5_data_analysis_processing/0_introduction_to_EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<table style=\"border:none\">\n",
    "    <tr style=\"border:none\">\n",
    "    <th style=\"border:none\">\n",
    "        <a  href='https://colab.research.google.com/github/AmirMardan/ml_course/blob/main/5_data_analysis_processing/0_introduction_to_EDA.ipynb'><img src='https://colab.research.google.com/assets/colab-badge.svg'></a>\n",
    "    </th>\n",
    "    <th style=\"border:none\">\n",
    "        <a  href='https://github1s.com/AmirMardan/ml_course/blob/main/5_data_analysis_processing/0_introduction_to_EDA.ipynb'><img src='../imgs/open_vscode.svg' height=20px width=115px></a>\n",
    "    </th>\n",
    "    </tr>\n",
    "</table>\n",
    "</center>\n",
    "\n",
    "\n",
    "This notebook is created by <a href='https://amirmardan.github.io'> Amir Mardan</a>. For any feedback or suggestion, please contact me via <a href=\"mailto:mardan.amir.h@gmail.com\">email</a>, (mardan.amir.h@gmail.com).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='top'></a>\n",
    "# Exploratory data analysis\n",
    "\n",
    "**Exploratory data analysis** or **EDA** is a crucial step in every machine-learning project that leads to understanding data. \n",
    "The application of EDA is to\n",
    "- Understand the statistics about data\n",
    "- Find the missing values and redundancies and face them\n",
    "- Find the correlation between features\n",
    "- Understand the importance of features\n",
    "- Visualization and summarization\n",
    "\n",
    "\n",
    "This notebook will cover the following topics:\n",
    "\n",
    "- [1. Import packages and dataset](#import)\n",
    "- [2. Initial general assessment](#general)\n",
    "- [3. Basic analysis](#basic)\n",
    "- [4. Missing data](#missing)\n",
    "- [5. Outliers](#outliers)\n",
    "- [6. Correlation](#Correlation)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='import'></a>\n",
    "## 1. Import packages and dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "sns.set_theme()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 61194  100 61194    0     0   165k      0 --:--:-- --:--:-- --:--:--  170k\n",
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 28629  100 28629    0     0  97674      0 --:--:-- --:--:-- --:--:--   98k\n"
     ]
    }
   ],
   "source": [
    "!curl -O https://raw.githubusercontent.com/AmirMardan/ml_course/main/data/titanic_train.csv\n",
    "!curl -O https://raw.githubusercontent.com/AmirMardan/ml_course/main/data/titanic_test.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./titanic_train.csv')\n",
    "df_test = pd.read_csv('./titanic_test.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='general'></a>\n",
    "## 2. Initial assessment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `PassengerId` is the unique id of the row and it doesn't have any effect on the target\n",
    "- `Survived` is the target variable we are trying to predict (**0** or **1**):\n",
    "    - **1** = Survived\n",
    "    - **0** = Not Survived\n",
    "- `Pclass` (Passenger Class) is the status of the passenger and it is a categorical ordinal feature which has 3 unique values (**1**, **2**, or **3**):\n",
    "    - **1** = Upper Class\n",
    "    - **2** = Middle Class\n",
    "    - **3** = Lower Class\n",
    "- `Name`, `Sex`, and `Age` are self-explanatory\n",
    "- `SibSp` is the total number of the passengers' siblings and spouse\n",
    "- `Parch` is the total number of the passengers' parents and children\n",
    "- `Ticket` is the ticket number of the passenger\n",
    "- `Fare` is the passenger fare\n",
    "- `Cabin` is the cabin number of the passenger\n",
    "- `Embarked` is port of embarkation and it is a categorical feature which has 3 unique values (**C**, **Q**, or **S**):\n",
    "    - **C** = Cherbourg\n",
    "    - **Q** = Queenstown\n",
    "    - **S** = Southampton"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the first `n` samples of a DataFrame using `DataFrame.head(n)` where the default value for `n` is 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the last `n` samples of a DataFrame using `DataFrame.tail(n)` where the default value for `n` is 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\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",
       "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see `n` random samples of a DataFrame using `DataFrame.sample(n)` where the default value for `n` is 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>224</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Nenkoff, Mr. Christo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349234</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>427</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Clarke, Mrs. Charles V (Ada Maria Winfield)</td>\n",
       "      <td>female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>478</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Lewis Richard</td>\n",
       "      <td>male</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3460</td>\n",
       "      <td>7.0458</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "882          883         0       3   \n",
       "223          224         0       3   \n",
       "426          427         1       2   \n",
       "477          478         0       3   \n",
       "\n",
       "                                            Name     Sex   Age  SibSp  Parch  \\\n",
       "882                 Dahlberg, Miss. Gerda Ulrika  female  22.0      0      0   \n",
       "223                         Nenkoff, Mr. Christo    male   NaN      0      0   \n",
       "426  Clarke, Mrs. Charles V (Ada Maria Winfield)  female  28.0      1      0   \n",
       "477                    Braund, Mr. Lewis Richard    male  29.0      1      0   \n",
       "\n",
       "     Ticket     Fare Cabin Embarked  \n",
       "882    7552  10.5167   NaN        S  \n",
       "223  349234   7.8958   NaN        S  \n",
       "426    2003  26.0000   NaN        S  \n",
       "477    3460   7.0458   NaN        S  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(4)"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "There are 891 people with 12 columns.\n",
    "We can get basic information about the DataFrame using `DataFrame.info()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": "markdown",
   "metadata": {},
   "source": [
    "As is shown, there are 12 columns. `Survived` is the target and the other 11 features are *predictors*.\n",
    "The data frame is a mix of categorical and numerical data.\n",
    "We also have missing values, especially in `Cabin`.\n",
    "\n",
    "We can get some statistical insight about data using `DataFrame.describe()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='basic'></a>\n",
    "## 3. Basic analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now dig the data more and get more information.\n",
    "The first thing I would like to know is about the number of people who survived and who couldn't make it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, 342 people out of 891 survived this disaster. Although there were approximately 2224 passengers and crew aboard, we talk about the numbers in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Survived', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "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.countplot(data=df, x='Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the passengers based on their gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sex', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "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.countplot(data=df, x='Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(data=df, x='Age', y='Sex', kind='strip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the target is categorical, I create a categorical version of `Survived` for further studies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To make it easier for our studies, I make a catergorical version of the targert\n",
    "df['Survived_cat'] = df['Survived'].astype('category')\n",
    "\n",
    "\n",
    "df['Pclass'] = df['Pclass'].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the target based on the age of people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.catplot(data=df, x='Age', y='Survived_cat',kind='strip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Has the place of embarkation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(data=df, x='Embarked')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, most of people embarked in Southampton."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 415.55x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(data=df, x='Age', y='Embarked', hue='Survived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that people who embarked in Cherbourg had more possibility to survive. This is clear if we group the data based on the place of embarkation. \n",
    "To do so, I create a copy of dataframe with `Survived` as a numerical feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Embarked</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>445.357143</td>\n",
       "      <td>0.553571</td>\n",
       "      <td>30.814769</td>\n",
       "      <td>0.386905</td>\n",
       "      <td>0.363095</td>\n",
       "      <td>59.954144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q</th>\n",
       "      <td>417.896104</td>\n",
       "      <td>0.389610</td>\n",
       "      <td>28.089286</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.168831</td>\n",
       "      <td>13.276030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>449.527950</td>\n",
       "      <td>0.336957</td>\n",
       "      <td>29.445397</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.413043</td>\n",
       "      <td>27.079812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          PassengerId  Survived        Age     SibSp     Parch       Fare\n",
       "Embarked                                                                 \n",
       "C          445.357143  0.553571  30.814769  0.386905  0.363095  59.954144\n",
       "Q          417.896104  0.389610  28.089286  0.428571  0.168831  13.276030\n",
       "S          449.527950  0.336957  29.445397  0.571429  0.413043  27.079812"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numerical= df.copy()\n",
    "df_numerical['Survived'] = df_numerical['Survived'].astype(np.int32)\n",
    "df_numerical.groupby(['Embarked']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It also seems that passengers from Cherbourg were older with fewer siblings and used more expensive parts of the ship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 423.425x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g =sns.catplot(data=df, y='Pclass', x='Fare', kind='strip', hue='Embarked')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This type of analysis can continue and there is no limit to it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='missing'></a>\n",
    "## 4. Missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's important to decrease the number of missing values in the data. So, first, we need to know about the situation of missing values in the dataframe. We can get the number of missing values for each feature by using\n",
    "\n",
    "```Python\n",
    "df.isna().sum().sort_values(ascending=False)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin           687\n",
       "Age             177\n",
       "Embarked          2\n",
       "PassengerId       0\n",
       "Survived          0\n",
       "Pclass            0\n",
       "Name              0\n",
       "Sex               0\n",
       "SibSp             0\n",
       "Parch             0\n",
       "Ticket            0\n",
       "Fare              0\n",
       "Survived_cat      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can present the missing values in percentage\n",
    "\n",
    "```Python\n",
    "df.isna().sum().sort_values(ascending=False)/df.shape[0] * 100\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin           77.104377\n",
       "Age             19.865320\n",
       "Embarked         0.224467\n",
       "PassengerId      0.000000\n",
       "Survived         0.000000\n",
       "Pclass           0.000000\n",
       "Name             0.000000\n",
       "Sex              0.000000\n",
       "SibSp            0.000000\n",
       "Parch            0.000000\n",
       "Ticket           0.000000\n",
       "Fare             0.000000\n",
       "Survived_cat     0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sort_values(ascending=False)/df.shape[0] * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As is shown, the most missing values belong to `Cabin`. \n",
    "We'll discuss how to deal with missing values in the next notebooks but for now and as an example, let's start with the easiest one which is `Embarked`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
       "      <th>Survived_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived Pclass                                       Name  \\\n",
       "61            62         1      1                        Icard, Miss. Amelie   \n",
       "829          830         1      1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "        Sex   Age  SibSp  Parch  Ticket  Fare Cabin Embarked Survived_cat  \n",
       "61   female  38.0      0      0  113572  80.0   B28      NaN            1  \n",
       "829  female  62.0      0      0  113572  80.0   B28      NaN            1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Embarked'].isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two missing values for `Embarked`. We can find easily on the internet that these people's embarkation should be `S`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['Embarked'].isna(), 'Embarked'] = 'S'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.heatmap(df.isna(), yticklabels=False, cbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='outliers'></a>\n",
    "## 5. Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many machine learning algorithms are sensitive to the range and distribution of the variables.\n",
    "Outliers can skew and mislead the training process which can cause\n",
    "- longer learning time,\n",
    "- less accuracy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<img src='./img/outlier.png' width='300px'>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 423.425x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(data=df, y='Pclass', x='Fare', kind='strip', hue='Embarked')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems there are some outliers in `Pclass = 1`. We can find these samples as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
       "      <th>Survived_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>680</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cardeza, Mr. Thomas Drake Martinez</td>\n",
       "      <td>male</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>738</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Lesurer, Mr. Gustave J</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B101</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ward, Miss. Anna</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>743</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Miss. Susan Parker \"Suzette\"</td>\n",
       "      <td>female</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>312</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Ryerson, Miss. Emily Borie</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PC 17608</td>\n",
       "      <td>262.3750</td>\n",
       "      <td>B57 B59 B63 B66</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived Pclass                                   Name  \\\n",
       "679          680         1      1     Cardeza, Mr. Thomas Drake Martinez   \n",
       "737          738         1      1                 Lesurer, Mr. Gustave J   \n",
       "258          259         1      1                       Ward, Miss. Anna   \n",
       "742          743         1      1  Ryerson, Miss. Susan Parker \"Suzette\"   \n",
       "311          312         1      1             Ryerson, Miss. Emily Borie   \n",
       "\n",
       "        Sex   Age  SibSp  Parch    Ticket      Fare            Cabin Embarked  \\\n",
       "679    male  36.0      0      1  PC 17755  512.3292      B51 B53 B55        C   \n",
       "737    male  35.0      0      0  PC 17755  512.3292             B101        C   \n",
       "258  female  35.0      0      0  PC 17755  512.3292              NaN        C   \n",
       "742  female  21.0      2      2  PC 17608  262.3750  B57 B59 B63 B66        C   \n",
       "311  female  18.0      2      2  PC 17608  262.3750  B57 B59 B63 B66        C   \n",
       "\n",
       "    Survived_cat  \n",
       "679            1  \n",
       "737            1  \n",
       "258            1  \n",
       "742            1  \n",
       "311            1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Pclass']==1) & (df['Embarked']=='C')].sort_values('Fare', ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By a little search on the internet, I found that these samples are correct and belong to Mr. Thomas Drake Martinez Cardeza and his manservant and his mother's maid. However, his mother's sample is in the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>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>343</th>\n",
       "      <td>1235</td>\n",
       "      <td>1</td>\n",
       "      <td>Cardeza, Mrs. James Warburton Martinez (Charlo...</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                               Name  \\\n",
       "343         1235       1  Cardeza, Mrs. James Warburton Martinez (Charlo...   \n",
       "\n",
       "        Sex   Age  SibSp  Parch    Ticket      Fare        Cabin Embarked  \n",
       "343  female  58.0      0      1  PC 17755  512.3292  B51 B53 B55        C  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test[df_test['Ticket']=='PC 17755']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mr. Thomas Drake Martinez Cardeza and his mother, Mrs. James Warburton Martinez, were living in one of the two most luxurious rooms.\n",
    "\n",
    "<center>\n",
    "<img src='./img/cab51.png', width='500px'>\n",
    "</center>\n",
    "\n",
    "If I plot boxplot of `Fare` for different cities, I can get"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def boxplot_embarked(df1):\n",
    "    fig = plt.figure(figsize=(12, 4))\n",
    "    i = 1\n",
    "    for city in df['Embarked'].unique():\n",
    "        if city is not np.nan:\n",
    "            ax = fig.add_subplot(1, 3, i)\n",
    "            ax = sns.boxplot(data=df1[df1['Embarked']==city], y='Fare')\n",
    "            ax.set_title(city)\n",
    "            i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot_embarked(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For higher values in `Q`, I checked again by searching on the internet and these samples are confirmed here\n",
    "https://www.encyclopedia-titanica.org/titanic-victim/william-edward-minahan.html\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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",
       "      <th>Survived_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Minahan, Dr. William Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>19928</td>\n",
       "      <td>90.0</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>413</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Minahan, Miss. Daisy E</td>\n",
       "      <td>female</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19928</td>\n",
       "      <td>90.0</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived Pclass                         Name     Sex   Age  \\\n",
       "245          246         0      1  Minahan, Dr. William Edward    male  44.0   \n",
       "412          413         1      1       Minahan, Miss. Daisy E  female  33.0   \n",
       "\n",
       "     SibSp  Parch Ticket  Fare Cabin Embarked Survived_cat  \n",
       "245      2      0  19928  90.0   C78        Q            0  \n",
       "412      1      0  19928  90.0   C78        Q            1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Embarked']=='Q') & (df['Pclass']==1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These samples belong to Dr. William Edward Minahan and his sister. The sample of his wife belongs to the test set. Although I know she was rescued, I leave this sample in the test set for the sake of the competition. They were the 3 first-class passengers embarked in Queenstown with fare of 90 ￡.\n",
    "\n",
    "<center>\n",
    "<table>\n",
    "<tr>\n",
    "    <tc>\n",
    "        <img src='./img/William.png' width='200px' height='250px'>\n",
    "    </tc>\n",
    "    <tc>\n",
    "        <img src='./img/Daisy.png' width='200px' height='250px'>\n",
    "    </tc>\n",
    "</tr>\n",
    "</table>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>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>411</th>\n",
       "      <td>1303</td>\n",
       "      <td>1</td>\n",
       "      <td>Minahan, Mrs. William Edward (Lillian E Thorpe)</td>\n",
       "      <td>female</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19928</td>\n",
       "      <td>90.0</td>\n",
       "      <td>C78</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Pclass                                             Name  \\\n",
       "411         1303       1  Minahan, Mrs. William Edward (Lillian E Thorpe)   \n",
       "\n",
       "        Sex   Age  SibSp  Parch Ticket  Fare Cabin Embarked  \n",
       "411  female  37.0      1      0  19928  90.0   C78        Q  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test[df_test['Ticket']=='19928']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, it seems although we see some high values for `Fare`, these values are actually correct.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Correlation'></a>\n",
    "## 6. Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlation is really important for learning more about data. \n",
    "This is a tool for feature selection which will be discussed in the next notebooks.\n",
    "\n",
    "Correlation between two variables show how much those two variables contain the same information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived       Age     SibSp     Parch      Fare\n",
       "PassengerId     1.000000 -0.005007  0.036847 -0.057527 -0.001652  0.012658\n",
       "Survived       -0.005007  1.000000 -0.077221 -0.035322  0.081629  0.257307\n",
       "Age             0.036847 -0.077221  1.000000 -0.308247 -0.189119  0.096067\n",
       "SibSp          -0.057527 -0.035322 -0.308247  1.000000  0.414838  0.159651\n",
       "Parch          -0.001652  0.081629 -0.189119  0.414838  1.000000  0.216225\n",
       "Fare            0.012658  0.257307  0.096067  0.159651  0.216225  1.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.heatmap(corr, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also have the correlation of all variables with respect to one variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId   -0.005007\n",
       "Survived       1.000000\n",
       "Age           -0.077221\n",
       "SibSp         -0.035322\n",
       "Parch          0.081629\n",
       "Fare           0.257307\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_survived = df.corr()['Survived']\n",
    "corr_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = corr_survived.abs().sort_values(ascending=False) \n",
    "x.drop(index=['Survived'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAD7CAYAAAB0d9PAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYPklEQVR4nO3dfbQddX3v8fdJyAMhEGOINQHRtl6+LrQ8B7SiFwtqCRW1gEhiI1ikFvFiseaCAQyuohewN6ICRZAHi6GyuEuoPGlTosQiDWBJ8EK/2oLIDdGbBgSSXpLAyf1j5sBmc/I7+8Qz2eecvF9rnZU9v/nN3t89mT2fPTN7Zno2b96MJElbMqbbBUiShjeDQpJUZFBIkooMCklSkUEhSSraodsFDLEJwCxgNfB8l2uRpJFiLDADuAfY0D5ytAXFLGBZt4uQpBHqbcAP2xtHW1CsBnjyyfX09np+iCR1YsyYHqZO3QnqdWi70RYUzwP09m42KCRp8PrdZe/BbElSkUEhSSoyKCRJRQaFJKnIoJAkFRkUkqQig0KSVDTazqMo2nmXiUycMK7bZQypZzds4pmnn+12GZJGse0qKCZOGMec+d/sdhlDavEFc3kGg0JScxoNioiYA5wFjAcWZebFbePfC5wL9ACPACdm5pMRMQ84H/hV3fWWzFzQZK2SpP41FhQRsRtwHnAA1dUI74qIpZn5YD1+F+BSYFZmroqIzwELgdOoLu53emZe11R9kqTONHkw+3Dgjsx8IjPXAzcAx7SMHweckpmr6uGVwB7141nAvIhYERHXRsTUBuuUJBU0GRQzeemVCFcDu/cNZObazLwRICJ2BM4AbmzpuxDYF3gM+GqDdUqSCpo8RtHTT1tve0NETKEKiBWZeQ1AZr6/ZfwFwMODeeFp0yYPqtCRbvr0nbtdgqRRrMmgWEV1E4w+M4DHWztExAzgu8AdwF/UbVOAj2TmorpbD7BpMC+8du26fi8zPlpXqGvWPNPtEiSNYGPG9BS/YDe562kJcFhETI+IScDRwO19IyNiLHAzcH1mfjIz+9bs64D5EXFwPXwq8O0G65QkFTS2RVH/kmkBsJTq57FXZObyiLgVOAd4DbAfMDYi+g5y35uZJ0XEB4BL62MXPwXmNVWnJKms0fMoMnMxsLitbXb98F62sEWTmcuA/ZusTZLUGa/1JEkqMigkSUUGhSSpyKCQJBUZFJKkIoNCklRkUEiSigwKSVKRQSFJKjIoJElFBoUkqcigkCQVGRSSpCKDQpJUZFBIkooMCklSkUEhSSoyKCRJRQaFJKnIoJAkFRkUkqQig0KSVGRQSJKKDApJUpFBIUkqMigkSUUGhSSpyKCQJBUZFJKkIoNCklRkUEiSigwKSVLRDk0+eUTMAc4CxgOLMvPitvHvBc4FeoBHgBMz88mI2AO4FngVkMDczFzXZK2SpP41tkUREbsB5wGHAPsAJ0fEXi3jdwEuBY7MzH2AlcDCevQlwCWZ+QbgXuDspuqUJJU1uevpcOCOzHwiM9cDNwDHtIwfB5ySmavq4ZXAHhExDnh73R/gauDYBuuUJBU0uetpJrC6ZXg1cFDfQGauBW4EiIgdgTOArwC7Ak9n5nMt0+0+mBeeNm3yVhc9Ek2fvnO3S5A0ijUZFD39tPW2N0TEFKrAWJGZ10TEzE6mK1m7dh29vZtf1j5aV6hr1jzT7RIkjWBjxvQUv2A3uetpFfDqluEZwOOtHSJiBrAMWAGcVDevAXaJiLFbmk6StO00GRRLgMMiYnpETAKOBm7vG1kHwc3A9Zn5yczcDJCZm6jC47i66zzgtgbrlCQVNLbrKTNXRcQCYCnVz2OvyMzlEXErcA7wGmA/YGxE9B3kvjczTwJOAa6JiLOAXwDHN1WnJKms0fMoMnMxsLitbXb98F62sEWTmY8ChzZZmySpM56ZLUkqMigkSUUGhSSpyKCQJBUZFJKkIoNCklRkUEiSigwKSVKRQSFJKjIoJElFBoUkqcigkCQVGRSSpCKDQpJUZFBIkooMCklSkUEhSSoyKCRJRQaFJKnIoJAkFRkUkqQig0KSVGRQSJKKDApJUpFBIUkqMigkSUU7lEZGxCPA5i2Nz8zfGfKKJEnDSjEogGPqf08BNgJfA54DTgTGN1iXJGmYKAZFZt4HEBFvysyDW0adHhHLG61MkjQsdHqM4hURMb1vICJmArs0U5IkaTgZaNdTny8BD0TEd4Ee4F3A/KaKkiQNHx0FRWZeGhF3AYdRHdy+IDN/MtB0ETEHOIvqeMaizLx4C/2uAZZm5tX18DzgfOBXdZdbMnNBJ7VKkoZWp1sUAP8FeCXwBeAooBgUEbEbcB5wALABuCsilmbmgy19ZgKXUQXQ0pbJZwGnZ+Z1g6hPktSAjo5RRMQZwJ8DHwAmAp+NiLMHmOxw4I7MfCIz1wM38OKvqPrMBW4Crm9rnwXMi4gVEXFtREztpE5J0tDr9GD2B4HZwPrMXAu8GZgzwDQzgdUtw6uB3Vs7ZOaFmXlFP9OuBhYC+wKPAV/tsE5J0hDrdNfTpszcEBEAZOavI2LTANP09NPW28mLZeb7+x5HxAXAwx3WCcC0aZMH033Emz59526XIGkU6zQoHouII4HNETEB+Evg0QGmWQW8rWV4BvD4QC8UEVOAj2TmorqpBxgolF5i7dp19Pa+/ITy0bpCXbPmmW6XIGkEGzOmp/gFu9NdT6cCpwN7A+uBI4CPDzDNEuCwiJgeEZOAo4HbO3itdcD8iOg7we9U4Nsd1ilJGmKdBsX6zDyM6iS7qZl5SGb+ojRBZq4CFlD9mul+YHFmLo+IWyPiwMJ0z1MdNL80Ih6i+tWU52xIUpd0uuvpkYi4Gbg8M5d1+uSZuRhY3NY2u59+J7QNLwP27/R1JEnN6TQofhs4HvhifQzhCuCazFzTWGWSpGGho11PmflUZv5NfWHA46jOh3is0cokScNCx2dmR8T+wAlUxw+WA8c2VJMkaRjpKCgiYiWwE3AlsH9mDvgzV0nS6NDpFsWnMvMfGq1EkjQsDXQr1PmZeQFwVES8p318Zv63xiqTJA0LA21RPFX/+x9NFyJJGp4GuhXqZfXDX1KdMOe1IiRpO9PpmdmHAg9HxJUR8ZYG65EkDTOdnkdxPLAncC9wUUQ8EBGnNVqZJGlY6HSLgsx8Evga1R3u1gNnNFWUJGn46PQ8iv2BE6lOsvsxcAHw9w3WJUkaJjo9j+JG4OvAQQNdNVaSNLp0GhTLMvPcRiuRJA1LnR6jeGNE9HdrU0nSKNfpFsUvgf8dEXdT3YEO8MxsSdoedBoUP6r/JEnbmY6CwuMTkrT96vTnsQ8Am9vbM3PvIa9IkjSsdLrr6dSWx+OB9wHek0KStgOd7nr6QetwRCwB7gLOa6IoSdLw0fElPNpMA2YOZSGSpOFpa45R9AB7AJdteQpJ0mgxYFDUJ9qdDmwEpgD7ADdm5gMN1yZJGgaKu54iYi/gEWACsBw4Hzge+E5EvLP58iRJ3TbQMYoLgQWZeTPwwbrtTcDbgIUN1iVJGiYGCoo9MvOb9eN3ADdlZm9mPka1G0qSNMoNFBTPtzz+feDOluGJQ1+OJGm4Gehg9hMRsQ+wMzAD+AFARPw+sKrh2iRJw8BAQfEZYAnVbqb5mbk+Iv4SWEB1drYkaZQrBkVm3h0RuwGTMvPXdfNdVHe6+1nTxUmSum/A8ygycyPVORR9w3d1+uQRMQc4i+r6UIsy8+It9LsGWJqZV9fDewDXAq8CEpibmev6m1aS1KytvYTHgOotkfOAQ6hO0ju5Pi+jtc/MiPgOcGzb5JcAl2TmG4B7gbObqlOSVNZYUACHA3dk5hOZuR64ATimrc9c4Cbg+r6GiBgHvL3uD3A1Lw8SSdI20ullxrfGTGB1y/Bq4KDWDpl5IUBEHNLSvCvwdGY+1zLd7g3WKUkqaDIoevpp621wuhdMmzZ5MN1HvOnTd+52CZJGsSaDYhXVpT76zKCzmx2tAXaJiLGZ+fwgpnvB2rXr6O192Q35Ru0Kdc2aZ7pdgqQRbMyYnuIX7CaPUSwBDouI6RExCTgauH2giTJzE7AMOK5umgfc1liVkqSixoIiM1dRnZi3FLgfWJyZyyPi1og4cIDJT6H6ldSDVFslZzVVpySprMldT2TmYmBxW9vsfvqd0Db8KHBok7VJkjrT5K4nSdIoYFBIkooMCklSkUEhSSoyKCRJRQaFJKnIoJAkFRkUkqQig0KSVGRQSJKKGr2Eh4avqVPGs8P4Cd0uY0g9t3EDTz61ceCOkgbFoNhO7TB+AvddcFK3yxhSB8y/gpbbu0saIu56kiQVuUWh7d4uUyYwYfz4bpcxpDZs3MjTT23odhkaJQwKbfcmjB/PCVed1u0yhtTVJ14EGBQaGu56kiQVGRSSpCKDQpJUZFBIkooMCklSkUEhSSoyKCRJRQaFJKnIoJAkFRkUkqQig0KSVGRQSJKKDApJUpFBIUkqMigkSUUGhSSpqNEbF0XEHOAsYDywKDMvbhu/L3A5MAW4E/hYZj4XEfOA84Ff1V1vycwFTdYqSepfY1sUEbEbcB5wCLAPcHJE7NXW7VrgE5m5J9ADfLRunwWcnpn71n+GhCR1SZO7ng4H7sjMJzJzPXADcEzfyIh4LbBjZt5dN10NHFs/ngXMi4gVEXFtRExtsE5JUkGTQTETWN0yvBrYvcPxq4GFwL7AY8BXmypSklTW5DGKnn7aejsZn5nv72uIiAuAhwfzwtOmTR5M9xFv+vSdu13CsOG8eJHzQkOlyaBYBbytZXgG8Hjb+Fe3j4+IKcBHMnNR3d4DbBrMC69du47e3s0vax+tH5w1a54Z9DTOixc5L7S9GzOmp/gFu8ldT0uAwyJiekRMAo4Gbu8bmZmPAs9GxFvrpnnAbcA6YH5EHFy3nwp8u8E6JUkFjQVFZq4CFgBLgfuBxZm5PCJujYgD625zgUUR8RCwE/DlzHwe+ABwad1+ADC/qTolSWWNnkeRmYuBxW1ts1serwAO6me6ZcD+TdYmSeqMZ2ZLkooMCklSkUEhSSoyKCRJRQaFJKnIoJAkFRkUkqSiRs+jkDSyvGLn8YybOKHbZQypTc9u4NfPbOx2GSOaQSHpBeMmTuDWeSd2u4whNfsbV4FB8Rtx15MkqcigkCQVGRSSpCKDQpJUZFBIkooMCklSkUEhSSoyKCRJRQaFJKnIoJAkFRkUkqQig0KSVGRQSJKKDApJUpFBIUkqMigkSUUGhSSpyKCQJBUZFJKkIoNCklRkUEiSigwKSVLRDt0uQJKGoym77Mj4CaNrFblxw3M89fT/G/R0jc6FiJgDnAWMBxZl5sVt4/cFLgemAHcCH8vM5yJiD+Ba4FVAAnMzc12TtUpSq/ETduDzC27odhlD6jPnHbNV0zW26ykidgPOAw4B9gFOjoi92rpdC3wiM/cEeoCP1u2XAJdk5huAe4Gzm6pTklTW5BbF4cAdmfkEQETcABwDfK4efi2wY2beXfe/Gjg3Iq4A3g68r6X9B8B/7+A1xwKMGdOzxQ67Tt1pcO9iBCi935Lxu0wb4kq6b2vnxa6TXznElXTf1s6LHXd1uegz5RWThriS7utvXrS0je1vmiaDYiawumV4NXDQAON3B3YFns7M59raOzEDYGohDL585vs6fKqRY9q0yVs13e997PwhrqT7tnZefPHYzw5xJd23tfPiHf/zi0NcSfdt7bz4+KdnD3El3TfAvJgB/Ht7Y5NB0V+E93YwfqDpSu4B3kYVLs93OI0kbe/GUoXEPf2NbDIoVlGttPvMAB5vG//qfsavAXaJiLGZ+Xw/05VsAH641RVL0vbrZVsSfZo8j2IJcFhETI+IScDRwO19IzPzUeDZiHhr3TQPuC0zNwHLgONa2xusU5JU0FhQZOYqYAGwFLgfWJyZyyPi1og4sO42F1gUEQ8BOwFfrttPofqV1INUWyVnNVWnJKmsZ/Pmzd2uQZI0jHkJD0lSkUEhSSoyKCRJRQaFJKlodF0asWER8Trgp8CDbaPek5mPbfuKtq2297+Z6mKPjwMnZub/+Q2edyFAZi78jYscBiLiTcADwDGZ+b+6XU/TIuIY4Eyq9ckY4BuZeWFE3AqcBLwLODQzT+hn2kOBLwCT6ulvAc6sz6Ha5ppaxrtlS5+t+n1+PzNf18nzGBSD93hm7tvtIrroJe8/Ir4AfAV4f9cqGn5OBG4APgaM6qCoL/7518D+mbk2IiYDP4iIzMzZdZ8tTTsBWAy8NTMfiYjxVPPr47z4U/lucBlvY1AMgfob5FeAyVSXRv/rzPxyneZvBvYAvgp8D7gUmAb8J9WVc/+lK0UPnTuBoyLiWOBTwI7130mZeWdEfB94Angj1UmUe1GdF7OZ6nIBfVcMPigi7gJ2A64aqVsXEbED8CGq83/uiojfzcx/r785fwV4DvgRsFdmHhoRr2dkLxO7AuOotgjWZua6iPgw1cm0PwcOrfu9PiLupHqf36HaAplEdYuBnQAyc2NEnEb1OaJedh4CDgYmAp/MzO9tm7f1EgMt46cDH6a61NDyzPyziNgb+BrVOvZZqi2Sn0XEH1JdGHUc8Ajw0Tpgfw78LfBuqvkxLzPvq9ctV9fPsww4IjNfHxG/BVwGvKZ+3TMzc0k/65wXRMR+wNfrwRWDmQEeoxi8mRFxf8vfp6k2r/8qM2cB76C6vHqfiZm5V2ZeAlwDzM/M/YGTgb/b5tUPoYgYR7Xy/yeqb89/lJn7AP8D+HRL15WZGVSXZ1kEvCsz30h1fZkj6z6/RTXvDgA+HRE7b5t3MeSOBB7NzJ8CNwJ/Vs+nv6W6r8p+wKaW/iN6mcjMFcBNwMMRsTwizgfGZua/tXX9baqrM+xHdeuBozLzSeDzwI8jYmVEXATMzMyVLdNNqOfNHOCaeqtjmxloGa+/GJwJHEi17PbWW1l/QfWF8UCqLwhvjojp9XTvrpeD7wKtV+Zcm5kHAX8DfKZuuwY4p97CeZgXv9xfBFyZmQcARwGXtXxmWtc5rb7Bi8vaw4OZD25RDN7Ldj1FxFjgDyPiTGBv6m9EtX+u+0wGZgFXtWyKT46IaZm5tvGqh87MiLi/fjwBWA6cQfVN+T1RvblDeelFGf+5/vctwD/17evNzD+BF25gdVtmbgA2RMR/AK8Enmn0nTTjROC6+vG3gG9S7Yb6vy0rwCuBi0bLMpGZfx4Rf0V1LOLdwN0RMbet299n5hqAiLieahm5KTPPi4jLgHfWf7dFxNmZ+aV6usvr17g/IlZTfb7ubfgtdbyM1zdau4tq6/gm4OLMXBURtwAX11sQN1MtA0dQfdNfWv9/j6Xa2u7Td4mjnwB/HBGvBF6XmbfW7VcCp9WPDwfeEBGfq4fHAb9bP+77vL0gInalCuElddPVwJ92OkMMiqFxPfAk1Sb13wEfbBnXd9/BscCzbfs+d+elC8pI0F9QTgbuo/rWfCewEji1pUvfPNjUNt30lsHnWh5vpv+rCA9rEfEqYDZwYL0LpQeYSrWC6G/rfcQvExFxJDA5M78FXEUVeh/l5Suh1v/fHmBTRLyZ6tjGJVThel1EXAd8qf5rn25M23BTBruMv49qd88RwO0RMTczb4iIHwF/BHySarm4GfhhZh5VP+dEoHXL+dn6377l/3m2/DkYC/xBy/1+ZgK/qmvp716n7Z+pQc1Hdz0NjXdSbR7eBPxXeGEr4wWZ+RTws4j4UD3+nVQL3GiwJ9V+0s8Dd1B9YPq7Aco9wMER0XfV4EXAe7dJhdvGh4B/zMzdM/N1mflaqt2Q7wamRsTv1f3mAJtHyTLxn8AX6l/REBE9VMeh2o+zzI6IV9Qrx+OpLhr6BLAwIvZp6ffGtmk/WD/vgVSh+0ATb6ID/S7j9Zedh4AHMvMcquOQe0fEt4CDMvMyqjt07k/1Tf8tEbFn/ZxnAxdu6QXr5ePfIuKIumkO1QqfuoZTAKK6c+hKqmM+W3qutcCjdbD3PVfHDIqhsRD4YUT8mGql8HOqfbLt5gInRcRKqp8EHpeZo+FiWyuoLvz4r8CPgXXAa9s7ZebjVJvO342In1B987lq25XZuBOpbuPb6hJgX6oQ+UZE3Ed1ALLvW9+IXiYycylwLnBzRCTVMjCW+k6WLf4VuJVq+bg5M79XH8c5Afh6RPysnn5/Xro1+jv15+prVPOmW/eZ6XcZr3enXQbcU//fTqXarfN54DN17V8ETs/MXwIfAa6PiAeo3uunBnjdDwPn1M9zMC8uN5+gOu6xkmoX559k5kC7aj8EfDYi/oUXd1N1xIsCSg2LiDFUBzHPzcz19a9kdsvMgVYS27X6V08LM/P7XS6layLiHODyzFwdEX9M9YOIo7d1HR6jkBqWmb0R8QTVt86NVFucHR9I1HbtF8A/RMQmquOgXVlu3KKQJBV5jEKSVGRQSJKKDApJUpFBIUkqMigkSUUGhSSp6P8DALL9i0xdoXAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x=x.index, y=x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### [TOP ☝️](#top)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ece4c4f9c4961c7468795d846fc72c6e00987d764e9134aaba41ea0514f48bf6"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}