Practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# how to explore data or perform EDA \n",
    "# Exploratory data analysis\n",
    "# eda 10 important steps\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load dataset\n",
    "df= sns.load_dataset('titanic')\n",
    "df1= sns.load_dataset('tips')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# step 1 data shape\n",
    "df.shape\n",
    "# df = df.sample(1000) # if we have huge data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Rows: 891\n",
      "Number of Columns: 15\n"
     ]
    }
   ],
   "source": [
    "row,col = df.shape\n",
    "print(\"Number of Rows:\", row) # instances\n",
    "print(\"Number of Columns:\", col) # series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   survived     891 non-null    int64   \n",
      " 1   pclass       891 non-null    int64   \n",
      " 2   sex          891 non-null    object  \n",
      " 3   age          714 non-null    float64 \n",
      " 4   sibsp        891 non-null    int64   \n",
      " 5   parch        891 non-null    int64   \n",
      " 6   fare         891 non-null    float64 \n",
      " 7   embarked     889 non-null    object  \n",
      " 8   class        891 non-null    category\n",
      " 9   who          891 non-null    object  \n",
      " 10  adult_male   891 non-null    bool    \n",
      " 11  deck         203 non-null    category\n",
      " 12  embark_town  889 non-null    object  \n",
      " 13  alive        891 non-null    object  \n",
      " 14  alone        891 non-null    bool    \n",
      "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
      "memory usage: 80.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# step 2 Data Structure\n",
    "# structure of table\n",
    "df.info()\n",
    "# info tell us about number of not null values , datatype, indexes, column name\n",
    "# we have to typecast objects cuz we don't get its correlation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age            177\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# step 3 find missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived        0.000000\n",
       "pclass          0.000000\n",
       "sex             0.000000\n",
       "age            19.865320\n",
       "sibsp           0.000000\n",
       "parch           0.000000\n",
       "fare            0.000000\n",
       "embarked        0.224467\n",
       "class           0.000000\n",
       "who             0.000000\n",
       "adult_male      0.000000\n",
       "deck           77.216611\n",
       "embark_town     0.224467\n",
       "alive           0.000000\n",
       "alone           0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# percent calculation of missing values\n",
    "# df.shape[0] --> number of rows\n",
    "df.isnull().sum() / df.shape[0] * 100\n",
    "# to know when to remove rows and when to remove columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>males</th>\n",
       "      <th>females</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lahore, Pakistan</td>\n",
       "      <td>67</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beijing, China</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>berlin, Germany</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            address males females\n",
       "0  Lahore, Pakistan    67     100\n",
       "1    Beijing, China     5       6\n",
       "2   berlin, Germany     8       9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# step 4 : split variables for new columns needed / Feature Engineering\n",
    "# if given then\n",
    "\n",
    "city = pd.DataFrame(np.array([[\"Lahore, Pakistan\", 67, 100], [\"Beijing, China\", 5, 6], [\"berlin, Germany\", 8, 9]]),\n",
    "                    columns=['address', 'males', 'females'])\n",
    "city.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address</th>\n",
       "      <th>males</th>\n",
       "      <th>females</th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lahore, Pakistan</td>\n",
       "      <td>67</td>\n",
       "      <td>100</td>\n",
       "      <td>Lahore</td>\n",
       "      <td>Pakistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beijing, China</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>berlin, Germany</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>berlin</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            address males females     City   Country\n",
       "0  Lahore, Pakistan    67     100   Lahore  Pakistan\n",
       "1    Beijing, China     5       6  Beijing     China\n",
       "2   berlin, Germany     8       9   berlin   Germany"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to split address data\n",
    "city[['City','Country']] = city['address'].str.split(', ',expand=True)\n",
    "city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 5 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   address  3 non-null      object\n",
      " 1   males    3 non-null      object\n",
      " 2   females  3 non-null      object\n",
      " 3   City     3 non-null      object\n",
      " 4   Country  3 non-null      object\n",
      "dtypes: object(5)\n",
      "memory usage: 248.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# type casting / conversion of dtype\n",
    "city.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 5 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   address  3 non-null      object\n",
      " 1   males    3 non-null      int32 \n",
      " 2   females  3 non-null      int32 \n",
      " 3   City     3 non-null      object\n",
      " 4   Country  3 non-null      object\n",
      "dtypes: int32(2), object(3)\n",
      "memory usage: 224.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# to convert into an integer\n",
    "city[['males','females']] = city[['males','females']].astype('int')\n",
    "city.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3 entries, 0 to 2\n",
      "Data columns (total 5 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   address  3 non-null      object\n",
      " 1   males    3 non-null      int32 \n",
      " 2   females  3 non-null      int32 \n",
      " 3   City     3 non-null      object\n",
      " 4   Country  3 non-null      object\n",
      "dtypes: int32(2), object(3)\n",
      "memory usage: 224.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "# to convert into an string\n",
    "city[['City', 'Country']] = city[['City', 'Country']].astype('str')\n",
    "city.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>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>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>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>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>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>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>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>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": [
       "         survived      pclass         age       sibsp       parch        fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# step 6 summary statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.00    30\n",
       "22.00    27\n",
       "18.00    26\n",
       "19.00    25\n",
       "28.00    25\n",
       "         ..\n",
       "36.50     1\n",
       "55.50     1\n",
       "0.92      1\n",
       "23.50     1\n",
       "74.00     1\n",
       "Name: age, Length: 88, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# step 7 value count of a specific column\n",
    "df['age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Third     491\n",
       "First     216\n",
       "Second    184\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['class'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Third', 'First', 'Second']\n",
       "Categories (3, object): ['First', 'Second', 'Third']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding unique values in a column/series\n",
    "df['class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.9500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "5           0       3    male   NaN      0      0   8.4583        Q  Third   \n",
       "16          0       3    male   2.0      4      1  29.1250        Q  Third   \n",
       "22          1       3  female  15.0      0      0   8.0292        Q  Third   \n",
       "28          1       3  female   NaN      0      0   7.8792        Q  Third   \n",
       "32          1       3  female   NaN      0      0   7.7500        Q  Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...    ...   \n",
       "790         0       3    male   NaN      0      0   7.7500        Q  Third   \n",
       "825         0       3    male   NaN      0      0   6.9500        Q  Third   \n",
       "828         1       3    male   NaN      0      0   7.7500        Q  Third   \n",
       "885         0       3  female  39.0      0      5  29.1250        Q  Third   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q  Third   \n",
       "\n",
       "       who  adult_male deck embark_town alive  alone  \n",
       "5      man        True  NaN  Queenstown    no   True  \n",
       "16   child       False  NaN  Queenstown    no  False  \n",
       "22   child       False  NaN  Queenstown   yes   True  \n",
       "28   woman       False  NaN  Queenstown   yes   True  \n",
       "32   woman       False  NaN  Queenstown   yes   True  \n",
       "..     ...         ...  ...         ...   ...    ...  \n",
       "790    man        True  NaN  Queenstown    no   True  \n",
       "825    man        True  NaN  Queenstown    no   True  \n",
       "828    man        True  NaN  Queenstown   yes   True  \n",
       "885  woman       False  NaN  Queenstown    no  False  \n",
       "890    man        True  NaN  Queenstown    no   True  \n",
       "\n",
       "[77 rows x 15 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# step 8 Deal with duplicates (remove) / and/or null values (mean,median and other methods)\n",
    "# this will show the people belong to queenstown\n",
    "df[df.embark_town == 'Queenstown']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# step 9 check the normality / Standard normal distribution\n",
    "\n",
    "sns.histplot(df['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "histplot() got multiple values for argument 'data'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\ANONYM~1\\AppData\\Local\\Temp/ipykernel_1904/30061709.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'age'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'sex'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: histplot() got multiple values for argument 'data'"
     ]
    }
   ],
   "source": [
    "sns.histplot(df[['age','sex']],data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "skew        0.389108\n",
       "kurtosis    0.178274\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# measure its skewness and kurtosis\n",
    "df['age'].agg(['skew','kurtosis']).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anonymous\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(df['age'],color='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>survived</th>\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",
       "      <td>-0.557080</td>\n",
       "      <td>-0.203367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass</th>\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",
       "      <td>0.094035</td>\n",
       "      <td>0.135207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\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",
       "      <td>0.280328</td>\n",
       "      <td>0.198270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sibsp</th>\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",
       "      <td>-0.253586</td>\n",
       "      <td>-0.584471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parch</th>\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",
       "      <td>-0.349943</td>\n",
       "      <td>-0.583398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fare</th>\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",
       "      <td>-0.182024</td>\n",
       "      <td>-0.271832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>adult_male</th>\n",
       "      <td>-0.557080</td>\n",
       "      <td>0.094035</td>\n",
       "      <td>0.280328</td>\n",
       "      <td>-0.253586</td>\n",
       "      <td>-0.349943</td>\n",
       "      <td>-0.182024</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.404744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alone</th>\n",
       "      <td>-0.203367</td>\n",
       "      <td>0.135207</td>\n",
       "      <td>0.198270</td>\n",
       "      <td>-0.584471</td>\n",
       "      <td>-0.583398</td>\n",
       "      <td>-0.271832</td>\n",
       "      <td>0.404744</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            survived    pclass       age     sibsp     parch      fare  \\\n",
       "survived    1.000000 -0.338481 -0.077221 -0.035322  0.081629  0.257307   \n",
       "pclass     -0.338481  1.000000 -0.369226  0.083081  0.018443 -0.549500   \n",
       "age        -0.077221 -0.369226  1.000000 -0.308247 -0.189119  0.096067   \n",
       "sibsp      -0.035322  0.083081 -0.308247  1.000000  0.414838  0.159651   \n",
       "parch       0.081629  0.018443 -0.189119  0.414838  1.000000  0.216225   \n",
       "fare        0.257307 -0.549500  0.096067  0.159651  0.216225  1.000000   \n",
       "adult_male -0.557080  0.094035  0.280328 -0.253586 -0.349943 -0.182024   \n",
       "alone      -0.203367  0.135207  0.198270 -0.584471 -0.583398 -0.271832   \n",
       "\n",
       "            adult_male     alone  \n",
       "survived     -0.557080 -0.203367  \n",
       "pclass        0.094035  0.135207  \n",
       "age           0.280328  0.198270  \n",
       "sibsp        -0.253586 -0.584471  \n",
       "parch        -0.349943 -0.583398  \n",
       "fare         -0.182024 -0.271832  \n",
       "adult_male    1.000000  0.404744  \n",
       "alone         0.404744  1.000000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 10\n",
    "# Correlation\n",
    "#Checking the correlation between variables is also necessary to see potential a feature that we can use for further analysis or building a model later.\n",
    "# We can use a correlation matrix to get this.\n",
    "# drawing correlation\n",
    "corr = df.corr(method=\"pearson\")  # you can use spearman if you want\n",
    "corr\n",
    "# this will display a correlation matrix\n",
    "# relationship between variables\n",
    "\n",
    "# assignment\n",
    "# correlation between male and female\n",
    "# correlation between fare\n",
    "# and different correlation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We can also draw a heatmap of correlation matrix instead of reading number\n",
    "\n",
    "sns.heatmap(corr, annot=True)\n",
    "# this will show the numbers with colors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x13a0c730490>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 72 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we can also draw a pairplot to see the correlation\n",
    "sns.pairplot(corr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "#T_a5082_row0_col0, #T_a5082_row1_col1, #T_a5082_row2_col2, #T_a5082_row3_col3, #T_a5082_row4_col4, #T_a5082_row5_col5, #T_a5082_row6_col6, #T_a5082_row7_col7 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row0_col1, #T_a5082_row4_col6 {\n",
       "  background-color: #6485ec;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row0_col2 {\n",
       "  background-color: #80a3fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row0_col3 {\n",
       "  background-color: #aec9fc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row0_col4, #T_a5082_row1_col3, #T_a5082_row1_col6 {\n",
       "  background-color: #c6d6f1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row0_col5, #T_a5082_row5_col0 {\n",
       "  background-color: #e2dad5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row0_col6, #T_a5082_row1_col2, #T_a5082_row1_col5, #T_a5082_row3_col7, #T_a5082_row4_col7, #T_a5082_row5_col1, #T_a5082_row6_col0, #T_a5082_row7_col3, #T_a5082_row7_col4 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row0_col7, #T_a5082_row5_col6 {\n",
       "  background-color: #89acfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row1_col0 {\n",
       "  background-color: #6687ed;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row1_col4 {\n",
       "  background-color: #bad0f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row1_col7 {\n",
       "  background-color: #d1dae9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row2_col0 {\n",
       "  background-color: #a1c0ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row2_col1 {\n",
       "  background-color: #5e7de7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row2_col3 {\n",
       "  background-color: #7295f4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row2_col4 {\n",
       "  background-color: #8caffe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row2_col5, #T_a5082_row6_col1, #T_a5082_row7_col2 {\n",
       "  background-color: #c5d6f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row2_col6 {\n",
       "  background-color: #e6d7cf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row2_col7, #T_a5082_row4_col5 {\n",
       "  background-color: #dbdcde;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row3_col0 {\n",
       "  background-color: #aac7fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row3_col1 {\n",
       "  background-color: #c3d5f4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row3_col2 {\n",
       "  background-color: #485fd1;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row3_col4, #T_a5082_row4_col3 {\n",
       "  background-color: #f5c2aa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row3_col5 {\n",
       "  background-color: #d2dbe8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row3_col6 {\n",
       "  background-color: #799cf8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row4_col0 {\n",
       "  background-color: #c4d5f3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row4_col1 {\n",
       "  background-color: #b5cdfa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row4_col2 {\n",
       "  background-color: #6384eb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row5_col2 {\n",
       "  background-color: #abc8fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row5_col3 {\n",
       "  background-color: #d5dbe5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row5_col4 {\n",
       "  background-color: #dedcdb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row5_col7 {\n",
       "  background-color: #7a9df8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row6_col2 {\n",
       "  background-color: #d6dce4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row6_col3 {\n",
       "  background-color: #7ea1fa;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row6_col4 {\n",
       "  background-color: #688aef;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row6_col5 {\n",
       "  background-color: #88abfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row6_col7 {\n",
       "  background-color: #f4c5ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row7_col0 {\n",
       "  background-color: #85a8fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row7_col1 {\n",
       "  background-color: #cdd9ec;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a5082_row7_col5 {\n",
       "  background-color: #7396f5;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a5082_row7_col6 {\n",
       "  background-color: #f4c6af;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a5082_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >survived</th>\n",
       "      <th class=\"col_heading level0 col1\" >pclass</th>\n",
       "      <th class=\"col_heading level0 col2\" >age</th>\n",
       "      <th class=\"col_heading level0 col3\" >sibsp</th>\n",
       "      <th class=\"col_heading level0 col4\" >parch</th>\n",
       "      <th class=\"col_heading level0 col5\" >fare</th>\n",
       "      <th class=\"col_heading level0 col6\" >adult_male</th>\n",
       "      <th class=\"col_heading level0 col7\" >alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a5082_level0_row0\" class=\"row_heading level0 row0\" >survived</th>\n",
       "      <td id=\"T_a5082_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_a5082_row0_col1\" class=\"data row0 col1\" >-0.338481</td>\n",
       "      <td id=\"T_a5082_row0_col2\" class=\"data row0 col2\" >-0.077221</td>\n",
       "      <td id=\"T_a5082_row0_col3\" class=\"data row0 col3\" >-0.035322</td>\n",
       "      <td id=\"T_a5082_row0_col4\" class=\"data row0 col4\" >0.081629</td>\n",
       "      <td id=\"T_a5082_row0_col5\" class=\"data row0 col5\" >0.257307</td>\n",
       "      <td id=\"T_a5082_row0_col6\" class=\"data row0 col6\" >-0.557080</td>\n",
       "      <td id=\"T_a5082_row0_col7\" class=\"data row0 col7\" >-0.203367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5082_level0_row1\" class=\"row_heading level0 row1\" >pclass</th>\n",
       "      <td id=\"T_a5082_row1_col0\" class=\"data row1 col0\" >-0.338481</td>\n",
       "      <td id=\"T_a5082_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_a5082_row1_col2\" class=\"data row1 col2\" >-0.369226</td>\n",
       "      <td id=\"T_a5082_row1_col3\" class=\"data row1 col3\" >0.083081</td>\n",
       "      <td id=\"T_a5082_row1_col4\" class=\"data row1 col4\" >0.018443</td>\n",
       "      <td id=\"T_a5082_row1_col5\" class=\"data row1 col5\" >-0.549500</td>\n",
       "      <td id=\"T_a5082_row1_col6\" class=\"data row1 col6\" >0.094035</td>\n",
       "      <td id=\"T_a5082_row1_col7\" class=\"data row1 col7\" >0.135207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5082_level0_row2\" class=\"row_heading level0 row2\" >age</th>\n",
       "      <td id=\"T_a5082_row2_col0\" class=\"data row2 col0\" >-0.077221</td>\n",
       "      <td id=\"T_a5082_row2_col1\" class=\"data row2 col1\" >-0.369226</td>\n",
       "      <td id=\"T_a5082_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_a5082_row2_col3\" class=\"data row2 col3\" >-0.308247</td>\n",
       "      <td id=\"T_a5082_row2_col4\" class=\"data row2 col4\" >-0.189119</td>\n",
       "      <td id=\"T_a5082_row2_col5\" class=\"data row2 col5\" >0.096067</td>\n",
       "      <td id=\"T_a5082_row2_col6\" class=\"data row2 col6\" >0.280328</td>\n",
       "      <td id=\"T_a5082_row2_col7\" class=\"data row2 col7\" >0.198270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5082_level0_row3\" class=\"row_heading level0 row3\" >sibsp</th>\n",
       "      <td id=\"T_a5082_row3_col0\" class=\"data row3 col0\" >-0.035322</td>\n",
       "      <td id=\"T_a5082_row3_col1\" class=\"data row3 col1\" >0.083081</td>\n",
       "      <td id=\"T_a5082_row3_col2\" class=\"data row3 col2\" >-0.308247</td>\n",
       "      <td id=\"T_a5082_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_a5082_row3_col4\" class=\"data row3 col4\" >0.414838</td>\n",
       "      <td id=\"T_a5082_row3_col5\" class=\"data row3 col5\" >0.159651</td>\n",
       "      <td id=\"T_a5082_row3_col6\" class=\"data row3 col6\" >-0.253586</td>\n",
       "      <td id=\"T_a5082_row3_col7\" class=\"data row3 col7\" >-0.584471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5082_level0_row4\" class=\"row_heading level0 row4\" >parch</th>\n",
       "      <td id=\"T_a5082_row4_col0\" class=\"data row4 col0\" >0.081629</td>\n",
       "      <td id=\"T_a5082_row4_col1\" class=\"data row4 col1\" >0.018443</td>\n",
       "      <td id=\"T_a5082_row4_col2\" class=\"data row4 col2\" >-0.189119</td>\n",
       "      <td id=\"T_a5082_row4_col3\" class=\"data row4 col3\" >0.414838</td>\n",
       "      <td id=\"T_a5082_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "      <td id=\"T_a5082_row4_col5\" class=\"data row4 col5\" >0.216225</td>\n",
       "      <td id=\"T_a5082_row4_col6\" class=\"data row4 col6\" >-0.349943</td>\n",
       "      <td id=\"T_a5082_row4_col7\" class=\"data row4 col7\" >-0.583398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5082_level0_row5\" class=\"row_heading level0 row5\" >fare</th>\n",
       "      <td id=\"T_a5082_row5_col0\" class=\"data row5 col0\" >0.257307</td>\n",
       "      <td id=\"T_a5082_row5_col1\" class=\"data row5 col1\" >-0.549500</td>\n",
       "      <td id=\"T_a5082_row5_col2\" class=\"data row5 col2\" >0.096067</td>\n",
       "      <td id=\"T_a5082_row5_col3\" class=\"data row5 col3\" >0.159651</td>\n",
       "      <td id=\"T_a5082_row5_col4\" class=\"data row5 col4\" >0.216225</td>\n",
       "      <td id=\"T_a5082_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "      <td id=\"T_a5082_row5_col6\" class=\"data row5 col6\" >-0.182024</td>\n",
       "      <td id=\"T_a5082_row5_col7\" class=\"data row5 col7\" >-0.271832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5082_level0_row6\" class=\"row_heading level0 row6\" >adult_male</th>\n",
       "      <td id=\"T_a5082_row6_col0\" class=\"data row6 col0\" >-0.557080</td>\n",
       "      <td id=\"T_a5082_row6_col1\" class=\"data row6 col1\" >0.094035</td>\n",
       "      <td id=\"T_a5082_row6_col2\" class=\"data row6 col2\" >0.280328</td>\n",
       "      <td id=\"T_a5082_row6_col3\" class=\"data row6 col3\" >-0.253586</td>\n",
       "      <td id=\"T_a5082_row6_col4\" class=\"data row6 col4\" >-0.349943</td>\n",
       "      <td id=\"T_a5082_row6_col5\" class=\"data row6 col5\" >-0.182024</td>\n",
       "      <td id=\"T_a5082_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "      <td id=\"T_a5082_row6_col7\" class=\"data row6 col7\" >0.404744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a5082_level0_row7\" class=\"row_heading level0 row7\" >alone</th>\n",
       "      <td id=\"T_a5082_row7_col0\" class=\"data row7 col0\" >-0.203367</td>\n",
       "      <td id=\"T_a5082_row7_col1\" class=\"data row7 col1\" >0.135207</td>\n",
       "      <td id=\"T_a5082_row7_col2\" class=\"data row7 col2\" >0.198270</td>\n",
       "      <td id=\"T_a5082_row7_col3\" class=\"data row7 col3\" >-0.584471</td>\n",
       "      <td id=\"T_a5082_row7_col4\" class=\"data row7 col4\" >-0.583398</td>\n",
       "      <td id=\"T_a5082_row7_col5\" class=\"data row7 col5\" >-0.271832</td>\n",
       "      <td id=\"T_a5082_row7_col6\" class=\"data row7 col6\" >0.404744</td>\n",
       "      <td id=\"T_a5082_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x13a0ff0acb0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "e626341f7d273a073ca6760d51f453142864e91a54b48c38f50eacc9d94e2c4c"
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
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   "file_extension": ".py",
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