaN        S  \n",
       "888              0      0            112053   30.0000          B42        S  \n",
       "889              1      2        W./C. 6607   23.4500          NaN        S  \n",
       "890              0      0            111369   30.0000         C148        C  \n",
       "891              0      0            370376    7.7500          NaN        Q  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from pandas import DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "titanic_data = DataFrame.from_csv('titanic-data.csv')\n",
    "survivors = titanic_data[titanic_data['Survived'] == 1]\n",
    "#titanic_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the data, we see that some passengers don't have an age specified, so we'll have to filter those rows out when looking at age."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gender Distribution\n",
    "A very simple question to anwser should be wether one sex was more likely to survive. As survival and sex are discrete variables we can compare survival propability across both groups fairly easy. Lets first look at the sex-ratio across all passengers on the ship. I choose a pie chart as this gives us a quick and good visual representation of the passenger sex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1116d11d0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u8qw/ll4wL193Ft04nycyt/JVNyxKK1z0J905wglLPsSlVWT/eMFHTl0pbEJ3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\nJIhZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114ce4090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "passengers_by_sex = titanic_data.groupby('Sex')\n",
    "passengers_by_sex.size().plot.pie(figsize=(6, 6), title='Passenger Sex', autopct='%.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chart shows us that there were almost twice as many male passengers then female passengers on the ship. Next we repeat this step for all passengers who survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112a63dd0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fy2xNQunKm475mu3r/gjP0AEk2EuQUbEy3G+JyJBSM4utuRWLvySE+LOUckh1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\nLETJaMpkb86/ywGIAExEkjjzsyPHvMrYX0THQl1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114ce4310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survivors_by_sex = survivors.groupby('Sex')\n",
    "survivors_by_sex.size().plot.pie(figsize=(6, 6), title='Survivor Sex', autopct='%.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This reveals a big difference in survivorship rates. With regards to survivorship the numbers have flipped. The chart now shows that female survivors outnumber males by almost twice. Lets examine the survivorship rates within those groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "74.2038216561 % of females survived\n",
      "18.8908145581 % of males survived\n",
      "38.3838383838 % total survival rate\n",
      "3.92803716473\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sexy/anaconda/envs/DAND/lib/python2.7/site-packages/ipykernel/__main__.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "/Users/sexy/anaconda/envs/DAND/lib/python2.7/site-packages/ipykernel/__main__.py:7: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n"
     ]
    }
   ],
   "source": [
    "def calc_survivorship_rates_by_sex(passengers, sex):\n",
    "    # accepts a DataFrame and a value for the sex to look at \n",
    "    # and calculates the survival rate for provided sex\n",
    "    # returns the survival_percentage for specified sex\n",
    "    df_by_passenger_sex = passengers[passengers['Sex'] == sex]\n",
    "    df_by_sex_survivors = df_by_passenger_sex[passengers['Survived'] == 1]\n",
    "    df_by_sex_dead = df_by_passenger_sex[passengers['Survived'] == 0]\n",
    "    total_rows = len(df_by_passenger_sex)\n",
    "    num_survived = len(df_by_sex_survivors)\n",
    "    num_dead = len(df_by_sex_dead)\n",
    "    survival_percent = (float(num_survived)/total_rows) * 100\n",
    "    \n",
    "    return survival_percent\n",
    "\n",
    "female_survivorship_rate = calc_survivorship_rates_by_sex(titanic_data, 'female')\n",
    "male_survivorship_rate = calc_survivorship_rates_by_sex(titanic_data, 'male')\n",
    "\n",
    "print \"{} % of females survived\".format(female_survivorship_rate)\n",
    "print \"{} % of males survived\".format(male_survivorship_rate)\n",
    "print \"{} % total survival rate\".format((float(len(survivors))/len(titanic_data)) * 100) \n",
    "\n",
    "print female_survivorship_rate/male_survivorship_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In depth analysis confirms what we've already seen. Females were 3.9 times more likely to survive the accident. We also calculated a overall survival rate (**38.4** %) which could be useful when comparing survival rates of different groups."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To analyse whether age was a factor lets get a rough overview by letting Pandas describe the data. Lets also use a histogram to visualize the distibution. When analyzing age we drop rows that dont have an age field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    714.000000\n",
      "mean      29.699118\n",
      "std       14.526497\n",
      "min        0.420000\n",
      "25%       20.125000\n",
      "50%       28.000000\n",
      "75%       38.000000\n",
      "max       80.000000\n",
      "Name: Age, dtype: float64\n",
      "28.0\n"
     ]
    }
   ],
   "source": [
    "known_age_column = titanic_data['Age'].dropna()\n",
    "print known_age_column.describe()\n",
    "print known_age_column.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x112d4a750>,\n",
       " <matplotlib.text.Text at 0x1154f0950>,\n",
       " <matplotlib.text.Text at 0x112d7d150>]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jWyknnhet5HmdDqe8D8dRThoHV/8/A9g5IiZUYyqeRzmJfIbSlfLb6nn3twqqfokfTvmF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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11587fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_hist = known_age_column.hist(bins=20)\n",
    "age_hist.set(title=\"Distirbution of age across passengers\", ylabel=\"Number of passengers\", xlabel=\"Age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspecting the age data shows us:\n",
    " - Passenger age is normally distributed\n",
    " - There are a lot of infants on the ship (< 5)\n",
    " - Overall the passengers are fairly young (mean 29.7, median 28)\n",
    "\n",
    "We should explore whether age was correleated with survival and take a specific look at infants, children and adults. \n",
    "\n",
    "First we filter out the survivors from our dataset and repeat the first step again to get a rough feel for the data. Afterwards we plot a histogram again but this time we plot one with survivors highlighted per age group. This should give us a rough idea about which age group was more likely to survive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    290.000000\n",
       "mean      28.343690\n",
       "std       14.950952\n",
       "min        0.420000\n",
       "25%       19.000000\n",
       "50%       28.000000\n",
       "75%       36.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survivors_known_age = survivors[\"Age\"].dropna()\n",
    "survivors_known_age.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x113278810>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ECd0vbwBDio4fGP1NRVpbZ9HW1p44yFrX1NSb5ub+NVnP1tZZQNdv1tbWWXzw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\nRfkZ4FeEReni/kd+6tmbMLPrfcLYl3U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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112f9eb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([known_age_column, survivors_known_age.values], color=['b', 'g'], bins=20)\n",
    "plt.title(\"Age of survivors (green) vs passenger age\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Number of passengers/survivors\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the histogram we see age didn't seem to be a big factor for most age groups, however we can tell that infants and very young children indeed seemed to be more likely to survive.\n",
    "Once again we can calculate the survivorship rate for that group. For that we refactor our previous function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_survivorship_rates_by_column_query(passengers, query):\n",
    "    # accepts a DataFrame and a query\n",
    "    # applies the query and seperates survivors and non survivors\n",
    "    # and calculates and returns the survivor rate accordingly\n",
    "    df_by_query = passengers[query]\n",
    "    df_by_query_survivors = df_by_query[passengers['Survived'] == 1]\n",
    "    df_by_query_dead = df_by_query[passengers['Survived'] == 0]\n",
    "    total_rows = len(df_by_query)\n",
    "    num_survived = len(df_by_query_survivors)\n",
    "    num_dead = len(df_by_query_dead)\n",
    "    if total_rows > 0:\n",
    "        survival_rate = (float(num_survived)/total_rows)\n",
    "        return survival_rate\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sexy/anaconda/envs/DAND/lib/python2.7/site-packages/ipykernel/__main__.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "/Users/sexy/anaconda/envs/DAND/lib/python2.7/site-packages/ipykernel/__main__.py:7: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7021276595744681"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calc_survivorship_rates_by_column_query(titanic_data, titanic_data[\"Age\"] <= 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing the function we look for the survival rate of passengers below 6 years of age. At 70% it is higher then avergage which confirms our suspicion.\n",
    "\n",
    "### Plotting survival rate across age groups\n",
    "Using the function we wrote above we can plot rates across different ages using a line graph. we can generate an array of ages from 0 to the max passenger age for the x axis and show the survival rate on the y axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sexy/anaconda/envs/DAND/lib/python2.7/site-packages/ipykernel/__main__.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "/Users/sexy/anaconda/envs/DAND/lib/python2.7/site-packages/ipykernel/__main__.py:7: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115e26410>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZmOc+3gQc7z7uARK7H3yWGa2rJTawdE5dOSXBRG7qOzWxFg+A5n3qbhERkdyU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\niIiISGWUeIiIiEhl/g2/H03SHCtVQgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115bebf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate an array of all possible ages in increments of one\n",
    "age_range = np.arange(0, known_age_column.max(), 1)\n",
    "# generate corresponding array of survival rates for each age in age_range\n",
    "survivor_rate_for_age = [calc_survivorship_rates_by_column_query(titanic_data, titanic_data[\"Age\"] <= age) for age in age_range]\n",
    "\n",
    "plt.plot(age_range, survivor_rate_for_age)\n",
    "plt.title(\"Survival rate for age\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Survival rate\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we look as this line graph we can see that survivorship rates drop off steeply till the age of 20. It levels out at 40% past 25. Looking at this graph we can see that indeed infants and young children and even teen agers where the most likely to survive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relationship between Age and Sex\n",
    "\n",
    "Now that we have a general idea, we can look back at the relationship between survival, age and sex.\n",
    "For this we split the data again, into male and female and calculate survival percentages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sexy/anaconda/envs/DAND/lib/python2.7/site-packages/ipykernel/__main__.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "/Users/sexy/anaconda/envs/DAND/lib/python2.7/site-packages/ipykernel/__main__.py:7: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SPZGbJl/FgOR+7aqDEO3hD/j5vnAZ/9vyKUWufbsS2Cw2/EFjzRuXz836sg2s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\nKHiIiIiIb/4/4Nu9AKp//ugAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115e31250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calc_survival_rate_for_age_by_sex(sex, age_range):\n",
    "    # select only passengers from sex from titanic_data \n",
    "    # and apply the calc_survivorship_rates_by_column_query for each age in age_range\n",
    "    # returns an array of survival rates corresponding to each age in age_range  \n",
    "    df_passengers_by_sex = titanic_data[titanic_data[\"Sex\"] == sex]\n",
    "    return [calc_survivorship_rates_by_column_query(df_passengers_by_sex, df_passengers_by_sex[\"Age\"] <= age) for age in age_range]\n",
    "\n",
    "# generate an array of all possible ages in increments of one\n",
    "all_ages = np.arange(0, titanic_data[\"Age\"].max(), 1)\n",
    "m_surv_rate = calc_survival_rate_for_age_by_sex(\"male\", all_ages)\n",
    "f_surv_rate = calc_survival_rate_for_age_by_sex(\"female\", all_ages)\n",
    "\n",
    "\n",
    "plt.plot(all_ages, m_surv_rate)\n",
    "plt.plot(all_ages, f_surv_rate)\n",
    "plt.legend([\"Male\", \"Female\"])\n",
    "plt.title(\"Survival rate across sexes\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Survival rate\")\n",
    "plt.ylim(0,1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting survival rates across gender and age we see an interessting development. While female children seem to have a small edge both male and female children are simlarly likely to survive. However from the age of 12 onwards the graph seems to diverge heavily. While it goes up for woman it drops sharply to about 20% for men and stays there.\n",
    "This is in line with our previous findings about the general difference in survival rates across sexes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relationship between class, sex and survival\n",
    "\n",
    "Now we can look at whether class factored into survival. Before, we've been doing the heavy lifting with calculating surival percentages and composing charts however once I got to read the Seaborn documentation in depth I found that SNS can do that heavy lifting for us. In particular the barplot function seems to be best suited if we look at discrete vs categorical data. First we let Seaborn plot a general bar plot for us, that shows the survival rate across the three available passengere classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x114802310>,\n",
       " <matplotlib.text.Text at 0x115f128d0>,\n",
       " <matplotlib.text.Text at 0x1150a2d10>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8BTgYWBn4BnAfcHBmnlpzfZIkaYQZ9LBIRDwL+HZmPgt4OrBWZk4DvhYRfQ2X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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1131f95d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_barplt = sns.barplot(x=\"Pclass\", y=\"Survived\", data=titanic_data)\n",
    "sns.barplot()\n",
    "class_barplt.set(title=\"Difference in survival across passenger class\", ylabel=\"Survival rate\", xlabel=\"Passenger class\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Studying this we see a big difference in survival rates accross classes. Passengers of the highest socioeconmic class were more than twice more likely to survive!\n",
    "From looking at the sex/survival data we know that there can be crass differences in survival across these groups. We should examine whether that also holds true for passenger class.\n",
    "Fortunately Seaborne provides us with an easy way to do this by changing up the parameters and adding a hue parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x112e73a90>, <matplotlib.text.Text at 0x1131a3850>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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REYmVwoWIiIjESuFCREREYqVwISIiIrFSuBAREZFYtct2QzOrAPoBHwIJd/8u\ntqpERESkZLU4XJhZArgSOAOoADYGLjezxcApChkiIiJtWzbDIqcDvwZOBb6Nlj0E/BS4JJ6yRERE\npFRlEy5OAk5z97FAEsDdJwDHA7+MrzQREREpRdnMuegHvJFh+WRgnWyKMLP2wCjgUKAGGOnu1zex\n7oBo3YHA+8CZ7v6vbNoVERGR+GXTc/EJsG2G5fsDH2VZx3XA1sBuhOGWYWZ2aPpKZlYJPA28DfwQ\neBB40Mx6ZtmuiIiIxCybnotrgVFm1psQTvY0sxMJEzwHt3RnZtYJ+C2wr7tPBiab2TXAacADaav/\nBvja3U+JHl9iZvsD2wBPZfFaREREJGYtDhfufoeZrQH8HugI3ArMBn7v7mOyqGGLqI5JKcv+A1yU\nYd1dgYfT6tk+izZFREQkR1o8LGJm6wO3ufv6wNrAOu7eC7jJzDINl6xKb2COu9emLJsJdDCzHmnr\nbgjMMbNbzewrM3vRzHbKok0RERHJkWyGRT4mTNyc7e5zUpb3A54HOrVwf51Yfkprg4bH7dOWdwHO\nB24E9gOOBJ42M3P36c1prKwsQVlZooUlikipKy/XBYnbmvLyMtq108+9EJoVLszsVOCc6GECeNXM\n6tJW6wZ8mkUN39A4RDQ8rklbXgu84e6XRo8nm9k+hOtuXNWcxrp370wioXAh0tZUVnYsdAmSZ5WV\nHenWrXOhy2iTmttzMRboSRhGGQr8DViU8nx99HhiFjVMB3qaWZm7J6Nl6wBL3H1+2rpfAdPSlr0H\nfK+5jc2bt1g9FyJt0MKFSwpdguTZwoVLqK5eXOgyWpXmhrVmhQt3rwGGA5hZPXBttCwObwLfATsA\nL0bLdgFeybDuS8CgtGWbAPc0t7Fksp5ksj6LMkWklNXVJVe9krQqdXVJamv1cy+EbM4WudTM2plZ\nH6A8WpwgDGVs6+7NPtBH+1tiZncBY8zsOGA94GzgGAAz6wUscPdvgDHAaWY2lBAojiHM9RjX0tch\nIiIiuZHN2SL7AF8AnxEmd35MuHjWu4TTUrMxGHgNeBa4GbjY3RtOOf0KOBzA3T8D9gUOBqYABwIH\nuPtXWbYrIiIiMcvmbJErgNeBm4D7CPcT6UsYNjk2myLcfUm0baPt3b0s7fEkwkWzREREis7ixWFK\nYufOXQpcSeFkc45Of+ACd3+KMF9isbvfTOh9OGelW4qIiLRijz76ECeccDQnnHA0jz32UKHLKZhs\nwkUdsCD7MhE8AAAXqklEQVT6+gPCPT4gDGlsFkdRIiIipaampoYJE8aRTCZJJpPce+84amriOveh\ntGQTLt4mzHmAMM9i5+jr9WKpSEREpATNmjWD2trlF5uura1l1qwZBayocLKZc3EVcL+ZLQXGA5ea\n2ePA5sAzcRYnIiIipafFPRfu/hCwHfCSu39OuAx3LeGGYifFW56IiIiUmhb3XJjZA8AQd38XwN2f\nJ9xTRERERCSrORd7ALqOroiIiGSUTbgYC1xtZv3NLP2GYyIiItLGZTOh80Dg+8DPAcxshSfdvTzD\nNiIiItJGZBMuRsRehYiIiLQa2dy47M5cFCIiIiKtQzZzLkRERESapHAhIiIisVK4EBERkVgpXIiI\niEismjWh08wGNXeH7v5C9uWIiIhIqWvu2SL/AuqBxCrWqwd0nQsREZE2rLnhol9OqxAREZFWo1nh\nwt0/bc56ZtZh9coRERGRUpfNXVF7AEOAASwfAkkA7YHNgK6xVSciIiIlJ5uzRUYBRwNzgEHAdGBN\nYAfgyvhKExERkVKUTbjYCzjG3Y8EHLjW3bcBbgP6x1mciIiIlJ5swkUX4K3o62nAltHXNwO7x1GU\niIiIlK5swsV0oG/09XvA5tHXNUD3OIoSERGR0pXNLdcnAmPN7Bjgn8C9ZvYScAjwfpzFiYiIZCNZ\nV4f7tLy2OXPmjEbLpk17l+rq6rzW0b//ACoqKvLaZrpswsUQYA2gr7v/1cwmAn8D5gOHxVmciIhI\nNhbOnM9H/xpHXVW3vLW5IJlstOyje+5mbln+7rTx6YJqGDaCrbYamLc2M8kmXFS4+1kND9z9ZDO7\nCFjo7rXxlSYiIpK9vlXd2Lhnz7y1N2vpUpi9Yu9F365dWbvAvQiFkE24mBH1Vox19+cA3H1evGWJ\niIhIqcqmr+ZUYB3gaTP7xMwuNbMNY65LRERESlSLw4W73+Xu+wLrATcCBwDvm9kLZnZs3AWKiIhI\nacl6lom7z3T3PwA7AacDWxAupCUiIiJtWDZzLgAws52BXxLOEGkH3AfcEVNdIiIiUqKyuXHZlcAv\ngO8BzwP/B9zv7ktirk1ERERKUDY9F4cTeijubO6t2EVERKTtaHG4cPfv56IQERERaR2aFS7M7Fng\nUHefH33dJHffI5bKREREpCQ1t+fiU6Au+vozoD435YiIiEipa1a4cPfU61ec5u6LclSPiIiIlLhs\nrnMxw8zuNLPdY69GRERESp4u/y0iIiKx0uW/W4HFixexeLFGqkREpDjo8t8l7tFHH+KEE47mhBOO\n5rHHHip0OSIiItmHCzPb2cxGA18BVxAu/z0orsJk1WpqapgwYRzJZJJkMsm9946jpqam0GWJiEgb\np8t/l7BZs2ZQW1u77HFtbS2zZs1ggw00BUZERApHl/8WERGRWGUzLDIFuE/BQkRERDLJJlzsBmhg\nX0RERDLKJlyMBa4xs/5m1j7mekRERKTEZTPn4kDg+8DPAcxshSfdvXz1yxIREZFSlU24GBF7FSIi\nIiWuql07yll+l8/yaFlb1OJX7e53xl1ENLwyCjiUMJ9jpLtfv4ptNiBMLj3Q3V+IuyYREZGWaF9W\nxs6VXXlh4XwAdq7sSvuyrC8nVdKyuc7F0JU97+7Ds6jjOmBrwmTRDYC7zOwTd39gJduMBjpl0ZaI\niEhObLNmJT/s3AWADm00WEB2wyLp9w9pB/QCvgP+29KdmVkn4LfAvu4+GZhsZtcApwEZw4WZ/RLo\n0tK2REREcq0th4oG2QyL9EtfZmaVwO3Ai1nUsEVUx6SUZf8BLsq0spn1AK4C9gGmZtGeiIiI5FAs\n8crdFwLDgLOz2Lw3MMfda1OWzQQ6REEi3fXAWHd/N4u2REREJMfinMZaBXTNYrtOwLdpyxoer3Ad\nDTPbi3AX1hOyaAeAsrIEZWWJbDcvKuXljbNheXkZ7dqpS04kXab3i0hrVAzHgbgmdFYCRwDPZlHD\nN6SFiJTHy64EamYdgDHAKe6+NIt2AOjevTOJROsIF3Pndmy0rLKyI926dS5ANSLFrbKy8ftFpDUq\nhuNAHBM6AZYCz9DEPIlVmA70NLMyd09Gy9YBlrj7/JT1tgP6ARPNLDUdPGlmd7r7qc1pbN68xa2m\n52LhwsY3ol24cAnV1YsLUI1Iccv0fhFpjXJ5HGhuaIllQudqepNwpskOLJ8QugvwStp6/wN+kLbs\nA8KZJv9sbmPJZD3JZH12lRaZurpkxmW1tY2Xi7R1md4vIq1RMRwHVmvOhZn1BAYBM9w9mzNFcPcl\nZnYXMMbMjgPWI0wMPSZqoxewwN2/AT5Kax/gS3efk/2rEBERkTg1e8aHmV1sZnPMbKPo8U6EnoP7\ngf+Y2T/MLNtBzcHAa4Q5GzcDF7v7w9FzXwGHN7Fd6+iCEBERaUWa1XNhZicCQ4A/ALOixX8hTLjc\nCVgATAQuIJyS2iLuvoQwl6PRfA53bzIA6SZpIiIixae5wyLHA2e7+x8BzGwbYGNgiLu/Ey0bAYwk\ni3AhIiIirUdzh0U2BZ5OebwHYUjiiZRlU4G+MdUlIiIiJaq54SLBivMbBgHzonuBNKgk5boUIiIi\n0jY1d1hkCvAj4AMz6wrsDjyUts5h0Xpt0tKlS5k6Nb8vf+bMGY2WTZv2LtXV1Xmto3//AVRUVOS1\nTRERKV7NDRe3EE4V3ZIwgbM9cCOAma0L/BI4l3DNiTZp6tQpDL1hPFU9+uStzfrvFjVadvfjr5NY\nI383jF0wdzrDz4KtthqYtzZFRKS4NStcuPs9ZtYeOAVIAke4+8vR0xcR7vVxtbuPy02ZpaGqRx96\n9P5+3tr7rmYu82a/tmINPddjjU6Z7vcmIiKSH82+iJa7/4Vw+mm6K4Fh7j43tqpERESkZK32XVHd\nfXochYiIiEjroHsQi4iISKwULkRERCRWChciIiISK4ULERERiZXChYiIiMRK4UJERERipXAhIiIi\nsVK4EBERkVgpXIiIiEisFC5EREQkVgoXIiIiEiuFCxEREYmVwoWIiIjESuFCREREYqVwISIiIrFS\nuBAREZFYKVyIiIhIrBQuREREJFYKFyIiIhIrhQsRERGJlcKFiIiIxErhQkRERGKlcCEiIiKxUrgQ\nERGRWClclLDy9mtCIuVHmCgLy0RERApI4aKElZVX0KXPQCABJOjSZyBl5RWFLktERNq4doUuQFZP\n514D6NhjYwDK2rUvcDUiIiIKF62CQoWIiBQTDYuIiIhIrBQuREREJFYKFyIiIhIrhQsRERGJlcKF\niIiIxErhQkRERGKlcCEiIiKxUrgQERGRWClcSMlavHgRixcvKnQZIiKSRlfolJL06KMPMX78XQAc\nddTRHHTQIQWuSEREGqjnQkpOTU0NEyaMI5lMkkwmuffecdTU1BS6LBERiShcSMmZNWsGtbW1yx7X\n1tYya9aMAlYkIiKpFC5EREQkVgoXIiIiEquimNBpZu2BUcChQA0w0t2vb2LdA4ERwEbAh8DF7v5o\nvmoVERGRlSuWnovrgK2B3YBTgWFmdmj6Sma2OTARuA3YAvgTcL+ZDchfqSIiIrIyBe+5MLNOwG+B\nfd19MjDZzK4BTgMeSFv9SOA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150c5790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex_class_barplt = sns.barplot(x=\"Sex\", y=\"Survived\", hue=\"Pclass\", data=titanic_data);\n",
    "sex_class_barplt.set(title=\"Sex/Class differences in survival\", ylabel=\"Survival rate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This chart also shows an interessting development. While class seemed to be a determining factor in survival for both men and women, men of the highest class only barely come close to the survival rate of female passengers of the lowest class. Even when taking class into account men seem to dwarf woman when it comes to surviving the titanic desaster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "We explored several different variables in this data set and see how they correlate with the chance of survival.\n",
    "Summarizing our findings we can say:\n",
    "- Regardless of sex, children and infants were most likely to survive.\n",
    "- When approaching the mean age woman were more likely to survive\n",
    "- Class played a factor but seems to be relatively minor compared to age and sex.\n",
    "\n",
    "We posed the question: Does the saying \"woman and children\" first hold true when it comes to the titanic desaster. This analysis seems to confirm it. However we only analyzed a subset of all passengers of the Titanic (891 of the 2224) also not passengers in our sample had an age specified so the findings could change when looking at all passengers.\n",
    "\n",
    "\n",
    "#### Resources\n",
    "- Seaborn Documentation\n",
    "- Matplotlib Cheatsheet: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Matplotlib_Cheat_Sheet.pdf\n",
    "- Pandas Cheatsheet: https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:DAND]",
   "language": "python",
   "name": "conda-env-DAND-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}