1 EDA and Data Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA) and Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A wonderful guide to exploratory data analysis and data visualization for every dataset.\n",
    "\n",
    "__Steps for EDA__:\n",
    "\n",
    "- GENERAL / GENERAL OVERVIEW / GENERAL PICTURE\n",
    "    - shape, info(), columns, index, describe(), isnull().any, isnull().sum()\n",
    "    \n",
    "    \n",
    "- CATEGORICAL VARIABLE ANALYSIS\n",
    "    - Catching categorical variables\n",
    "    - Number of categorical variables\n",
    "    - Classes\n",
    "    - Frequencies\n",
    "    - Basic visualization --> barplot, countplot \n",
    "    \n",
    "\n",
    "- NUMERICAL VARIABLE ANALYSIS\n",
    "    - Catching numerical variables\n",
    "    - describe with quantiles to see whether there are extraordinary values or not\n",
    "    - Basic visualization --> histogram, boxplot and density\n",
    "    \n",
    "    \n",
    "- TARGET ANALYSIS\n",
    "    - Target analysis according to categorical variables --> target_summary_with_cats()\n",
    "    - target analysis according to numerical variables --> target_summary_with_nums()\n",
    "    \n",
    "    \n",
    "- ANALYSIS OF NUMERCIAL VARIABLES IN COMPARISON WITH EACH OTHER\n",
    "    - scatterplot\n",
    "    - lmplot\n",
    "    - correlation\n",
    "    \n",
    "\n",
    "__Dataset__: titanic and tips datasets\n",
    "\n",
    "- https://www.kaggle.com/hesh97/titanicdataset-traincsv\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. GENERAL / GENERAL OVERVIEW / GENERAL PICTURE\n",
    "# 2. CATEGORICAL VARIABLE ANALYSIS\n",
    "# 3. NUMERICAL VARIABLE ANALYSIS\n",
    "# 4. TARGET ANALYSIS\n",
    "# 5. ANALYSIS OF NUMERCIAL VARIABLES IN COMPARISON WITH EACH OTHER (SAYISAL DEGISKENLERIN BIRBIRLERINE GORE ANALIZI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/yakup/OneDrive/Masaüstü/DSMLBC/datasets/train.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. GENERAL / GENERAL OVERVIEW / GENERAL PICTURE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "      <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",
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       "      <th>2</th>\n",
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       "      <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",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
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       "      <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",
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      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>211536</td>\n",
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       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
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       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=891, step=1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>891.0</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>257.353842</td>\n",
       "      <td>1.00</td>\n",
       "      <td>223.5000</td>\n",
       "      <td>446.0000</td>\n",
       "      <td>668.5</td>\n",
       "      <td>891.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>891.0</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>714.0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>0.42</td>\n",
       "      <td>20.1250</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>38.0</td>\n",
       "      <td>80.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>891.0</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.9104</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>31.0</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count        mean         std   min       25%       50%    75%  \\\n",
       "PassengerId  891.0  446.000000  257.353842  1.00  223.5000  446.0000  668.5   \n",
       "Survived     891.0    0.383838    0.486592  0.00    0.0000    0.0000    1.0   \n",
       "Pclass       891.0    2.308642    0.836071  1.00    2.0000    3.0000    3.0   \n",
       "Age          714.0   29.699118   14.526497  0.42   20.1250   28.0000   38.0   \n",
       "SibSp        891.0    0.523008    1.102743  0.00    0.0000    0.0000    1.0   \n",
       "Parch        891.0    0.381594    0.806057  0.00    0.0000    0.0000    0.0   \n",
       "Fare         891.0   32.204208   49.693429  0.00    7.9104   14.4542   31.0   \n",
       "\n",
       "                  max  \n",
       "PassengerId  891.0000  \n",
       "Survived       1.0000  \n",
       "Pclass         3.0000  \n",
       "Age           80.0000  \n",
       "SibSp          8.0000  \n",
       "Parch          6.0000  \n",
       "Fare         512.3292  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. CATEGORICAL VARIABLE ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\yakup\\\\OneDrive\\\\Masaüstü\\\\DSMLBC\\\\Codes'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of categorical variables\n",
    "# Classes\n",
    "# Frequencies\n",
    "# Basic visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorical Classes --> Names?\n",
    "df[\"Survived\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of categorical classes\n",
    "len(df[\"Survived\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Survived\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Classes and frequencies?\n",
    "df[\"Survived\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's catch categorical variables\n",
    "cat_cols = [col for col in df.columns if df[col].dtype == \"O\"]\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Categorical Variables :  5\n"
     ]
    }
   ],
   "source": [
    "print('Number of Categorical Variables : ', len(cat_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Survived', 'Pclass', 'Sex', 'SibSp', 'Parch', 'Embarked']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We also want ot catch secret categorical varibales. They seem to be numerical,but in fact they are categrorical. \n",
    "#That means, in fact these are categorical, because they do not have many unique values, but a few and repeated.\n",
    "# Let's bring the ones with less than 10 unique values. You may choose another number, too. For this dataset we chose '10'\n",
    "cat_cols = [col for col in df.columns if len(df[col].unique()) < 10]\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Categorical Variables:  6\n"
     ]
    }
   ],
   "source": [
    "print('Number of Categorical Variables: ', len(cat_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    2\n",
       "Pclass      3\n",
       "Sex         2\n",
       "SibSp       7\n",
       "Parch       7\n",
       "Embarked    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numbers of unique classes for each cat_cols\n",
    "df[cat_cols].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic visualization rules\n",
    "# categorical variables --> barplot, countplot \n",
    "# numerical variables --> histogram, boxplot and density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# barplot\n",
    "sns.countplot(x=\"Sex\", data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the unique values in 'Sex'\n",
    "df[\"Sex\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      64.758698\n",
       "female    35.241302\n",
       "Name: Sex, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ratio\n",
    "100 * df[\"Sex\"].value_counts() / len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function that catches categorical variables, prints the distribution and ratio of unique values and finally creates a countplot \n",
    "def cats_summary1(data):\n",
    "    cat_names = [col for col in data.columns if len(data[col].unique()) < 10]\n",
    "    for col in cat_names:\n",
    "        print(pd.DataFrame({col: data[col].value_counts(),\n",
    "                           \"Ratio\": 100 * data[col].value_counts()/ len (data)}), end = \"\\n\\n\\n\")\n",
    "        sns.countplot(x = col, data = data)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Survived      Ratio\n",
      "0       549  61.616162\n",
      "1       342  38.383838\n",
      "\n",
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pclass      Ratio\n",
      "3     491  55.106622\n",
      "1     216  24.242424\n",
      "2     184  20.650954\n",
      "\n",
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Sex      Ratio\n",
      "male    577  64.758698\n",
      "female  314  35.241302\n",
      "\n",
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   SibSp      Ratio\n",
      "0    608  68.237935\n",
      "1    209  23.456790\n",
      "2     28   3.142536\n",
      "4     18   2.020202\n",
      "3     16   1.795735\n",
      "8      7   0.785634\n",
      "5      5   0.561167\n",
      "\n",
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Parch      Ratio\n",
      "0    678  76.094276\n",
      "1    118  13.243547\n",
      "2     80   8.978676\n",
      "5      5   0.561167\n",
      "3      5   0.561167\n",
      "4      4   0.448934\n",
      "6      1   0.112233\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Q5gTPFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktQyFCRJLUNBktS64EIhyZokTyc5kOSecdcjSZeSCyoUkswD3gu8EVgJvDXJyvFWJUmXjvnjLuA0q4ADVfVFgCRbgbXAvrFWpUvGp17/I+MuYaAf+fSnxl1C5zbd8TPjLuGsfvUDD4+7hJG50EJhCXCob3sK+MEx1SLNKf/tF/903CWc1S/89pvHXULn9m/6xLhLGOj7fvWWGfVPVXVUyswluR24tar+bbP9NmBVVd3V12cDsKHZvB54usOSrgG+3OHxu2b942X94zOXa4fu6/+nVTUx6IML7UxhCljWt70UONzfoao2A5tHUUyS3VU1OYrv6oL1j5f1j89crh3GW/8FdaEZ+CywIsl1Sa4A1gHbx1yTJF0yLqgzhao6keQXgD8H5gHvq6q9Yy5Lki4ZF1QoAFTVnwF/Nu46GiMZpuqQ9Y+X9Y/PXK4dxlj/BXWhWZI0XhfaNQVJ0hgZCmcxl6fbSPK+JMeSPDnuWmYqybIkf5lkf5K9Se4ed00zkeQlSXYl+UJT/2+Mu6bZSDIvyeeTfHTctcxUkmeTPJHk8SS7x13PTCV5RZKHkzzV/HfwQyP9foePztRMt/G/gB+nd5vsZ4G3VtWceLI6yeuBbwAPVdX3j7uemUiyGFhcVZ9L8nJgD3DbHPpnH+CqqvpGksuBvwLurqr/MebSZiTJfwQmgaur6k3jrmcmkjwLTFbVnHxOIckW4DNV9UBzF+bLquqro/p+zxQGa6fbqKpvA6em25gTqurTwFfGXcdsVNWRqvpcs/51YD+9J93nhOr5RrN5efMzp/7ySrIU+EnggXHXcqlJcjXweuBBgKr69igDAQyFsxk03cac+R/TxSLJcuAm4LHxVjIzzdDL48AxYEdVzan6gd8Bfgn4h3EXMksFfDzJnmYGhLnk1cA08P5m+O6BJFeNsgBDYbAMaJtTf+3NdUm+B/gI8I6q+tq465mJqjpZVTfSeyJ/VZI5M4SX5E3AsaraM+5aXoSbq+q19GZb3tgMp84V84HXAr9XVTcB3wRGek3TUBjsBafbUHeasfiPAB+sqj8edz2z1Zz2fxJYM+ZSZuJm4C3NuPxW4JYkHxhvSTNTVYeb5THgEXrDwXPFFDDVd3b5ML2QGBlDYTCn2xiT5kLtg8D+qnrXuOuZqSQTSV7RrL8U+DHgqfFWNbyqureqllbVcnr/3n+iqu4Yc1lDS3JVc4MCzbDLTwBz5i68qvoScCjJ9U3Takb86oAL7onmC8Fcn24jyYeANwDXJJkCfr2qHhxvVUO7GXgb8EQzLg/wK82T7nPBYmBLcwfbZcC2qppzt3XOYYuAR3p/WzAf+MOq+th4S5qxu4APNn+QfhF4+yi/3FtSJUkth48kSS1DQZLUMhQkSS1DQZLUMhQkSS1DQRpCkpPNrJtPJvlwkpe9yOMtn4uz2OriZyhIw/lWVd3YzDr7beDfDbNTEp8F0pxiKEgz9xngnyd5c5LHmonL/iLJIoAk70yyOcnHgYeSLErySPOOhS8k+eHmOPOS/PfmvQsfb56AlsbKUJBmoPnL/43AE/TelfC6ZuKyrfRmFj3lB4C1VfWvgd8FPlVVr6E3j82pp+NXAO+tqhuArwI/PZrfQjo7T22l4by0b9qNz9Cbn+l64I+aFwNdARzs67+9qr7VrN8C/Bz0ZlAF/m+SBcDBqjp1zD3A8m5/BemFGQrScL7VTIfdSvIe4F1VtT3JG4B39n38zSGOebxv/STg8JHGzuEjafb+EfB3zfr6c/TbCfx7aF/Ac3XXhUmzZShIs/dO4MNJPgOc633AdwM/muQJesNEN4ygNmlWnCVVktTyTEGS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEktQ0GS1DIUJEmt/w9sc3mQ0A/T2wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Embarked      Ratio\n",
      "S       644  72.278339\n",
      "C       168  18.855219\n",
      "Q        77   8.641975\n",
      "\n",
      "\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": [
    "cats_summary1(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Survived', 'Pclass', 'Sex', 'SibSp', 'Parch', 'Embarked']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's ignore secret cat_cols for now\n",
    "cat_cols = [col for col in df.columns if df[col].dtype == \"O\"]\n",
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function takes dataframe, categorical columns and if required number of classes (default value = 10).\n",
    "# Then, it prints the distribution and ratio of unique values for the variables, which have less than 10 (number of classes) unique values.\n",
    "# Afterwards, it reports categorical variables it described, how many numerical variables, but seem categorical we have and finally report these variables. \n",
    "\n",
    "def cats_summary2(data, categorical_cols, number_of_classes=10):\n",
    "    \n",
    "    var_count = 0  # reporting how many categorical variables are there?\n",
    "    vars_more_classes = []  # save the variables that have classes more than a number that we determined \n",
    "    \n",
    "    for var in data:\n",
    "        if var in categorical_cols:\n",
    "            if len(list(data[var].unique())) <= number_of_classes:  # select according to number of classes\n",
    "                print(pd.DataFrame({var: data[var].value_counts(),\n",
    "                                    \"Ratio\": 100 * data[var].value_counts() / len(data)}),end=\"\\n\\n\\n\")\n",
    "                var_count += 1\n",
    "            else:\n",
    "                vars_more_classes.append(data[var].name)\n",
    "    print('%d categorical variables have been described' % var_count, end=\"\\n\\n\")\n",
    "    print('There are', len(vars_more_classes), \"variables have more than\", number_of_classes, \"classes\", end=\"\\n\\n\")\n",
    "    print('Variable names have more than %d classes:' % number_of_classes, end=\"\\n\\n\")\n",
    "    print(vars_more_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Sex      Ratio\n",
      "male    577  64.758698\n",
      "female  314  35.241302\n",
      "\n",
      "\n",
      "   Embarked      Ratio\n",
      "S       644  72.278339\n",
      "C       168  18.855219\n",
      "Q        77   8.641975\n",
      "\n",
      "\n",
      "2 categorical variables have been described\n",
      "\n",
      "There are 3 variables have more than 10 classes\n",
      "\n",
      "Variable names have more than 10 classes:\n",
      "\n",
      "['Name', 'Ticket', 'Cabin']\n"
     ]
    }
   ],
   "source": [
    "cats_summary2(df, cat_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. NUMERICAL VARIABLES ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>891.0</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>257.353842</td>\n",
       "      <td>1.00</td>\n",
       "      <td>223.5000</td>\n",
       "      <td>446.0000</td>\n",
       "      <td>668.5</td>\n",
       "      <td>891.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>891.0</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>714.0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>0.42</td>\n",
       "      <td>20.1250</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>38.0</td>\n",
       "      <td>80.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>891.0</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.9104</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>31.0</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count        mean         std   min       25%       50%    75%  \\\n",
       "PassengerId  891.0  446.000000  257.353842  1.00  223.5000  446.0000  668.5   \n",
       "Survived     891.0    0.383838    0.486592  0.00    0.0000    0.0000    1.0   \n",
       "Pclass       891.0    2.308642    0.836071  1.00    2.0000    3.0000    3.0   \n",
       "Age          714.0   29.699118   14.526497  0.42   20.1250   28.0000   38.0   \n",
       "SibSp        891.0    0.523008    1.102743  0.00    0.0000    0.0000    1.0   \n",
       "Parch        891.0    0.381594    0.806057  0.00    0.0000    0.0000    0.0   \n",
       "Fare         891.0   32.204208   49.693429  0.00    7.9104   14.4542   31.0   \n",
       "\n",
       "                  max  \n",
       "PassengerId  891.0000  \n",
       "Survived       1.0000  \n",
       "Pclass         3.0000  \n",
       "Age           80.0000  \n",
       "SibSp          8.0000  \n",
       "Parch          6.0000  \n",
       "Fare         512.3292  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remember the dataset\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>5%</th>\n",
       "      <th>10%</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>80%</th>\n",
       "      <th>90%</th>\n",
       "      <th>95%</th>\n",
       "      <th>99%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>891.0</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>257.353842</td>\n",
       "      <td>1.00</td>\n",
       "      <td>45.500</td>\n",
       "      <td>90.00</td>\n",
       "      <td>223.5000</td>\n",
       "      <td>446.0000</td>\n",
       "      <td>668.5</td>\n",
       "      <td>713.0000</td>\n",
       "      <td>802.0000</td>\n",
       "      <td>846.50000</td>\n",
       "      <td>882.10000</td>\n",
       "      <td>891.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>891.0</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>3.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>714.0</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>0.42</td>\n",
       "      <td>4.000</td>\n",
       "      <td>14.00</td>\n",
       "      <td>20.1250</td>\n",
       "      <td>28.0000</td>\n",
       "      <td>38.0</td>\n",
       "      <td>41.0000</td>\n",
       "      <td>50.0000</td>\n",
       "      <td>56.00000</td>\n",
       "      <td>65.87000</td>\n",
       "      <td>80.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>8.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>891.0</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>6.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>891.0</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.225</td>\n",
       "      <td>7.55</td>\n",
       "      <td>7.9104</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>31.0</td>\n",
       "      <td>39.6875</td>\n",
       "      <td>77.9583</td>\n",
       "      <td>112.07915</td>\n",
       "      <td>249.00622</td>\n",
       "      <td>512.3292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count        mean         std   min      5%    10%       25%  \\\n",
       "PassengerId  891.0  446.000000  257.353842  1.00  45.500  90.00  223.5000   \n",
       "Survived     891.0    0.383838    0.486592  0.00   0.000   0.00    0.0000   \n",
       "Pclass       891.0    2.308642    0.836071  1.00   1.000   1.00    2.0000   \n",
       "Age          714.0   29.699118   14.526497  0.42   4.000  14.00   20.1250   \n",
       "SibSp        891.0    0.523008    1.102743  0.00   0.000   0.00    0.0000   \n",
       "Parch        891.0    0.381594    0.806057  0.00   0.000   0.00    0.0000   \n",
       "Fare         891.0   32.204208   49.693429  0.00   7.225   7.55    7.9104   \n",
       "\n",
       "                  50%    75%       80%       90%        95%        99%  \\\n",
       "PassengerId  446.0000  668.5  713.0000  802.0000  846.50000  882.10000   \n",
       "Survived       0.0000    1.0    1.0000    1.0000    1.00000    1.00000   \n",
       "Pclass         3.0000    3.0    3.0000    3.0000    3.00000    3.00000   \n",
       "Age           28.0000   38.0   41.0000   50.0000   56.00000   65.87000   \n",
       "SibSp          0.0000    1.0    1.0000    1.0000    3.00000    5.00000   \n",
       "Parch          0.0000    0.0    1.0000    2.0000    2.00000    4.00000   \n",
       "Fare          14.4542   31.0   39.6875   77.9583  112.07915  249.00622   \n",
       "\n",
       "                  max  \n",
       "PassengerId  891.0000  \n",
       "Survived       1.0000  \n",
       "Pclass         3.0000  \n",
       "Age           80.0000  \n",
       "SibSp          8.0000  \n",
       "Parch          6.0000  \n",
       "Fare         512.3292  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the distribuiton of variables according to quantiles \n",
    "df.describe([0.05, 0.10, 0.25, 0.50, 0.75, 0.80, 0.90, 0.95, 0.99]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "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\n",
    "sns.boxplot(x=df[\"Age\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PassengerId', 'Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NUMBER OF NUMERICAL VARIABLES\n",
    "num_cols = [col for col in df.columns if df[col].dtypes != \"O\"]\n",
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NUMBER OF NUMERICAL VARIABLES:  7\n"
     ]
    }
   ],
   "source": [
    "# Report NUMBER OF NUMERICAL VARIABLES\n",
    "print('NUMBER OF NUMERICAL VARIABLES: ', len(num_cols))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
       "       'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop 'PassengerId' column\n",
    "df.drop(\"PassengerId\", axis = 1).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols = [col for col in df.columns if df[col].dtypes != \"O\" \n",
    "           and col not in \"PassengerId\"\n",
    "           and col not in \"Survived\"]\n",
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We know, that 'Survived' is our target variable and 'PassengerId' is not a numerical variable. Let's exlude them.\n",
    "num_cols = [col for col in df.columns if df[col].dtypes != \"O\" \n",
    "           and col not in [\"PassengerId\", \"Survived\"]]\n",
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic visualization rules\n",
    "# categorical variables --> barplot, countplot \n",
    "# numerical variables --> histogram, boxplot and density"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's see the frequencies of values and distribution of the dataset by using histogram and boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18c1725bdf0>"
      ]
     },
     "execution_count": 52,
     "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": [
    "# see the frequencies of values\n",
    "df[\"Age\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18c172e4e50>"
      ]
     },
     "execution_count": 53,
     "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": [
    "# distribution of the dataset\n",
    "sns.boxplot(x=df[\"Age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a funtion that plots histogram of numerical variables and prints the number of numerical variables if plotted.\n",
    "\n",
    "def hist_for_nums(data, numeric_cols):\n",
    "    \n",
    "    col_counter = 0\n",
    "    \n",
    "    for col in numeric_cols:\n",
    "        \n",
    "        data[col].hist()\n",
    "        plt.xlabel(col)\n",
    "        plt.title(col)\n",
    "        plt.show()\n",
    "        \n",
    "        col_counter += 1\n",
    "        \n",
    "    print(col_counter, \"variables have been plotted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LgYeBCwGqameSzcB9wHPAZVX1/IhqlyRNY8Zwr6r3TrPqvGn6bwA2HE5RkqTD4ydUJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDVoZOGeZE2SB5LsSrJ+VM8jSXqxkYR7kgXAlcCvAGcA701yxiieS5L0YqM6cz8X2FVVP6iqvwYmgPNH9FySpIMcM6LHPQn4Yd/ybuDN/R2SrAPWdYuTSR44jOc7EXj8MLaflfz+jF3mpa4BWNdwHF/Dsa4hrP79w6rr1OlWjCrcM0Vb/cxC1UZg45w8WfKtqlo5F481l6xrONY1HOsaztFW16guy+wGTulbPhl4dETPJUk6yKjC/ZvAiiTLk7wcWAvcNKLnkiQdZCSXZarquSS/Afw5sAD4VFXtHMVzdebk8s4IWNdwrGs41jWco6quVNXMvSRJLyl+QlWSGmS4S1KDjthwT/KpJHuT3DvN+iT5RDe9wXeSvLFv3UinPhigtou6mr6T5OtJ3tC37qEkO5LcneRbY65rVZKnuue+O8lH+9aN7JgNUNdv9dV0b5Lnk5zQrRvJ8UpySpLbk9yfZGeSD03RZ+xjbMC6xj6+Bqxr7ONrwLrmY3y9Msm2JPd0df3eFH1GO76q6oj8Ad4OvBG4d5r17wRupfee+rcAd3XtC4DvAz8PvBy4BzhjzLW9FVjS3f+VA7V1yw8BJ87TMVsF3DxF+0iP2Ux1HdT3PcCXR328gGXAG7v7xwH/++B9no8xNmBdYx9fA9Y19vE1SF3zNL4CLOruHwvcBbxlnOPriD1zr6o7gCcP0eV84I+r505gcZJljGHqg5lqq6qvV9W+bvFOeu/zH7kBjtl0RnrMhqzrvcBn5uq5p1NVj1XVt7v7PwHup/fJ6n5jH2OD1DUf42vA4zWdeT1eBxnX+KqqmuwWj+1+Dn73ykjH1xEb7gOYaoqDkw7RPl8upffb+YACvpRke3pTMIzb3+v+VLw1yZld2xFxzJL8DWAN8Nm+5pEfrySnAb9E7+yq37yOsUPU1W/s42uGuuZtfM10vMY9vpIsSHI3sBe4rarGOr5GNf3AOEw3xcGMUx+MS5LV9P7z/f2+5rdV1aNJXgfcluS73ZntOHwbOLWqJpO8E/g8sIIj55i9B/haVfWf5Y/0eCVZRO8/+4er6scHr55ik7GMsRnqOtBn7ONrhrrmbXwNcrwY8/iqqueBs5MsBm5IclZV9b/uNNLx9VI+c59uioMjYuqDJL8I/BFwflU9caC9qh7tbvcCN9D7E2wsqurHB/5UrKovAMcmOZEj5JjR+yTzz/zJPMrjleRYeoFwXVV9boou8zLGBqhrXsbXTHXN1/ga5Hh1xjq++p5jP7CV3l8N/UY7vubyRYS5/gFOY/oXB9/Fz74Ysa1rPwb4AbCcF16MOHPMtf0tYBfw1oPaFwLH9d3/OrBmjHX9TV744Nq5wMPd8Rv5MTtUXd364+ldl184juPV7fcfA//tEH3GPsYGrGvs42vAusY+vgapa57G12uBxd39VwFfBd49zvF1xF6WSfIZeq++n5hkN/A79F6UoKr+O/AFeq827wL+L/Dr3bqRT30wQG0fBV4DXJUE4Lnqzfq2lN6fZ9D7B/x0VX1xjHX9U+BfJXkOeAZYW73RNNJjNkBdAL8KfKmqnu7bdJTH623A+4Ed3XVRgI/QC875HGOD1DUf42uQuuZjfA1SF4x/fC0DNqX3xUUvAzZX1c1JPthX10jHl9MPSFKDXsrX3CVJ0zDcJalBhrskNchwl6QGGe6S1CDDXUeFbibAA7MC/ln3UfTp+v5ukt8cZ33SXDPcdbR4pqrOrqqzgL8GPjjfBUmjZLjraPRV4HSAJL/WzaV9T5I/Obhjkg8k+Wa3/rMHzviTXNj9FXBPkju6tjO7Obzv7h5zxVj3Surjh5h0VEgyWVWLkhxDbx6SLwJ3AJ+jN3nU40lOqKonk/wuMFlVf5DkNdXN3ZLkPwF7quqTSXbQ+6j6I0kWV9X+JJ8E7qyq65K8HFhQVc/Myw7rqOeZu44Wr+o+nv4tenOeXA38MnB9VT0OUD87W+ABZyX5ahfmFwEHprH9GnBNkg/Q+4g4wDeAjyT5bXqzIxrsmjdH7Nwy0hx7pqrO7m9Ib1KRmf50vQa4oKruSXIJvTlyqKoPJnkzvcmf7k5ydlV9OsldXdufJ/kXVfXlOd4PaSCeuetotgX4Z0leA5DuezUPchzwWDet7EUHGpP87aq6q6o+CjwOnJLk54EfVNUngJuAXxz5HkjT8MxdR62q2plkA/CVJM8DfwlcclC3f0/vm33+CthBL+wB/kv3gmno/ZK4B1gPvC/JT4EfAf9h5DshTcMXVCWpQV6WkaQGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQf8fWS2cbxKMdpkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 variables have been plotted\n"
     ]
    }
   ],
   "source": [
    "hist_for_nums(df, num_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. TARGET ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TARGET ANALYSIS ACCORDING TO CATEGORICAL VARIABLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Survived\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Sex\")[\"Survived\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_cols = [col for col in df.columns if len(df[col].unique()) < 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Categorical Variables:  6\n",
      "['Survived', 'Pclass', 'Sex', 'SibSp', 'Parch', 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "print('Number of Categorical Variables: ', len(cat_cols))\n",
    "print(cat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function that catches categorical variables with class number less than 10 and exclude target variable and analyse target variable with respect to categories\n",
    "\n",
    "def target_summary_with_cats(data, target):\n",
    "    \n",
    "    cats_names = [col for col in data.columns if len(data[col].unique()) < 10 and col not in target]\n",
    "    \n",
    "    for var in cats_names:\n",
    "        print(pd.DataFrame({\"TARGET_MEAN\": data.groupby(var)[target].mean()}), end=\"\\n\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        TARGET_MEAN\n",
      "Pclass             \n",
      "1          0.629630\n",
      "2          0.472826\n",
      "3          0.242363\n",
      "\n",
      "\n",
      "        TARGET_MEAN\n",
      "Sex                \n",
      "female     0.742038\n",
      "male       0.188908\n",
      "\n",
      "\n",
      "       TARGET_MEAN\n",
      "SibSp             \n",
      "0         0.345395\n",
      "1         0.535885\n",
      "2         0.464286\n",
      "3         0.250000\n",
      "4         0.166667\n",
      "5         0.000000\n",
      "8         0.000000\n",
      "\n",
      "\n",
      "       TARGET_MEAN\n",
      "Parch             \n",
      "0         0.343658\n",
      "1         0.550847\n",
      "2         0.500000\n",
      "3         0.600000\n",
      "4         0.000000\n",
      "5         0.200000\n",
      "6         0.000000\n",
      "\n",
      "\n",
      "          TARGET_MEAN\n",
      "Embarked             \n",
      "C            0.553571\n",
      "Q            0.389610\n",
      "S            0.336957\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "target_summary_with_cats(df, \"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TARGET ANALYSIS ACCORDING TO NUMERICAL VARIABLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30.626179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.343690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age\n",
       "Survived           \n",
       "0         30.626179\n",
       "1         28.343690"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See the average ages for survived and not survided\n",
    "df.groupby(\"Survived\").agg({\"Age\": np.mean})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function that catches numerical variables exclude target variable ang groupby survived with respect to numerical variables\n",
    "def target_summary_with_nums(data, target):\n",
    "    num_names = [col for col in data.columns if len(data[col].unique()) > 5\n",
    "                 and df[col].dtypes != 'O'\n",
    "                 and col not in target\n",
    "                 and col not in \"PassengerId\"]\n",
    "\n",
    "    for var in num_names:\n",
    "        print(df.groupby(target).agg({var: np.mean}), end=\"\\n\\n\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Age\n",
      "Survived           \n",
      "0         30.626179\n",
      "1         28.343690\n",
      "\n",
      "\n",
      "             SibSp\n",
      "Survived          \n",
      "0         0.553734\n",
      "1         0.473684\n",
      "\n",
      "\n",
      "             Parch\n",
      "Survived          \n",
      "0         0.329690\n",
      "1         0.464912\n",
      "\n",
      "\n",
      "               Fare\n",
      "Survived           \n",
      "0         22.117887\n",
      "1         48.395408\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "target_summary_with_nums(df, \"Survived\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. ANALYSIS OF NUMERCIAL VARIABLES IN COMPARISON WITH EACH OTHER (SAYISAL DEGISKENLERIN BIRBIRLERINE GORE ANALIZI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here we used another dataset --> 'tips'\n",
    "df = sns.load_dataset(\"tips\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# scatterplot\n",
    "sns.scatterplot(x=\"total_bill\", y=\"tip\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lmplot\n",
    "sns.lmplot(x=\"total_bill\", y=\"tip\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_bill</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.675734</td>\n",
       "      <td>0.598315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tip</th>\n",
       "      <td>0.675734</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.489299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size</th>\n",
       "      <td>0.598315</td>\n",
       "      <td>0.489299</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_bill       tip      size\n",
       "total_bill    1.000000  0.675734  0.598315\n",
       "tip           0.675734  1.000000  0.489299\n",
       "size          0.598315  0.489299  1.000000"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation table\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>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": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's bring titanic dataset again\n",
    "df = pd.read_csv(\"C:/Users/yakup/OneDrive/Masaüstü/DSMLBC/datasets/train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Special function to summarize this notebook\n",
    "\n",
    "def analyse(data, target):\n",
    "    cat_cols = [col for col in df.columns if df[col].dtype == \"O\"]\n",
    "    num_cols = [col for col in df.columns if df[col].dtypes != 'O' and col not in target]\n",
    "    \n",
    "    print('********************************Let\\'s see categorical variables and take a quick look at them!********************************\\n')\n",
    "    cats_summary1(data)\n",
    "    print('********************************Let\\'s see numerical variables and take a quick look at them with histograms!********************************\\n')\n",
    "    hist_for_nums(data, num_cols)\n",
    "    print('********************************Let\\'s see target summary with categorical variables!********************************\\n')\n",
    "    target_summary_with_cats(data, target)\n",
    "    print('********************************Let\\'s see target summary with numerical variables!********************************\\n')\n",
    "    target_summary_with_nums(data, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************Let's see categorical variables and take a quick look at them!********************************\n",
      "\n",
      "   Survived      Ratio\n",
      "0       549  61.616162\n",
      "1       342  38.383838\n",
      "\n",
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pclass      Ratio\n",
      "3     491  55.106622\n",
      "1     216  24.242424\n",
      "2     184  20.650954\n",
      "\n",
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Sex      Ratio\n",
      "male    577  64.758698\n",
      "female  314  35.241302\n",
      "\n",
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   SibSp      Ratio\n",
      "0    608  68.237935\n",
      "1    209  23.456790\n",
      "2     28   3.142536\n",
      "4     18   2.020202\n",
      "3     16   1.795735\n",
      "8      7   0.785634\n",
      "5      5   0.561167\n",
      "\n",
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Parch      Ratio\n",
      "0    678  76.094276\n",
      "1    118  13.243547\n",
      "2     80   8.978676\n",
      "5      5   0.561167\n",
      "3      5   0.561167\n",
      "4      4   0.448934\n",
      "6      1   0.112233\n",
      "\n",
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Embarked      Ratio\n",
      "S       644  72.278339\n",
      "C       168  18.855219\n",
      "Q        77   8.641975\n",
      "\n",
      "\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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "********************************Let's see numerical variables and take a quick look at them with histograms!********************************\n",
      "\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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 variables have been plotted\n",
      "********************************Let's see target summary with categorical variables!********************************\n",
      "\n",
      "        TARGET_MEAN\n",
      "Pclass             \n",
      "1          0.629630\n",
      "2          0.472826\n",
      "3          0.242363\n",
      "\n",
      "\n",
      "        TARGET_MEAN\n",
      "Sex                \n",
      "female     0.742038\n",
      "male       0.188908\n",
      "\n",
      "\n",
      "       TARGET_MEAN\n",
      "SibSp             \n",
      "0         0.345395\n",
      "1         0.535885\n",
      "2         0.464286\n",
      "3         0.250000\n",
      "4         0.166667\n",
      "5         0.000000\n",
      "8         0.000000\n",
      "\n",
      "\n",
      "       TARGET_MEAN\n",
      "Parch             \n",
      "0         0.343658\n",
      "1         0.550847\n",
      "2         0.500000\n",
      "3         0.600000\n",
      "4         0.000000\n",
      "5         0.200000\n",
      "6         0.000000\n",
      "\n",
      "\n",
      "          TARGET_MEAN\n",
      "Embarked             \n",
      "C            0.553571\n",
      "Q            0.389610\n",
      "S            0.336957\n",
      "\n",
      "\n",
      "********************************Let's see target summary with numerical variables!********************************\n",
      "\n",
      "                Age\n",
      "Survived           \n",
      "0         30.626179\n",
      "1         28.343690\n",
      "\n",
      "\n",
      "             SibSp\n",
      "Survived          \n",
      "0         0.553734\n",
      "1         0.473684\n",
      "\n",
      "\n",
      "             Parch\n",
      "Survived          \n",
      "0         0.329690\n",
      "1         0.464912\n",
      "\n",
      "\n",
      "               Fare\n",
      "Survived           \n",
      "0         22.117887\n",
      "1         48.395408\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "analyse(df, \"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "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": 4
}