00_Kaggle_Titanic_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np   \n",
    "from pandas import Categorical \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "pd.set_option('display.max_columns', None)  \n",
    "pd.set_option('display.expand_frame_repr', False)\n",
    "pd.set_option('max_colwidth', None)\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# set up notebook to display multiple output in one cell\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "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",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>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       Parch        Fare\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "<div>\n",
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       "<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 Thayer)</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                                                 Name     Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked\n",
       "0            1         0       3                              Braund, Mr. Owen Harris    male  22.0      1      0         A/5 21171   7.2500   NaN        S\n",
       "1            2         1       1  Cumings, Mrs. John Bradley (Florence Briggs Thayer)  female  38.0      1      0          PC 17599  71.2833   C85        C\n",
       "2            3         1       3                               Heikkinen, Miss. Laina  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S\n",
       "3            4         1       1         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1      0            113803  53.1000  C123        S\n",
       "4            5         0       3                             Allen, Mr. William Henry    male  35.0      0      0            373450   8.0500   NaN        S"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv('train.csv')\n",
    "\n",
    "# check out the variables\n",
    "data.info()\n",
    "\n",
    "#basic descriptive statistics\n",
    "data.describe()\n",
    "\n",
    "#head()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 2])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([22.  , 38.  , 26.  , 35.  ,   nan, 54.  ,  2.  , 27.  , 14.  ,\n",
       "        4.  , 58.  , 20.  , 39.  , 55.  , 31.  , 34.  , 15.  , 28.  ,\n",
       "        8.  , 19.  , 40.  , 66.  , 42.  , 21.  , 18.  ,  3.  ,  7.  ,\n",
       "       49.  , 29.  , 65.  , 28.5 ,  5.  , 11.  , 45.  , 17.  , 32.  ,\n",
       "       16.  , 25.  ,  0.83, 30.  , 33.  , 23.  , 24.  , 46.  , 59.  ,\n",
       "       71.  , 37.  , 47.  , 14.5 , 70.5 , 32.5 , 12.  ,  9.  , 36.5 ,\n",
       "       51.  , 55.5 , 40.5 , 44.  ,  1.  , 61.  , 56.  , 50.  , 36.  ,\n",
       "       45.5 , 20.5 , 62.  , 41.  , 52.  , 63.  , 23.5 ,  0.92, 43.  ,\n",
       "       60.  , 10.  , 64.  , 13.  , 48.  ,  0.75, 53.  , 57.  , 80.  ,\n",
       "       70.  , 24.5 ,  6.  ,  0.67, 30.5 ,  0.42, 34.5 , 74.  ])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#List unique values data.columnname.unique()\n",
    "\n",
    "data.Pclass.unique()\n",
    "\n",
    "data.Survived.unique()\n",
    "\n",
    "data.Age.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x125259130>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot to visualize age of all passengers\n",
    "data.boxplot(column='Age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#series to dataframe\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    216\n",
       "2    184\n",
       "3    491\n",
       "Name: PassengerId, dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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",
       "    <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",
       "      <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</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>216</td>\n",
       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>147</td>\n",
       "      <td>94</td>\n",
       "      <td>133</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>184</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>184</td>\n",
       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>140</td>\n",
       "      <td>42</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>491</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>491</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>394</td>\n",
       "      <td>119</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId  Survived  Pclass  Name  Sex  Age  SibSp  Parch  Ticket  Fare  Cabin  Embarked\n",
       "Pclass                                                                                            \n",
       "1               216         2       1   216    2   57      4      4     147    94    133         3\n",
       "2               184         2       1   184    2   57      4      4     140    42      7         3\n",
       "3               491         2       1   491    2   68      7      7     394   119      7         3"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See number of passengers per Pclass\n",
    "#More passengers in Pclass 3\n",
    "df2 = df.groupby(['Pclass']).nunique()\n",
    "df2['PassengerId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    38.233441\n",
       "2    29.877630\n",
       "3    25.140620\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Average age is lower in Pclass 3\n",
    "df3 = df.groupby(['Pclass']).mean()\n",
    "df3['Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Grouped Stripplot\n",
    "#Pclass may have a strong influence on survivability\n",
    "\n",
    "bplot=sns.stripplot(y='Age', x='Pclass',\n",
    "                   data = df,\n",
    "                   jitter = True,\n",
    "                   marker = 'o',\n",
    "                   alpha = 0.5,\n",
    "                   hue = 'Survived')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch      Fare\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652  0.012658\n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629  0.257307\n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443 -0.549500\n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119  0.096067\n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838  0.159651\n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000  0.216225\n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225  1.000000"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x13746e280>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1374bb280>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1374c66a0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1374f2a00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13751fe20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1375591f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1375592e0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x137584790>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1375e1fa0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137619430>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137644880>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137671cd0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1376a4a00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1376d81c0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x137704940>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13772e160>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137763880>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13778c0a0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1377c17c0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1377ecf40>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137821700>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x13784ce80>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137881670>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1378abdf0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1378e45b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13790cd30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1379404f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x13796ac70>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1379a0430>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1379c8bb0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1379ff370>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137a27af0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137a5f2b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137a86a30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137abd1f0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x137ae7970>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137b1d130>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137b458b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137b700d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137ba57f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137bd1f70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137c05730>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x137c32eb0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137c66670>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137c8edf0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137cc45b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137cedd30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137d234f0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x137d4cc70>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 49 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatter_matrix \n",
    "pandas.plotting.scatter_matrix(df, marker='+')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.3 64-bit",
   "language": "python",
   "name": "python38364bit28c7afb0fc794b32845c6356447d60eb"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}