intro-data-proj-titanic.ipynb

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    "# Titanic Survivability\n",
    "## Introduction\n",
    "<p>On 15 April 1912, Titanic, the largest ship of its time, sank after hitting an iceberg in the North Atlantic Ocean. Of the 2,224 people estimated on board, only 705 survived. Although limited, there were enough lifeboats to save 1,178 people and yet fewer made it.</p>\n",
    "\n",
    "## Questions\n",
    "<p>How likely would a passenger survive the tragedy?</p>\n",
    "\n",
    "- If you are rich, would you most likely be prioritized?\n",
    "- \"Women and Children First\". Does your age or gender influence your chances of survivability?\n",
    "    \n",
    "### Objectives\n",
    "<p>This study analyzes the likelihood of survivability of passengers on board of the Titanic. The analysis is divided according to Demographics and Social Economic Status. The former will be based on Gender and Age and the latter will be based on Ticket Class and Fare.</p>\n",
    "\n",
    "### Variables\n",
    "Dependent Variable: If the passenger survived or not. <br>\n",
    "Independent Variables: 1. Gender 2. Age 3. Ticket Class 4. Fare. <br>\n",
    "Null Hypothesis: The likelihood of surviving the event are not influenced by demographics and socio economic status. <br> \n",
    "Hypothesis: The likelihood of survival is influenced by the demographics and socio economic status of the passengers."
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    "## Data Wrangling\n",
    "### Data Acquisition\n",
    "<p>The data provided is a list of names of 891 of the 2,224 passengers with the corresponding information for each on board. Below is the Data Dictionary of the data set from [Kaggle](https://www.kaggle.com/c/titanic/data).</p>\n",
    "\n",
    "- survival: Survival (0 = No, 1 = Yes)\n",
    "- pclass: Ticket class (1st = Upper, 2nd = Middle, 3rd = Lower)\n",
    "- sex: Sex\n",
    "- Age: Age in years (Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5)\n",
    "- sibsp: # of siblings / spouse aboard the Titanic (Sibling = brother, sister, stepbrother, stepsister, Spouse = husband, wife (mistresses and fiancés were ignored))\n",
    "- parch: # of parents / children aboard the Titanic (Parent = mother, father, Child = daughter, son, stepdaughter, stepson, Some children travelled only with a nanny, therefore parch=0 for them.)\n",
    "- ticket: Ticket number\n",
    "- fare: Passenger fare\n",
    "- cabin: Cabin number\n",
    "- embarked: Port of Embarkation (C = Cherbourg, Q = Queenstown, S = Southampton)"
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    "import pandas as pd\n",
    "import numpy as np\n",
    "# Graphing\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_style(\"darkgrid\")\n",
    "titanic_df = pd.read_csv('titanic-data.csv') # Read CSV and stores in to titanic_df variable."
   ]
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    "### Data Cleaning\n",
    "<p>Once my file is loaded, I check if there are duplicate values in any of the column that could affect the analysis. I am also looking for inconsistencies in values, data type or missing values that may affect the investigation.</p>"
   ]
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    "# PassengerId and Name must be unique. I check if there are any duplicate values in each col.\n",
    "# There are no duplicates on the data.\n",
    "print titanic_df.duplicated('PassengerId').sum()\n",
    "print titanic_df.duplicated('Name').sum()\n",
    "# I also check if the Ticket # is unique. Turns out that the Ticket isn't unique for each passenger.\n",
    "# Seems odd and I will take note and come back to it if needed.\n",
    "print titanic_df.duplicated('Ticket').sum()"
   ]
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       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
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     "execution_count": 3,
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   "source": [
    "# I check the data type of each column for any inconsistencies. Seems odd to have Age as an float64.\n",
    "# I investigate and print out a couple of rows with non-whole number age.\n",
    "titanic_df.dtypes"
   ]
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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>78</th>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Caldwell, Master. Alden Gates</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>248738</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>470</td>\n",
       "      <td>1</td>\n",
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       "      <td>Baclini, Miss. Helene Barbara</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>645</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Miss. Eugenie</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>756</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hamalainen, Master. Viljo</td>\n",
       "      <td>male</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>250649</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>804</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Thomas, Master. Assad Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2625</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>832</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Richards, Master. George Sibley</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29106</td>\n",
       "      <td>18.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     PassengerId  Survived  Pclass                             Name     Sex  \\\n",
       "78            79         1       2    Caldwell, Master. Alden Gates    male   \n",
       "305          306         1       1   Allison, Master. Hudson Trevor    male   \n",
       "469          470         1       3    Baclini, Miss. Helene Barbara  female   \n",
       "644          645         1       3           Baclini, Miss. Eugenie  female   \n",
       "755          756         1       2        Hamalainen, Master. Viljo    male   \n",
       "803          804         1       3  Thomas, Master. Assad Alexander    male   \n",
       "831          832         1       2  Richards, Master. George Sibley    male   \n",
       "\n",
       "      Age  SibSp  Parch  Ticket      Fare    Cabin Embarked  \n",
       "78   0.83      0      2  248738   29.0000      NaN        S  \n",
       "305  0.92      1      2  113781  151.5500  C22 C26        S  \n",
       "469  0.75      2      1    2666   19.2583      NaN        C  \n",
       "644  0.75      2      1    2666   19.2583      NaN        C  \n",
       "755  0.67      1      1  250649   14.5000      NaN        S  \n",
       "803  0.42      0      1    2625    8.5167      NaN        C  \n",
       "831  0.83      1      1   29106   18.7500      NaN        S  "
      ]
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   "source": [
    "# Saw 7 entries that are less than 1. Looking at their names, I see prefix \"Master\",\n",
    "# which is what is given to children. In these case, these were babies below the age of 1.\n",
    "non_whole = titanic_df['Age'] < 1\n",
    "titanic_df[non_whole]"
   ]
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       "  <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",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
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       "      <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>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
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      "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  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      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  "
      ]
     },
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   ],
   "source": [
    "# I check the head of the data set.\n",
    "titanic_df.head()"
   ]
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       "  <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",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
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       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
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       "      <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>19.0</td>\n",
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       "      <td>889</td>\n",
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       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
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       "      <td>1</td>\n",
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       "      <td>23.45</td>\n",
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       "      <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>30.00</td>\n",
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       "      <th>890</th>\n",
       "      <td>891</td>\n",
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       "      <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",
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      "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      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      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      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and the tail.\n",
    "titanic_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "nbpresent": {
     "id": "c623ef57-3347-45df-ae23-30a73d1b9325"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 177 logs with their Age not specified.\n"
     ]
    }
   ],
   "source": [
    "# Looking at Age col, I see that there are some empty fields. I check how many there are.\n",
    "missing_age = titanic_df['Age'].isnull()\n",
    "print 'There are {} logs with their Age not specified.'.format(missing_age.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory\n",
    "### Women and Children First\n",
    "<p>The 177 logs that have missing age will affect the analysis. I attempt to limit the descrepency by rephrasing my question to distinguish the survivability between women and children vs Male adult passengers.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Of the 342 passengers that survived from the sample of 891 on the data provided, 259 are women and children.\n"
     ]
    }
   ],
   "source": [
    "titanic_df['womChil'] = 0 # Created a column that groups women and children.\n",
    "women = titanic_df['Sex'] == 'female' # Criteria - all female passengers.\n",
    "child = titanic_df['Age'] < 19 # Criteria - all children under the age of 18 years.\n",
    "# For passengers with missing age, I identify the children from the group of male passengers by looking \n",
    "# for the title 'Master' in their Names, which are titles given to minors on board without their\n",
    "# parents.\n",
    "masters = titanic_df['Name'].str.contains('Master') # Criteria - all male children.\n",
    "titanic_df.loc[(women | child | masters),'womChil'] = 1 # Add 1 (yes) that fits the criterias\n",
    "women_children = titanic_df['womChil'] == 1 # Criteria - all Women and Children.\n",
    "survived = titanic_df['Survived'] == 1 # Criteria - all passengers that survived.\n",
    "\n",
    "# Extracted all women and children that sruvived.\n",
    "women_children_survived = titanic_df[women_children & survived]\n",
    "women_children_survived['PassengerId'].count() # Counted the women and children survivors.\n",
    "print \"Of the {} passengers that survived from the sample of 891 on the data provided, {} are \\\n",
    "women and children.\".format(titanic_df[survived]['PassengerId'].count(), \\\n",
    "                            women_children_survived['PassengerId'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    },
    {
     "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>womChil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Woolner, Mr. Hugh</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19947</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>C52</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>299</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Saalfeld, Mr. Adolphe</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19988</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>C106</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>508</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bradley, Mr. George (\"George Arthur Brayton\")</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111427</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>741</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hawksford, Mr. Walter James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16988</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>D45</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>840</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Marechal, Mr. Pierre</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11774</td>\n",
       "      <td>29.7000</td>\n",
       "      <td>C47</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Williams, Mr. Charles Eugene</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244373</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>548</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Padro y Manent, Mr. Julian</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SC/PARIS 2146</td>\n",
       "      <td>13.8625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Mamee, Mr. Hanna</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2677</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Moss, Mr. Albert Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>312991</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>302</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McCoy, Mr. Bernard</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>367226</td>\n",
       "      <td>23.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>445</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johannesen-Bratthammer, Mr. Bernt</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65306</td>\n",
       "      <td>8.1125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>644</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Foo, Mr. Choong</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>692</th>\n",
       "      <td>693</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Lam, Mr. Ali</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>829</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>McCormack, Mr. Thomas Joseph</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>367228</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "55            56         1       1   \n",
       "298          299         1       1   \n",
       "507          508         1       1   \n",
       "740          741         1       1   \n",
       "839          840         1       1   \n",
       "17            18         1       2   \n",
       "547          548         1       2   \n",
       "36            37         1       3   \n",
       "107          108         1       3   \n",
       "301          302         1       3   \n",
       "444          445         1       3   \n",
       "643          644         1       3   \n",
       "692          693         1       3   \n",
       "828          829         1       3   \n",
       "\n",
       "                                              Name   Sex  Age  SibSp  Parch  \\\n",
       "55                               Woolner, Mr. Hugh  male  NaN      0      0   \n",
       "298                          Saalfeld, Mr. Adolphe  male  NaN      0      0   \n",
       "507  Bradley, Mr. George (\"George Arthur Brayton\")  male  NaN      0      0   \n",
       "740                    Hawksford, Mr. Walter James  male  NaN      0      0   \n",
       "839                           Marechal, Mr. Pierre  male  NaN      0      0   \n",
       "17                    Williams, Mr. Charles Eugene  male  NaN      0      0   \n",
       "547                     Padro y Manent, Mr. Julian  male  NaN      0      0   \n",
       "36                                Mamee, Mr. Hanna  male  NaN      0      0   \n",
       "107                         Moss, Mr. Albert Johan  male  NaN      0      0   \n",
       "301                             McCoy, Mr. Bernard  male  NaN      2      0   \n",
       "444              Johannesen-Bratthammer, Mr. Bernt  male  NaN      0      0   \n",
       "643                                Foo, Mr. Choong  male  NaN      0      0   \n",
       "692                                   Lam, Mr. Ali  male  NaN      0      0   \n",
       "828                   McCormack, Mr. Thomas Joseph  male  NaN      0      0   \n",
       "\n",
       "            Ticket     Fare Cabin Embarked  womChil  \n",
       "55           19947  35.5000   C52        S        0  \n",
       "298          19988  30.5000  C106        S        0  \n",
       "507         111427  26.5500   NaN        S        0  \n",
       "740          16988  30.0000   D45        S        0  \n",
       "839          11774  29.7000   C47        C        0  \n",
       "17          244373  13.0000   NaN        S        0  \n",
       "547  SC/PARIS 2146  13.8625   NaN        C        0  \n",
       "36            2677   7.2292   NaN        C        0  \n",
       "107         312991   7.7750   NaN        S        0  \n",
       "301         367226  23.2500   NaN        Q        0  \n",
       "444          65306   8.1125   NaN        S        0  \n",
       "643           1601  56.4958   NaN        S        0  \n",
       "692           1601  56.4958   NaN        S        0  \n",
       "828         367228   7.7500   NaN        Q        0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Knowing that 259 of the passengers fit the criteria of women and children, I double check on\n",
    "# the remaining survivors that did not fit the criteria and whose age were missing from the records\n",
    "# and try find any other clue that might distinguish the the male passengers as children.\n",
    "non_womChil = titanic_df['womChil'] == 0\n",
    "print titanic_df[non_womChil & survived & missing_age]['Age'].isnull().sum()\n",
    "titanic_df[non_womChil & survived & missing_age].sort_values('Pclass')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def survivability(data, criteria):\n",
    "    yes = data[criteria & survived]['PassengerId'].size\n",
    "    whole = data[criteria]['PassengerId'].size\n",
    "    chances = (float(yes) / float(whole))* 100\n",
    "    return chances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "survive_womenAndChild = survivability(titanic_df, women_children)\n",
    "survive_men = survivability(titanic_df, non_womChil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LAAAA0yNqAQAAYHpELQAAAEyPqAUAAIDpEbUAAAAwPaIWAAAApkfUAgAAwPSIWgAAAJge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\n/wG9M8m5jX41HgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a09b0b690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Women and Children had a 66.58% chance of surviving the tragedy\n",
      "Men only had a 16.53% chances of surviving the tragedy\n"
     ]
    }
   ],
   "source": [
    "## There is no more criteria we can use to identify the remaining surviving male passengers with \n",
    "## no specified age as children. We then move on and draw our conclusions.\n",
    "ax = sns.countplot(x='Survived', hue='womChil', data=titanic_df)\n",
    "sns.plt.show()\n",
    "\n",
    "print \"Women and Children had a {0:.2f}% chance of surviving the tragedy\".format(survive_womenAndChild)\n",
    "print \"Men only had a {0:.2f}% chances of surviving the tragedy\".format(survive_men)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preferential Treatment\n",
    "<p> We move on to the next question, would rich people have a higher chances of surviving the tragedy than the rest of the people on board Titanic?</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>womChil</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">1</th>\n",
       "      <th>count</th>\n",
       "      <td>186.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>216.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.233441</td>\n",
       "      <td>84.154687</td>\n",
       "      <td>0.356481</td>\n",
       "      <td>461.597222</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.458333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.802856</td>\n",
       "      <td>78.380373</td>\n",
       "      <td>0.693997</td>\n",
       "      <td>246.737616</td>\n",
       "      <td>0.611898</td>\n",
       "      <td>0.484026</td>\n",
       "      <td>0.499418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>30.923950</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>270.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>60.287500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>472.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>93.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>670.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>890.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">2</th>\n",
       "      <th>count</th>\n",
       "      <td>173.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "      <td>184.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.877630</td>\n",
       "      <td>20.662183</td>\n",
       "      <td>0.380435</td>\n",
       "      <td>445.956522</td>\n",
       "      <td>0.402174</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.494565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.001077</td>\n",
       "      <td>13.417399</td>\n",
       "      <td>0.690963</td>\n",
       "      <td>250.852161</td>\n",
       "      <td>0.601633</td>\n",
       "      <td>0.500623</td>\n",
       "      <td>0.501335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.670000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>234.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>14.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>435.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>668.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>887.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">3</th>\n",
       "      <th>count</th>\n",
       "      <td>355.000000</td>\n",
       "      <td>491.000000</td>\n",
       "      <td>491.000000</td>\n",
       "      <td>491.000000</td>\n",
       "      <td>491.000000</td>\n",
       "      <td>491.000000</td>\n",
       "      <td>491.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25.140620</td>\n",
       "      <td>13.675550</td>\n",
       "      <td>0.393075</td>\n",
       "      <td>439.154786</td>\n",
       "      <td>0.615071</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.405295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.495398</td>\n",
       "      <td>11.778142</td>\n",
       "      <td>0.888861</td>\n",
       "      <td>264.441453</td>\n",
       "      <td>1.374883</td>\n",
       "      <td>0.428949</td>\n",
       "      <td>0.491450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>8.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>432.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>666.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>69.550000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Age        Fare       Parch  PassengerId       SibSp  \\\n",
       "Pclass                                                                      \n",
       "1      count  186.000000  216.000000  216.000000   216.000000  216.000000   \n",
       "       mean    38.233441   84.154687    0.356481   461.597222    0.416667   \n",
       "       std     14.802856   78.380373    0.693997   246.737616    0.611898   \n",
       "       min      0.920000    0.000000    0.000000     2.000000    0.000000   \n",
       "       25%     27.000000   30.923950    0.000000   270.750000    0.000000   \n",
       "       50%     37.000000   60.287500    0.000000   472.000000    0.000000   \n",
       "       75%     49.000000   93.500000    0.000000   670.500000    1.000000   \n",
       "       max     80.000000  512.329200    4.000000   890.000000    3.000000   \n",
       "2      count  173.000000  184.000000  184.000000   184.000000  184.000000   \n",
       "       mean    29.877630   20.662183    0.380435   445.956522    0.402174   \n",
       "       std     14.001077   13.417399    0.690963   250.852161    0.601633   \n",
       "       min      0.670000    0.000000    0.000000    10.000000    0.000000   \n",
       "       25%     23.000000   13.000000    0.000000   234.500000    0.000000   \n",
       "       50%     29.000000   14.250000    0.000000   435.500000    0.000000   \n",
       "       75%     36.000000   26.000000    1.000000   668.000000    1.000000   \n",
       "       max     70.000000   73.500000    3.000000   887.000000    3.000000   \n",
       "3      count  355.000000  491.000000  491.000000   491.000000  491.000000   \n",
       "       mean    25.140620   13.675550    0.393075   439.154786    0.615071   \n",
       "       std     12.495398   11.778142    0.888861   264.441453    1.374883   \n",
       "       min      0.420000    0.000000    0.000000     1.000000    0.000000   \n",
       "       25%     18.000000    7.750000    0.000000   200.000000    0.000000   \n",
       "       50%     24.000000    8.050000    0.000000   432.000000    0.000000   \n",
       "       75%     32.000000   15.500000    0.000000   666.500000    1.000000   \n",
       "       max     74.000000   69.550000    6.000000   891.000000    8.000000   \n",
       "\n",
       "                Survived     womChil  \n",
       "Pclass                                \n",
       "1      count  216.000000  216.000000  \n",
       "       mean     0.629630    0.458333  \n",
       "       std      0.484026    0.499418  \n",
       "       min      0.000000    0.000000  \n",
       "       25%      0.000000    0.000000  \n",
       "       50%      1.000000    0.000000  \n",
       "       75%      1.000000    1.000000  \n",
       "       max      1.000000    1.000000  \n",
       "2      count  184.000000  184.000000  \n",
       "       mean     0.472826    0.494565  \n",
       "       std      0.500623    0.501335  \n",
       "       min      0.000000    0.000000  \n",
       "       25%      0.000000    0.000000  \n",
       "       50%      0.000000    0.000000  \n",
       "       75%      1.000000    1.000000  \n",
       "       max      1.000000    1.000000  \n",
       "3      count  491.000000  491.000000  \n",
       "       mean     0.242363    0.405295  \n",
       "       std      0.428949    0.491450  \n",
       "       min      0.000000    0.000000  \n",
       "       25%      0.000000    0.000000  \n",
       "       50%      0.000000    0.000000  \n",
       "       75%      0.000000    1.000000  \n",
       "       max      1.000000    1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To answer the question, we group the passengers according to Pclass and see the describe info.\n",
    "# There were 216 Passengers in Upper Class. Their Fare price has a maximum value of 512.33.\n",
    "# There are 184 Passengers in the Middle Class. Max Fare is 73.50\n",
    "# There are 491 Passengers in the Lowest Class. Max Fare is 69.50\n",
    "passengers_by_class = titanic_df.groupby(['Pclass'])\n",
    "passengers_by_class.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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>womChil</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Leonard, Mr. Lionel</td>\n",
       "      <td>male</td>\n",
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       "      <th>263</th>\n",
       "      <td>264</td>\n",
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       "      <td>Harrison, Mr. William</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
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       "      <th>271</th>\n",
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       "      <td>3</td>\n",
       "      <td>Tornquist, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
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       "      <th>277</th>\n",
       "      <td>278</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Parkes, Mr. Francis \"Frank\"</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239853</td>\n",
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       "      <th>302</th>\n",
       "      <td>303</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mr. William Cahoone Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
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       "      <td>LINE</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>414</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Cunningham, Mr. Alfred Fleming</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239853</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>467</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Campbell, Mr. William</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239853</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>S</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>482</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Frost, Mr. Anthony Wood \"Archie\"</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239854</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>597</th>\n",
       "      <td>598</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mr. Alfred</td>\n",
       "      <td>male</td>\n",
       "      <td>49.0</td>\n",
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       "      <th>633</th>\n",
       "      <td>634</td>\n",
       "      <td>0</td>\n",
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       "      <td>Parr, Mr. William Henry Marsh</td>\n",
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       "      <th>674</th>\n",
       "      <td>675</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Watson, Mr. Ennis Hastings</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>732</th>\n",
       "      <td>733</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Knight, Mr. Robert J</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>S</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>807</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Andrews, Mr. Thomas Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A36</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>816</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fry, Mr. Richard</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112058</td>\n",
       "      <td>0.0</td>\n",
       "      <td>B102</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>822</th>\n",
       "      <td>823</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Reuchlin, Jonkheer. John George</td>\n",
       "      <td>male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19972</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                              Name   Sex  \\\n",
       "179          180         0       3               Leonard, Mr. Lionel  male   \n",
       "263          264         0       1             Harrison, Mr. William  male   \n",
       "271          272         1       3      Tornquist, Mr. William Henry  male   \n",
       "277          278         0       2       Parkes, Mr. Francis \"Frank\"  male   \n",
       "302          303         0       3   Johnson, Mr. William Cahoone Jr  male   \n",
       "413          414         0       2    Cunningham, Mr. Alfred Fleming  male   \n",
       "466          467         0       2             Campbell, Mr. William  male   \n",
       "481          482         0       2  Frost, Mr. Anthony Wood \"Archie\"  male   \n",
       "597          598         0       3               Johnson, Mr. Alfred  male   \n",
       "633          634         0       1     Parr, Mr. William Henry Marsh  male   \n",
       "674          675         0       2        Watson, Mr. Ennis Hastings  male   \n",
       "732          733         0       2              Knight, Mr. Robert J  male   \n",
       "806          807         0       1            Andrews, Mr. Thomas Jr  male   \n",
       "815          816         0       1                  Fry, Mr. Richard  male   \n",
       "822          823         0       1   Reuchlin, Jonkheer. John George  male   \n",
       "\n",
       "      Age  SibSp  Parch  Ticket  Fare Cabin Embarked  womChil  \n",
       "179  36.0      0      0    LINE   0.0   NaN        S        0  \n",
       "263  40.0      0      0  112059   0.0   B94        S        0  \n",
       "271  25.0      0      0    LINE   0.0   NaN        S        0  \n",
       "277   NaN      0      0  239853   0.0   NaN        S        0  \n",
       "302  19.0      0      0    LINE   0.0   NaN        S        0  \n",
       "413   NaN      0      0  239853   0.0   NaN        S        0  \n",
       "466   NaN      0      0  239853   0.0   NaN        S        0  \n",
       "481   NaN      0      0  239854   0.0   NaN        S        0  \n",
       "597  49.0      0      0    LINE   0.0   NaN        S        0  \n",
       "633   NaN      0      0  112052   0.0   NaN        S        0  \n",
       "674   NaN      0      0  239856   0.0   NaN        S        0  \n",
       "732   NaN      0      0  239855   0.0   NaN        S        0  \n",
       "806  39.0      0      0  112050   0.0   A36        S        0  \n",
       "815   NaN      0      0  112058   0.0  B102        S        0  \n",
       "822  38.0      0      0   19972   0.0   NaN        S        0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seems odd that on the 3 classes, the minimum Fare is 0, which is free.\n",
    "# I check the Fare priced at 0.\n",
    "free = titanic_df['Fare'] == 0\n",
    "freeloaders = titanic_df[free]\n",
    "freeloaders # Looks like some of these are crew of the titanic but I can't be sure.\n",
    "# Might be too complicated to include the Fare in my analysis. I ignore it and move on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# I create criteria for each of the class and see how many survived for each.\n",
    "first = titanic_df['Pclass'] == 1\n",
    "second = titanic_df['Pclass'] == 2\n",
    "third = titanic_df['Pclass'] == 3\n",
    "non_survived = titanic_df['Survived'] == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62.96% of the First Class Passengers survived.\n",
      "47.28% of the Second Class Passengers survived.\n",
      "24.24% of the Third Class Passengers survived.\n"
     ]
    }
   ],
   "source": [
    "survive_first = survivability(titanic_df, first)\n",
    "survive_second = survivability(titanic_df, second)\n",
    "survive_third = survivability(titanic_df, third)\n",
    "print \"{0:.2f}% of the First Class Passengers survived.\".format(survive_first)\n",
    "print \"{0:.2f}% of the Second Class Passengers survived.\".format(survive_second)\n",
    "print \"{0:.2f}% of the Third Class Passengers survived.\".format(survive_third)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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q7Xa75s+fr+eee04DBgxQ9erVNWjQID3wwAOSpDlz5mj27NmaPXu2wsLCNGvWLLVs2VKS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\nSVLoGbWSJEkKPaNWkiRJofcXHnffE08U5NwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a12f45610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You have a 62.96% chance of surviving the tragedy if you were a First Class Passenger,\n",
      "than if you were in 3rd Class, who only had a 24.24% chance of surviving.\n"
     ]
    }
   ],
   "source": [
    "ax = sns.countplot(x='Pclass', hue='Survived', data=titanic_df)\n",
    "ax.set_xticklabels(['First', 'Second', 'Third'])\n",
    "\n",
    "sns.plt.show()\n",
    "print \"You have a {0:.2f}% chance of surviving the tragedy if you were a First Class Passenger,\" \\\n",
    ".format(survive_first)\n",
    "print \"than if you were in 3rd Class, who only had a {0:.2f}% chance of surviving.\" \\\n",
    ".format(survive_third)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Wi6xWi3/CA8AfWFCQ4b/UQz0IbmKTrBbJ5T4xYLWcGAtQQUFWBQc3vH+b3bt3V9D0J+tt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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1311f190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "If we look at the average count of women and children survivors for each class, it seems that, in passenger class, women and children had a higher chances of survival if you were in the upper class than if you were in the lower class.\n"
     ]
    }
   ],
   "source": [
    "g = sns.barplot(x=\"womChil\", y=\"Survived\", hue=\"Pclass\", data=titanic_df)\n",
    "sns.plt.show()\n",
    "print \"If we look at the average count of women and children survivors for each class, it seems that, \\\n",
    "in passenger class, women and children had a higher chances of survival if you were in the upper class \\\n",
    "than if you were in the lower class.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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44AGNGDFCt956q8LCwpSRkaHCwkINGzZM69ev14IFC7j5AAAAQBNkfKmAdHRN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\n2TUGpaWlpiMAqIXQXEQBAAA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a12f4eb90>"
      ]
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     "text": [
      "The Box Plot shows that people on First Class are generally much older than the lower class. Though it goes to show that much younger passengers survived the tragedy, the variability amongst the classes are greated the higher the class. On average the Younger Survivors belonged on the lower class and more older passengers belonged on the upper class.\n"
     ]
    }
   ],
   "source": [
    "# I took a look on the combination of Age grouped according to their Economic Status \n",
    "# into account and see the probability of Survivability.\n",
    "fig = sns.boxplot(x='Pclass', y='Age', data=titanic_df, hue='Survived')\n",
    "plt.show()\n",
    "print \"The Box Plot shows that people on First Class are generally much older than the lower class. \\\n",
    "Though it goes to show that much younger passengers survived the tragedy, the variability amongst \\\n",
    "the classes are greated the higher the class. On average the Younger Survivors belonged on the lower \\\n",
    "class and more older passengers belonged on the upper class.\""
   ]
  },
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   "metadata": {},
   "source": [
    "## Conclusions\n",
    "<p>Looking back on the questions that were asked at the beginning, we can observe certain \n",
    "relations towards survivability of passengers with their Demographics and Socia Economic.\n",
    "</p>\n",
    "\n",
    "### \"Women and Children First\". Does your age or gender influence your chances of survivability?\n",
    "\n",
    "<p>With the data provided, it seems that women and children have a higher chances of suriving the tragedy than that of adult males. 67% of the women and children survived the tragedy while only 17% of the adult males survived. Please take note that analysis is limited to the fact that some of the passenger's age were missing in the dataset and that it may skew results.\n",
    "</p>\n",
    "\n",
    "### If you are rich, would you most likely be prioritized?\n",
    "\n",
    "<p>The answer to this question is most probably. Looking at the data grouped according to the Passenger Class, it would seem that the higher you are in the passenger class, the higher your chances of surviving the tragedy. 63% of the  First Class Passengers survived the tragedy. While only 48% of the Second Passengers survived and only 24% of the Third Class Passengers survived. Please take note that this part of the analysis is limited to the fact that there is no direct relationship between socio-economic status and their class. In other words, we cannot be sure that First Class passengers are rich and the rest of the passengers are poor. It would be great to have more details about each passenger such as their occupation or income for example. I looked at the Ticket Fare, but it seems that some passengers had 0 Fare, which could mean many things hence could not be a great basis in this part of the analysis.\n",
    "</p>"
   ]
  }
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