tatanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# This is Titanic Data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Main objective:\n",
    "The main objective of this study is to investigate what factors made people more likely to survive in the Titanic Data. The data I am using is the \"titanic_data.csv\". The questions to be solved in this project include:\n",
    "1: how the sex influence the survive rate?\n",
    "2: how the age influence the survive rate?\n",
    "3: how the pclass influence the survive rate?"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "1:import the module which is needed in this study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "2: import the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tatanic_data = pd.read_csv(\"titanic-data.csv\")\n",
    "Tatanic_data.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "3. The age distribution in male and famale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Age                                                    \n",
      "        count       mean        std   min   25%   50%   75%   max\n",
      "Sex                                                              \n",
      "female  261.0  27.915709  14.110146  0.75  18.0  27.0  37.0  63.0\n",
      "male    453.0  30.726645  14.678201  0.42  21.0  29.0  39.0  80.0\n"
     ]
    },
    {
     "data": {
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sYQAg6WhgJlnz1L0R0dfsUGZmVj619GHsA9wAdJDdQAngnZJWAn8REU+NuLCZmW3zajlK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\nmVkSFwwzM0vy/wFXijOdA902bAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc9fd390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sl/oi2WRHRsSDVQdhZma95wYCZmZWuH5JNgF8U9IqSadUHYzZoJF0iaQtktbU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\nK9z/B4haZ6hz/wd6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc1140b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Tatanic_data_clean =  Tatanic_data.loc[:, ('Sex', 'Age')].dropna()\n",
    "print Tatanic_data_clean.groupby('Sex').describe()\n",
    "plt.figure(1)\n",
    "Tatanic_data_clean['Age'].hist(bins = 15, color='green')\n",
    "plt.xlabel('Age',fontsize=14)\n",
    "plt.ylabel('Count',fontsize=14)\n",
    "plt.title('Age distribution',fontsize=14)\n",
    "plt.figure(2)\n",
    "plt.subplot(121)\n",
    "Tatanic_data_clean.loc[Tatanic_data_clean['Sex'] == 'female','Age'].hist(bins = 15, color='red')\n",
    "plt.xlabel('Age',fontsize=14)\n",
    "plt.ylabel('Count',fontsize=14)\n",
    "plt.title('Age distribution of female',fontsize=14)\n",
    "plt.subplot(122)\n",
    "Tatanic_data_clean.loc[Tatanic_data_clean['Sex'] == 'male','Age'].hist(bins = 15, color='blue')\n",
    "plt.xlabel('Age',fontsize=14)\n",
    "plt.ylabel('Count',fontsize=14)\n",
    "plt.title('Age distribution of male',fontsize=14)\n",
    "plt.subplots_adjust(\n",
    "                wspace=0.8, hspace=None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "From the the histogram, we can decide that male number is twice than female. The mean age of famale is around 27.92, the mean age of male is around 30.73. "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "4. How sex influence the survive rate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</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>female</th>\n",
       "      <td>314.0</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>0.438211</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>577.0</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>0.391775</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived                                             \n",
       "          count      mean       std  min  25%  50%  75%  max\n",
       "Sex                                                         \n",
       "female    314.0  0.742038  0.438211  0.0  0.0  1.0  1.0  1.0\n",
       "male      577.0  0.188908  0.391775  0.0  0.0  0.0  0.0  1.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tatanic_sex_data = Tatanic_data.loc[:, ('Survived', 'Sex')]\n",
    "Tatanic_sex_data_clean =  Tatanic_sex_data.dropna()\n",
    "male_Tatanic_sex_data_clean = Tatanic_sex_data_clean.loc[Tatanic_sex_data_clean['Sex'] =='male', ('Survived', 'Sex')]\n",
    "female_Tatanic_sex_data_clean = Tatanic_sex_data_clean.loc[Tatanic_sex_data_clean['Sex'] == 'female',('Survived', 'Sex')]\n",
    "Tatanic_sex_data_clean.groupby('Sex').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9hs4dhrrt6IS9rGKA/RNe37n9dtq9fqH3+hXdxJt2OcJ29Pmadp2O2m33P95n/Ap7TbvP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\nPyVPPahl0G3NP9VJxjt7n0h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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc518f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(121)\n",
    "male_Tatanic_sex_data_clean.groupby('Survived')['Sex'].count().plot.pie(labels=('died','survived'),autopct='%1.1f%%',fontsize=14)\n",
    "plt.title('Male survived/died ratio',fontsize=14)\n",
    "plt.subplot(122)\n",
    "female_Tatanic_sex_data_clean.groupby('Survived')['Sex'].count().plot.pie(labels=('died','survived'),autopct='%1.1f%%',\n",
    "                                                                          fontsize=14)\n",
    "plt.title('Female survived/died ratio',fontsize=14)\n",
    "plt.subplots_adjust(left=1, bottom=None, right=2, top=None,\n",
    "                wspace=None, hspace=None)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this we can know that the male survive mean is 0.1889, which is much lower than the famale survive mean, which is 0.7420. So most man is gentle man. "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "5. How age influence the survive rate? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Survived</th>\n",
       "      <th colspan=\"8\" halign=\"left\">Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</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",
       "      <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>(0, 18]</th>\n",
       "      <td>139.0</td>\n",
       "      <td>0.503597</td>\n",
       "      <td>0.501795</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>10.717050</td>\n",
       "      <td>6.465828</td>\n",
       "      <td>0.42</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 60]</th>\n",
       "      <td>553.0</td>\n",
       "      <td>0.388788</td>\n",
       "      <td>0.487916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>33.025316</td>\n",
       "      <td>10.332133</td>\n",
       "      <td>19.00</td>\n",
       "      <td>24.5</td>\n",
       "      <td>31.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60, 100]</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.428932</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>66.022727</td>\n",
       "      <td>5.024884</td>\n",
       "      <td>61.00</td>\n",
       "      <td>62.0</td>\n",
       "      <td>64.5</td>\n",
       "      <td>70.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Survived                                                 Age  \\\n",
       "             count      mean       std  min  25%  50%  75%  max  count   \n",
       "Age                                                                      \n",
       "(0, 18]      139.0  0.503597  0.501795  0.0  0.0  1.0  1.0  1.0  139.0   \n",
       "(18, 60]     553.0  0.388788  0.487916  0.0  0.0  0.0  1.0  1.0  553.0   \n",
       "(60, 100]     22.0  0.227273  0.428932  0.0  0.0  0.0  0.0  1.0   22.0   \n",
       "\n",
       "                                                                \n",
       "                mean        std    min   25%   50%   75%   max  \n",
       "Age                                                             \n",
       "(0, 18]    10.717050   6.465828   0.42   4.0  13.0  17.0  18.0  \n",
       "(18, 60]   33.025316  10.332133  19.00  24.5  31.0  40.0  60.0  \n",
       "(60, 100]  66.022727   5.024884  61.00  62.0  64.5  70.0  80.0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tatanic_age_data = Tatanic_data.loc[:, ('Survived', 'Age')]\n",
    "Tatanic_age_data_clean =  Tatanic_age_data.dropna()\n",
    "bins = np.array([0, 18,  60, 100])\n",
    "groups = Tatanic_age_data_clean.groupby((pd.cut(Tatanic_age_data_clean['Age'], bins)))\n",
    "groups.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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stNNOdOvmRcPMuiav3axDevbsyfDhwxk1ahQPPPAAxx57LKNHj+aDDz6gW7du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\nTvxmZmYNxInfzMysgfx/D9SB6w7X3cMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc507518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "groups['Survived'].count().plot.bar(grid=True, color = 'blue')\n",
    "groups['Survived'].sum().plot.bar(grid=True,color = 'red')\n",
    "plt.xlabel('Age group',fontsize=14)\n",
    "plt.ylabel('number of people',fontsize=14)\n",
    "plt.xticks(fontsize=14) \n",
    "plt.yticks(fontsize=14)\n",
    "plt.title('Number of total/survived people in different age group',fontsize=18)\n",
    "plt.legend(('Total','Survived'),fontsize=14,loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this we can know that the children survive rate is around 0.50, which is much higher than adult survive rate. But tht old people survive rate is the smallest. "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "6. How the class influence the survive rate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</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>1</th>\n",
       "      <td>216.0</td>\n",
       "      <td>0.629630</td>\n",
       "      <td>0.484026</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>184.0</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.500623</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>491.0</td>\n",
       "      <td>0.242363</td>\n",
       "      <td>0.428949</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived                                             \n",
       "          count      mean       std  min  25%  50%  75%  max\n",
       "Pclass                                                      \n",
       "1         216.0  0.629630  0.484026  0.0  0.0  1.0  1.0  1.0\n",
       "2         184.0  0.472826  0.500623  0.0  0.0  0.0  1.0  1.0\n",
       "3         491.0  0.242363  0.428949  0.0  0.0  0.0  0.0  1.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Tatanic_age_data = Tatanic_data.loc[:, ('Survived', 'Pclass')]\n",
    "Tatanic_age_data_clean =  Tatanic_age_data.dropna()\n",
    "Tatanic_age_data_clean.groupby('Pclass').describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this study, it shows that the survive rate in the first class is highest, while the third class is the lowest.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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z9957M3Xq1Ar3Rayp9IYbbqBVq1Y0btyYs846i7Vr1xbHcXdGjRrFHnvsQYMG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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd4da128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "Tatanic_age_data_clean.loc[:, 'Pclass'].hist(bins = 15, color='blue')\n",
    "Tatanic_age_data_clean.loc[Tatanic_age_data_clean['Survived'] == 1, 'Pclass'].hist(bins = 15, color='red')\n",
    "plt.xlabel('pclass',fontsize=14)\n",
    "plt.ylabel('number of people',fontsize=14)\n",
    "plt.legend(('Total number','Survived people number'),fontsize=14)\n",
    "plt.title('Number of total/survived people in different class',fontsize=18)\n",
    "plt.xticks([1,2,3], ('class1', 'class2', 'class3'),fontsize=14) \n",
    "plt.yticks(fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "From this study, it shows that most people in class 1 survive, almost half of people in class 2 survive. However, less than \n",
    "quarter of people in class 3 survive. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Whether the age has a strong correlation with the class?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.369226015316\n"
     ]
    },
    {
     "data": {
      "image/png": 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F9Ety+qcr0ktdic+vkect8v5cSCo5PU46OZwD7DOc/FK/O+9Gx0qd7/MwUrBaSQoO/0MK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\nL+ksSffndd0n6YtDbMNROW9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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc9eed30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Tatanic_age_class_data_clean =  Tatanic_data.loc[:, ('Pclass', 'Age')].dropna()\n",
    "class_data = Tatanic_age_class_data_clean['Pclass']\n",
    "age_data = Tatanic_age_class_data_clean['Age']\n",
    "def correlation(x, y):\n",
    "    std_x = (x - x.mean())/x.std(ddof = 0)\n",
    "    std_y = (y - y.mean())/y.std(ddof = 0)\n",
    "    return (std_x * std_y).mean()\n",
    "print correlation(class_data, age_data)\n",
    "def stan(column):\n",
    "    return (column - column.mean())/column.std(ddof = 0)\n",
    "df_clean = Tatanic_age_class_data_clean.apply(stan)\n",
    "class_data = df_clean['Pclass']\n",
    "age_data = df_clean['Age']\n",
    "plt.xlabel('pclass distribution',fontsize=14)\n",
    "plt.ylabel('Age distribution',fontsize=14)\n",
    "plt.legend(('Total number','Survived people number'),fontsize=14)\n",
    "plt.title('Correlations between age with class',fontsize=18)\n",
    "plt.xticks(fontsize=14) \n",
    "plt.scatter(x=class_data, y=age_data)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the p value, we know that the age has a slight negative correlation with the class. So we can think that age and class are two independent variables. From Wikipedia (https://en.wikipedia.org/wiki/RMS_Titanic#Passengers), we know that the total passengers is around 1300. There has around 800 passengers in our data base. So the sample size is close to the population size. That is why I calculate the population standard deviation with \"ddof = 0\" in my code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Conclusion:\n",
    "From the analysis, the sex, age and class all influence the survive rate. The sex and class have a significant effect on the survive rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}