P2_Titanic_Data.ipynb

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   "source": [
    "# Introduction\n",
    "\n",
    "The Titanic data Contains demographics and passenger information from 891 of the 2224 passengers and crew on board the Titanic Ship. \n",
    "<br/>\n",
    "Data includes the following details for each of the passengers.\n",
    "\n",
    "* PassengerId : Unique Numerical ID of the Passenger\n",
    "* Survived   :     Survival\n",
    "    (0 = No; 1 = Yes)\n",
    "* Pclass     :     Passenger Class\n",
    "    (1 = 1st; 2 = 2nd; 3 = 3rd)\n",
    "* Name       :     Name\n",
    "* Sex        :     Sex\n",
    "* Age        :     Age\n",
    "* Sibsp      :     Number of Siblings/Spouses Aboard\n",
    "* Parch      :     Number of Parents/Children Aboard\n",
    "* Ticket     :     Ticket Number\n",
    "* Fare       :     Passenger Fare\n",
    "* Cabin      :     Cabin\n",
    "* Embarked   :     Port of Embarkation\n",
    "     (C = Cherbourg; Q = Queenstown; S = Southampton)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze the data\n",
    "\n",
    "After analyzing the data, we could answer following set of questions form the data available.\n",
    "\n",
    "1. What factors made people more likely to survive ?\n",
    "2. How is Fare related to Pclass ?\n",
    "3. Does Point to Embarkation had any impact on the survival of a person ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Independent Variables\n",
    "\n",
    "1. Name\n",
    "2. Age\n",
    "3. Sex\n",
    "4. Pclass\n",
    "5. Embarked\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependent Variable\n",
    "1. Survived\n",
    "2. Fare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      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  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the titanic-data.csv\n",
    "titanic_df = pd.read_csv('titanic-data.csv')\n",
    "\n",
    "# preview the data\n",
    "titanic_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Info about the data\n",
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investing the Data\n",
    "\n",
    "After Investing the data we can see that some of the entries in 'Age', 'Embarked', 'Cabin' Field are not present. We must tackle this problem before doing any kind of operation on the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Embarked field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "max_embarking_string = titanic_df['Embarked'].max()\n",
    "\n",
    "# Replacing the two missing values in Embarked columns with the most occuring one\n",
    "titanic_df['Embarked'] = titanic_df['Embarked'].fillna(max_embarking_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       891 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The point of Embarkation was missing for two persons. To complete the data set and remove the null variables, I swapped the null places with most common point of embarkation. Since only two values are missing, the result of this process will not be significant and can be dealt with. This step would result in a symmetrical dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Age Field\n",
    "\n",
    "177 Data entries of the Age filed are missing. To prevent the occurance of the null values in this case, we could have filled the empty places with the average of the 'Age' field from the rest of the data. But since the no of such entries is huge, filling the empty places might effect the age dataset in the ways that are unwanted. So we end up not changing the data but dealing with the data as it is. We can work with the data present but we must not alter the behaviour of the 'Age' Field in any way possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
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   "outputs": [
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       "29.69911764705882"
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     "execution_count": 50,
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     "output_type": "execute_result"
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M3wH7U46Q+SHwbuCEzPxIZq6nhI+lwGXAS4CDPJmZJEndo9URkRHK6MNIU/uz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdc712e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculating the mean of Age field\n",
    "age_mean = titanic_df['Age'].mean()\n",
    "titanic_df['Age'].hist()\n",
    "plt.title(\"Age histogram\")\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel(\"Age\")\n",
    "age_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# titanic_df['Age'] = titanic_df['Age'].fillna(age_mean)\n",
    "# #print  titanic_df['Age'].mean()\n",
    "# titanic_df['Age'].hist()\n",
    "# plt.xlabel('Age')\n",
    "# plt.title('After fill Age Histogram')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       891 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 177 field of age are still missing. If we try to fill the missing values based on the mean of the age variable, the effect on the age variable is immense. The filling of the values alter the behaviour of the age variable. The effect of this is very evidently seen if we try to plot the age variable before and after the filling process.\n",
    "<br/>\n",
    "So we will not alter the age variable. In our upcomming calculations we will overlook the missing values and work only with the values that are already present."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def count(nd):\n",
    "    if (type(nd) != float):\n",
    "        return len(nd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have multiple entries in a single cabin field, this function is to calculate the no of cabin entries povided in a single Cabin Field.\n",
    "<br/>\n",
    "We need to calculates the total number of cabin entries that are povided in the whole titanic database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "238.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Cabin'].str.split(pat = ' ').apply(count).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This command calculate the total number of cabin entries that are mentionde in the Titanic database.\n",
    "<br/>\n",
    "So there are only 238 cabin entries in the Titanic Database. A lot of the data is missing and we will have to work with the provided data only. Since it won't be feasable to fill up the rest unmentioned cabin entries, we won't be able to do much of anlaysis using the Cabins."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Exploration \n",
    "\n",
    "## Fare \n",
    "\n",
    "## Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      32.204208\n",
       "std       49.693429\n",
       "min        0.000000\n",
       "25%        7.910400\n",
       "50%       14.454200\n",
       "75%       31.000000\n",
       "max      512.329200\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_df['Fare'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xdff2320>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YTCRJUm0YTCRJUm0YTCRJUm0YTCRJUm0YTCRJUm0YTCRJUm0YTCRJUm0YTCRJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdea13c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fare_hist = titanic_df['Fare'].hist(bins = 20)\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Fare Histogram')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram that 0 to 25 seems to be most common or favourite choice for the fare of the ship. Even though there are people who gave more than 200 dolars for the ticket of the ship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BoxPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xdc715f8>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5wKaW9nm7ab+3kzdpNBpTqU3SDHvXu454XsgAePLJPk44YQef/vT9NVQlaTq6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\nlfn/qz5GmS7ZpoAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe05a630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bx_fare = plt.boxplot(titanic_df['Fare'], 0, 'gD')\n",
    "plt.ylabel('Fare')\n",
    "plt.title('Fare BoxPlot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Box Plot shows that the Fare data contained a lot of outliers. The Titanic Ship's fare  is wide spread. This points to the point that the ship carried the person's from a diversed backgrounds. Majority of the Passengers were form weak financial Backgrounds and weren't able to afford costly tickets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embarked Point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def embarked(df):\n",
    "    return (df.values[0],  len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "embarked_grpd = titanic_df.groupby('Embarked', as_index=False)['Embarked']\n",
    "embarked_count = embarked_grpd.apply(lambda df: len(df))\n",
    "embarked_points = embarked_grpd.apply(lambda df: df.values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[168  77 646]\n",
      "['C' 'Q' 'S']\n"
     ]
    }
   ],
   "source": [
    "print embarked_count.values\n",
    "print embarked_points.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xde84da0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kYGPg34FPUoZQ7mna9z5gbvV8rHZJktRCLQ8aEfFsoBPoB95MGSr5YrWtCxhsOmQQ6Kie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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdc71588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab\n",
    "x = range(3)\n",
    "pylab.xticks(x, embarked_points.values)\n",
    "embarked_plot = plt.bar(x, embarked_count.values,)\n",
    "plt.xlabel('Embarcation Points')\n",
    "plt.ylabel('Counts')\n",
    "plt.title('Embarked Point')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This bar plot shows that from the sample provided majority of the people embarked from point 'S', then from point 'C' and then in the last from point 'Q'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xdc81438>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S2hooSZImZNosVo2IQ4BXjiu+EFgB7Al8GbggInaqum2SJKk10yKIRMS2wKnA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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe26ef28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_hist = titanic_df['Age'].hist(bins=16)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Age Histogram')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This histogram shows that majority of the populaltion that boarded the ship were young and in 20's to 30. That young people were more inclined to travel by that ship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xd446da0>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7HElNGo3GiD+M1x4mIuJ9wF8Ar8rMy5p23QycHhFdmTnc3XE4cH27r9Hd3U1P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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd2cd5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bx_plt = titanic_df['Age'].plot(kind='box')\n",
    "plt.ylabel('Age')\n",
    "plt.title('Box Plot for Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Box Plot show that the variation in the age of passengers of the Titanic. Even though the majority of the population lie between 20 to 30, \n",
    "the age varied from a new born  to that of a 70 year old person."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## split split the names in the titanic based on the ','.\n",
    "names_str_split = titanic_df['Name'].str.split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## create a df of surnames from the split name dataFrame.\n",
    "surnames = names_str_split.apply(lambda df:df[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "surnames    Andersson\n",
       "counts              9\n",
       "Name: 16, dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## find the most common surname among the persons who boarded the ship.\n",
    "surname_df = pd.DataFrame({'surnames': surnames.values, 'counts':[1] * len(titanic_df)})\n",
    "surname_df.groupby('surnames', as_index= False).sum().loc[16]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "This shows that 'Andersson' seems to be the most common surname among the peoples that boarded the ship."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Pclass_1 = titanic_df[titanic_df['Pclass'] == 1]\n",
    "Pclass_2 = titanic_df[titanic_df['Pclass'] == 2]\n",
    "Pclass_3 = titanic_df[titanic_df['Pclass'] == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xe895550>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u7m4mTZrU7jI6iudaazzXhs5zrTWdfq6N9qBxH4vOQpkCzG/aP2Ux+28byi+Z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\nZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xccae4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "# Pclass_plot = titanic_df['Pclass'].plot.bar()\n",
    "# plt.axhline(0, color='k')\n",
    "\n",
    "# Earlier version\n",
    "# Pclass_1['Pclass'].hist()\n",
    "# Pclass_2['Pclass'].hist()\n",
    "# Pclass_plot = Pclass_3['Pclass'].hist()\n",
    "# Pclass_plot.legend([\"Upper Class\", \"Middle Class\", \"Lower Class\"], loc='best')\n",
    "\n",
    "# Recommended Version\n",
    "titanic_df.groupby('Pclass')['PassengerId'].count().plot(kind='bar')\n",
    "plt.xticks([0,1,2], [\"Upper\", \"Middle\", \"Lower\"], rotation=45)\n",
    "plt.xlabel('Pclass')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Pclass Bar plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This histogram shows that the number of persons that belong to Lower Social and Economic Background boarded the ship in large number compared to the persons from the Upper and middle Social and Economic Background."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Tickets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tickets_split = titanic_df['Ticket'].str.split(' ')\n",
    "#tickets = tickets_split.apply(lambda df: df[len(df) - 1] if (int(df[len(df) - 1]) == False) else int(df[len(df) - 1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['STON/O', '2.', '3101294']"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickets_split[115]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Factors for Survival\n",
    "\n",
    "## Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "577\n",
      "314\n"
     ]
    }
   ],
   "source": [
    "males_data = titanic_df[titanic_df['Sex'] == 'male']\n",
    "females_data = titanic_df[titanic_df['Sex'] == 'female']\n",
    "print len(males_data)\n",
    "print len(females_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def survival_ratio(df):\n",
    "    survived = df[df['Survived'] == 1]\n",
    "    return [len(survived), len(df), (float)(len(survived)) / len(df)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[109, 577, 0.18890814558058924]\n"
     ]
    }
   ],
   "source": [
    "print survival_ratio(males_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[233, 314, 0.7420382165605095]\n"
     ]
    }
   ],
   "source": [
    "print survival_ratio(females_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So a female had a higher chance of surviving than a male."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[342, 891, 0.3838383838383838]\n"
     ]
    }
   ],
   "source": [
    "print survival_ratio(titanic_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a survivor being a male.(Bayesian Probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "male_if_survived =  (survival_ratio(males_data)[2] * (float (577) / 891)) / survival_ratio(titanic_df)[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of a survivor being a female.(Bayesian Probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "female_if_survived = (survival_ratio(females_data)[2] * (float (314) / 891)) / survival_ratio(titanic_df)[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xeab20b8>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SaZIkaYb6ZeRCkiTNE4YLSZJUlOFCkiQVZbiQJElFGS4kSVJRhgtJklSU4UKSJBVluJAk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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcaa41d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex = ['male', 'female']\n",
    "x = range(2)\n",
    "sex_survival_ratio = [survival_ratio(males_data)[2], survival_ratio(females_data)[2]] \n",
    "pylab.xticks(x, sex)\n",
    "\n",
    "sex_plot = plt.bar(x, sex_survival_ratio, color=['b', 'r'], align='center')\n",
    "plt.xlabel('Sex')\n",
    "plt.ylabel('Survival')\n",
    "plt.title('Line Plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows the chance of survial of a Male and a Female in the Titanic mishap Graphically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This analysis shows that among the survivors, there was more than double the chances of a person being a female than the male. This suggests that saving the females was kind of a priority than saving the males or that somehow females were more resilient to the natural conditions on that day than the males.\n",
    "<br/>\n",
    "Since the sample size is not big enough, we can't be sure of the accuracy of these results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "491 184 216\n"
     ]
    }
   ],
   "source": [
    "lower_class = titanic_df[titanic_df['Pclass'] == 3]\n",
    "middle_class = titanic_df[titanic_df['Pclass'] == 2]\n",
    "upper_class = titanic_df[titanic_df['Pclass'] == 1]\n",
    "print len(lower_class), len(middle_class), len(upper_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[119, 491, 0.24236252545824846]\n",
      "[87, 184, 0.47282608695652173]\n",
      "[136, 216, 0.6296296296296297]\n"
     ]
    }
   ],
   "source": [
    "print survival_ratio (lower_class)\n",
    "print survival_ratio (middle_class)\n",
    "print survival_ratio (upper_class)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xecdbba8>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DLq/KXgFc1abuH4AtW8rWBYZ3D9jK5MmTmThx4gjDXHYMDAwwa9as5f59UO/x2Owd/f39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\nuZAkSbUyuZAkSbX6/3rIIVwxFk4kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xeb81978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(3)\n",
    "X_values = ['Lower Class', 'Middle Class', 'Upper Class']\n",
    "Pclass_survival = [survival_ratio (lower_class)[2], survival_ratio (middle_class)[2], survival_ratio (upper_class)[2]]\n",
    "pylab.xticks(x, X_values)\n",
    "plt.bar(x, Pclass_survival, color=['b', 'r', 'g'], align='center')\n",
    "plt.xlabel('Social-Economic Class')\n",
    "plt.ylabel('Survival Ratio')\n",
    "plt.title('Pclass Survival Ratio')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows that your chances of survival increases with the upgradation of your Social and Economic Class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "So it necessarily means that the Socio-Economic Status did play a role in the Survival of the Passengers. From the data available we can infer that :\n",
    "<br/>\n",
    "* A person from Lower Class) had just 24% chance of getting saved or Survival.\n",
    "* A person form a Middle class had a 47% Chance of getting saved or survival.\n",
    "* A Perosn form a Upper Class had a staggering 62% Chance of survival.\n",
    "\n",
    "It shows that a person with more resources, connections and Money had a better chance of survival.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## make a group of titanic database based on Parch \n",
    "## calculate the survival ratio of each group formed.\n",
    "Parch_survival_ratio = titanic_df.groupby('Parch').apply(lambda df: survival_ratio(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parch\n",
       "0    [233, 678, 0.343657817109]\n",
       "1     [65, 118, 0.550847457627]\n",
       "2                 [40, 80, 0.5]\n",
       "3                   [3, 5, 0.6]\n",
       "4                   [0, 4, 0.0]\n",
       "5                   [1, 5, 0.2]\n",
       "6                   [0, 1, 0.0]\n",
       "dtype: object"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Parch_survival_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that having a Parent or child or both aboard on the ship could have increased the chances of survival of that person."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Sibsp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## make a group of titanic database based on Sibsp. \n",
    "## calculate the survival ratio of each group formed.\n",
    "SibSp_survival_ratio = titanic_df.groupby('SibSp').apply(lambda df: survival_ratio(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SibSp\n",
       "0    [210, 608, 0.345394736842]\n",
       "1    [112, 209, 0.535885167464]\n",
       "2      [13, 28, 0.464285714286]\n",
       "3                 [4, 16, 0.25]\n",
       "4       [3, 18, 0.166666666667]\n",
       "5                   [0, 5, 0.0]\n",
       "8                   [0, 7, 0.0]\n",
       "dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SibSp_survival_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "This shows that having a spouse or a sibling along with the person on the ship increased the chance of survival of that person."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Parch and Sibsp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the no of persons belonging to the category of having 2 to 6 Parents or Child aboard on the ship are less. The population from this sample won't resemble a randon sample but a specific set of persons which is not good for statistical analysis. So we won't include the people's from these categories in our statistical analysis anymore."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the he no of persons belonging to the category of having 2 to 8 Spouse or Siblings aboard on the ship are less. The population from this sample won't resemble a randon sample but a specific set of persons which is not good for statistical analysis. So we won't include the people's from these categories in our statistical analysis anymore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "755"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## create a dataFrame that would include the persons that had either no Parch and Sibsp or one Parch and Sibsp.\n",
    "Parch_df = titanic_df[(titanic_df['Parch'].isin([0,1]))]\n",
    "Parch_SibSp = Parch_df[Parch_df['SibSp'].isin([0,1])]\n",
    "len(Parch_SibSp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parch  SibSp\n",
      "0      0        [163, 537, 0.303538175047]\n",
      "       1         [64, 123, 0.520325203252]\n",
      "1      0          [25, 38, 0.657894736842]\n",
      "       1           [34, 57, 0.59649122807]\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "Parch_Sibsp_survival_df = Parch_SibSp.groupby(['Parch', 'SibSp']).apply(lambda df: survival_ratio(df))\n",
    "print Parch_Sibsp_survival_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xee1e908>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SJEk9aiIXLvss8EbgscBvgPcDp2fm4ppjkyRJPWgiLRdvoLRQnDTWS7FLkqT+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\nsQlSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe857630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(4)\n",
    "X_values = ['0 P & 0 Sibsp', '0 Parch & 1 Sibsp', '1 Parch & 0 Sibsp', '1 Parch & 1 Sibsp']\n",
    "Pclass_Sibsp_survival_ratio = [Parch_Sibsp_survival_df[0][0][2], Parch_Sibsp_survival_df[0][1][2], \\\n",
    "                               Parch_Sibsp_survival_df[1][0][2], Parch_Sibsp_survival_df[1][1][2]]\n",
    "pylab.xticks(x, X_values, rotation=45) \n",
    "plt.bar(x, Pclass_Sibsp_survival_ratio, color=['b', 'r', 'g', 'y'], align='center')\n",
    "plt.xlabel('Pclass and Sibsp')\n",
    "plt.ylabel('Survival Ratio')\n",
    "plt.title('Pclass and Sibsp vs Survival')\n",
    "#lt.legend(X_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot clearly shows that the survival chance of an individual increase if he or she has someone (parent, child, spouse or sibling) along with him on the ship. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above information states the survival ratio in four instances.\n",
    "<br/>\n",
    "* Having no Parent, child, Spouse or Sibling. (A)\n",
    "* Having one Parent or child and no Spouse or Sibling. (B)\n",
    "* Having no Parent or child and one Spouse or Sibling. (C)\n",
    "* Having one Parent or child and one Spouse or Sibling. (D)\n",
    "\n",
    "survival ratios:\n",
    "* A = 0.303538175047\n",
    "* B = 0.657894736842\n",
    "* C = 0.520325203252\n",
    "* D = 0.59649122807\n",
    "\n",
    "Comparing the above results we can clearly see that survival ratio for B,C and D is substantially more than that of A.\n",
    "Having a Parent or Child and a Spouse or Sibling substantially increases the chance of Survival in the Titanic mishap."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the significance of Sex and combination of Parch and Sibsp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "516 239\n"
     ]
    }
   ],
   "source": [
    "## create two different df for Males and Females\n",
    "Parch_SibSp_male = Parch_SibSp[Parch_SibSp['Sex'] == 'male']\n",
    "Parch_SibSp_female = Parch_SibSp[Parch_SibSp['Sex'] == 'female']\n",
    "print len(Parch_SibSp_male), len(Parch_SibSp_female)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parch  SibSp\n",
       "0      0        [64, 411, 0.155717761557]\n",
       "       1         [14, 60, 0.233333333333]\n",
       "1      0          [4, 14, 0.285714285714]\n",
       "       1         [14, 31, 0.451612903226]\n",
       "dtype: object"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## shows the survival ratio of Males based on the Parch and Sibsp.\n",
    "Parch_SibSp_male.groupby(['Parch', 'SibSp']).apply(lambda df: survival_ratio(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parch  SibSp\n",
       "0      0        [99, 126, 0.785714285714]\n",
       "       1         [50, 63, 0.793650793651]\n",
       "1      0                  [21, 24, 0.875]\n",
       "       1         [20, 26, 0.769230769231]\n",
       "dtype: object"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## shows the survival ratio of Females based on the Parch and Sibsp.\n",
    "Parch_SibSp_female.groupby(['Parch', 'SibSp']).apply(lambda df: survival_ratio(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The effect of Sex and Parch and Sibsp on the chances of Survival\n",
    "* The above data shows that for a female having a Parent or Child and a Spouse or Sibling didn't affect the chance of survival significantly.\n",
    "<br/>\n",
    "* But for a male the chances of survival increased substantially by just having a Parent or child and a Spouse or Sibling.\n",
    "* By this I can conclude that having a Parent or child and a Spouse or Sibling played it's part in increasing the chances of survival of a Male but not in the case of a Female.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fare and Pclass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xf08dcc0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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KGo93UC6XvQi4vtp2InAQsJKyFqQjfX19tUYd+vv7Oz5monPM1hGPgYGBWdv3brFm9Vi3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\neEiSpMYYPCRJUmMMHpIkqTH/DznbADBbP8hdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xecc57f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pclass1_hist = Pclass_1['Fare'].hist(bins = 25);\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Pclass 1 people\\'s Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    216.000000\n",
       "mean      84.154687\n",
       "std       78.380373\n",
       "min        0.000000\n",
       "25%       30.923950\n",
       "50%       60.287500\n",
       "75%       93.500000\n",
       "max      512.329200\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pclass_1['Fare'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* This histogram shows that among the people belonging to the Upper class 20 to 100 seems to be most common or most choosen fare for the ticket. \n",
    "* It also shows that there were some mega rich persons who were able to pay 200 or 500 price for the ticket of the Titanic Ship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xf5ee3c8>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cxQM3Ufsi8GHKPIp3bubz7Ac8quX3kykX+Xo7sAT4y4gYahlW2Re4dLaNHRkZ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\neZ+YdNGPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf2f1a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pclass_2['Fare'].hist(bins = 15)\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Pclass 2 people\\'s Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    184.000000\n",
       "mean      20.662183\n",
       "std       13.417399\n",
       "min        0.000000\n",
       "25%       13.000000\n",
       "50%       14.250000\n",
       "75%       26.000000\n",
       "max       73.500000\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pclass_2['Fare'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The histogram shows that among the people belonging to the middle class 10 to 15 appears to be the most common choice of fare they were willing to pay for the ride in Titanic.\n",
    "- 20 seems to be the mean price for the population belonging to the Middle class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xf8f9390>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6B9AWQlquZvJeJTdx/xGYRUw9WrNGixcvZsGCBd11cIbKSMxU83Dn3pIlSxgc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\nIpIkqRqDiCRJqub/A9luSDMTA3DyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf0616a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Pclass_3['Fare'].hist(bins = 14)\n",
    "plt.xlabel('Fare')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Pclass 1 people\\'s Fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    491.000000\n",
       "mean      13.675550\n",
       "std       11.778142\n",
       "min        0.000000\n",
       "25%        7.750000\n",
       "50%        8.050000\n",
       "75%       15.500000\n",
       "max       69.550000\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pclass_3['Fare'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The histogram show that among the people belonging to the Lower class 5 to 10 appears to be the most common choice.\n",
    "* 13 seems to the mean fare price for the population belonging to the lower class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This analysis shows that fare is somewhat related to the Social Economic status of the person. It simply means tha a person would pay a fare for the ticket of Titanic that would match his Social Economic Status."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embarkation and Survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "embarkation_s = titanic_df[titanic_df['Embarked'] == 'S']\n",
    "embarkation_q = titanic_df[titanic_df['Embarked'] == 'Q']\n",
    "embarkation_c = titanic_df[titanic_df['Embarked'] == 'C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[219, 646, 0.33900928792569657]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_ratio(embarkation_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[30, 77, 0.38961038961038963]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_ratio(embarkation_q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[93, 168, 0.5535714285714286]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_ratio(embarkation_c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data is pointing to the fact that people embarked from point form 'C' supposedly had a better chance of survival. \n",
    "<br/>\n",
    "There may be a correlation but we can't consider it to be a causation as many lure varaibles are not controlled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64 115 230 155 86 64\n"
     ]
    }
   ],
   "source": [
    "age_0_to_10 = titanic_df[(titanic_df['Age'] <=10) & (titanic_df['Age'] > 0)]\n",
    "age_11_to_20 = titanic_df[(titanic_df['Age'] <= 20) & (titanic_df['Age'] > 10)]\n",
    "age_21_to_30 = titanic_df[(titanic_df['Age'] <= 30) & (titanic_df['Age'] > 20)]\n",
    "age_31_to_40 = titanic_df[(titanic_df['Age'] <= 40) & (titanic_df['Age'] > 30)]\n",
    "age_41_to_50 = titanic_df[(titanic_df['Age'] <= 50) & (titanic_df['Age'] > 40)]\n",
    "age_above_50 = titanic_df[titanic_df['Age'] > 50]\n",
    "\n",
    "print len(age_0_to_10), len(age_11_to_20), len(age_21_to_30), len(age_31_to_40), len(age_41_to_50), len(age_above_50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age is something which can't be ignored when it comes to the factors that could have influenced the survival chance. I have tried to differentiate the population that boarded the ship based on the age interval they belonged to. I have made 6 intervals.\n",
    "* Age 0 to 10 \n",
    "* Age 11 to 20 \n",
    "* Age 21 to 30 \n",
    "* Age 31 to 40 \n",
    "* Age 41 to 50 \n",
    "* Age above 50\n",
    "\n",
    "\n",
    "I have made these intervals to analyze the role of the age in the survival of a person. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[38, 64, 0.59375]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_ratio(age_0_to_10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[44, 115, 0.3826086956521739]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_ratio(age_11_to_20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[84, 230, 0.3652173913043478]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_ratio(age_21_to_30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[69, 155, 0.44516129032258067]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_ratio(age_31_to_40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[33, 86, 0.38372093023255816]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_ratio(age_41_to_50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[22, 64, 0.34375]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival_ratio(age_above_50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xfc17128>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pPYkpW6a4G0ZERCSI5LUPz3DgL96nHmCjMcZ//HNldEvLVe1rtqfdRe1Yv3c9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\nAY+IiIjE3v8Hl0jDgPLEKvMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf080898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## this line of code collects the survival ratio of all these age interval into a 'age_survival_ratio' List\n",
    "age_survival_ratio = [survival_ratio(age_0_to_10)[2], survival_ratio(age_11_to_20)[2], survival_ratio(age_21_to_30)[2],\\\n",
    "                           survival_ratio(age_31_to_40)[2], survival_ratio(age_41_to_50)[2], survival_ratio(age_above_50)[2]]\n",
    "\n",
    "x = range(6)\n",
    "X_values = ['0-10', '11-20', '21-30', '31-40', '41-50', 'Above 50']\n",
    "pylab.xticks(x,X_values)\n",
    "plt.plot(x, age_survival_ratio, 'g')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Survival Ratio')\n",
    "plt.title('Age Survival Ratio')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows some very interesting aspects about the survival of a person and their age. \n",
    "* The survival ratio of childrens from age 0 till 10 years seems to be the highest. This could be dute to the fact that saving the newborn and children of smaller age was kind of given a preference. \n",
    "* The survival ratio of teenagers fells drastically as compared to the very young childrens. \n",
    "* The survival raito of young adults (21 to 30) seems to be the lowest of all.\n",
    "* The survival ratio of adults from 31 to 40 very more likey to be saved than the persons from (21 to 30) or (41 to 50). In my opinion it is due to the fact they had been working for some time now and had saved a lot of money which when combined with the \n",
    "fact they still had a lot of years to live made them more likely to survive.\n",
    "* The survival ratio of persons belonging to (41 to 50) and above 50 continues to fall. It may be due to the fact that even though they might have had a lot of money, their days to live were outnumbered and hence there were less instances of their survival."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before starting my analysis on this data set, I had a hypothesis that \n",
    "* somehow sex and age might have played a significant role in the survival of an individual. \n",
    "* Price of the ticket payed by an individual depended on the Social and economic class of that person.\n",
    "\n",
    "I founded that the survival of a person was likely to be affected by the following factors:\n",
    "* Sex\n",
    "* Age\n",
    "* Embarkation Point\n",
    "* Pclass\n",
    "* Parch and Sibsp\n",
    "\n",
    "All of these factors seem to have some effect on the survival chances of an individual in the Titanic Ship mishap. Even though some factors seem to have enough correlation to be considered as a causation, but we can't be sure of it. There are so many other factors or lure variables that are not controlled and hence the result can't be considered as a causation. \n",
    "<br/>\n",
    "It only means that these factors might have played their hand in the survival chance of an individual but we can't be sure of it.\n",
    "\n",
    "<br/>\n",
    "I also founded that Pclass and fare do have a kind of correlation. The people from upper class were more inclined to pay a heavy price for ticket. People from lower economic background spent less for the ticket.\n",
    "\n",
    "The titanic dataset gives very detailed information about the peoples that boarded the ship. Through the dataset provided I able to figure out the average age of individuals on the ship. The survival of an individual gave us some of the very special insights. I founded out what kind of factors were likey to influence the survival of an individual. I found that in these kind of tragerdies, more importance is given for the survival of the females and children. "
   ]
  }
 ],
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