liga 1 player analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [],
   "source": [
    "liga1_player = pd.read_csv(\"player_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kit_number</th>\n",
       "      <th>player_name</th>\n",
       "      <th>position</th>\n",
       "      <th>Date of birth / Age</th>\n",
       "      <th>nationality</th>\n",
       "      <th>height</th>\n",
       "      <th>foot</th>\n",
       "      <th>joined</th>\n",
       "      <th>signed_from</th>\n",
       "      <th>contract</th>\n",
       "      <th>market_value</th>\n",
       "      <th>club_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Bayu Aji</td>\n",
       "      <td>Centre-Back</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1,78m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21-Jan-23</td>\n",
       "      <td>NZR Sumbersari FC</td>\n",
       "      <td>-</td>\n",
       "      <td>€10k</td>\n",
       "      <td>Arema FC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Syaeful Anwar</td>\n",
       "      <td>Centre-Back</td>\n",
       "      <td>Dec 1, 1994 (28)</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1,84m</td>\n",
       "      <td>right</td>\n",
       "      <td>27-Apr-22</td>\n",
       "      <td>Persita Tangerang</td>\n",
       "      <td>2024</td>\n",
       "      <td>€125k</td>\n",
       "      <td>Arema FC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>Bagas Adi</td>\n",
       "      <td>Centre-Back</td>\n",
       "      <td>Mar 8, 1997 (26)</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1,76m</td>\n",
       "      <td>left</td>\n",
       "      <td>7-Jan-20</td>\n",
       "      <td>Bhayangkara FC</td>\n",
       "      <td>-</td>\n",
       "      <td>€200k</td>\n",
       "      <td>Arema FC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  kit_number    player_name     position Date of birth / Age nationality  \\\n",
       "0          3       Bayu Aji  Centre-Back                 NaN   Indonesia   \n",
       "1          4  Syaeful Anwar  Centre-Back    Dec 1, 1994 (28)   Indonesia   \n",
       "2          5      Bagas Adi  Centre-Back    Mar 8, 1997 (26)   Indonesia   \n",
       "\n",
       "  height   foot     joined        signed_from contract market_value club_name  \n",
       "0  1,78m    NaN  21-Jan-23  NZR Sumbersari FC        -         €10k  Arema FC  \n",
       "1  1,84m  right  27-Apr-22  Persita Tangerang     2024        €125k  Arema FC  \n",
       "2  1,76m   left   7-Jan-20     Bhayangkara FC        -        €200k  Arema FC  "
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liga1_player.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 568 entries, 0 to 567\n",
      "Data columns (total 12 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   kit_number           568 non-null    object\n",
      " 1   player_name          568 non-null    object\n",
      " 2   position             568 non-null    object\n",
      " 3   Date of birth / Age  550 non-null    object\n",
      " 4   nationality          568 non-null    object\n",
      " 5   height               567 non-null    object\n",
      " 6   foot                 522 non-null    object\n",
      " 7   joined               568 non-null    object\n",
      " 8   signed_from          564 non-null    object\n",
      " 9   contract             538 non-null    object\n",
      " 10  market_value         567 non-null    object\n",
      " 11  club_name            568 non-null    object\n",
      "dtypes: object(12)\n",
      "memory usage: 53.4+ KB\n"
     ]
    }
   ],
   "source": [
    "liga1_player.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*There are 568 players in Liga 1 Indonesia*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [],
   "source": [
    "liga1_player[\"birth_date\"] = liga1_player[\"Date of birth / Age\"].str[:-5] #Get birth date string values\n",
    "liga1_player['birth_date'] = pd.to_datetime(liga1_player['birth_date']) #Convert to datetime type\n",
    "\n",
    "liga1_player[\"age\"] = liga1_player[\"Date of birth / Age\"].str[-3:-1] #Get age string values\n",
    "liga1_player['age'] = round(pd.to_numeric(liga1_player['age']),0) #Convert to numeric\n",
    "\n",
    "liga1_player[\"height\"] = liga1_player[\"height\"].replace([\"-\",\"-.\"], 0) #Replace \"-\" values to 0\n",
    "liga1_player[\"height\"] = liga1_player[\"height\"].str[:1] + \".\" + liga1_player[\"height\"].str[2:4] #Get only the height number from string values\n",
    "liga1_player[\"height\"] = pd.to_numeric(liga1_player[\"height\"]) #Convert to numeric\n",
    "liga1_player.rename(columns = {'height':'height_M'}, inplace = True)\n",
    "\n",
    "liga1_player[\"joined\"] = liga1_player[\"joined\"].replace(\"-\",0) #Replace \"-\" to 0\n",
    "liga1_player[\"joined\"] = pd.to_datetime(liga1_player[\"joined\"]) #Convert to datetime type\n",
    "\n",
    "liga1_player[\"market_value\"] = liga1_player[\"market_value\"].replace(\"-\", 0) \n",
    "liga1_player[\"market_value\"] = liga1_player[\"market_value\"].str[1:-1] #Get only the market value number from string values\n",
    "liga1_player[\"market_value\"] = pd.to_numeric(liga1_player[\"market_value\"]) #Convert to numeric type\n",
    "liga1_player[\"market_value\"] = liga1_player[\"market_value\"]*1000\n",
    "liga1_player.rename(columns = {'market_value':'market_value_EUR'}, inplace = True)\n",
    "\n",
    "liga1_player = liga1_player.drop(columns='Date of birth / Age')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Centre-Back           91\n",
       "Goalkeeper            69\n",
       "Centre-Forward        65\n",
       "Defensive Midfield    62\n",
       "Attacking Midfield    61\n",
       "Right Winger          51\n",
       "Right-Back            45\n",
       "Left-Back             41\n",
       "Left Winger           34\n",
       "Central Midfield      32\n",
       "Midfield               4\n",
       "Goal Keeper            3\n",
       "Attack                 2\n",
       "Center-Back            2\n",
       "Second Striker         2\n",
       "Left Midfield          1\n",
       "Defence                1\n",
       "Centre Midfield        1\n",
       "Center Midfield        1\n",
       "Name: position, dtype: int64"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liga1_player.position.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correcting data in player position\n",
    "liga1_player[\"position\"] = liga1_player[\"position\"].replace([\"Centre Midfield\", \"Center Midfield\"], \"Central Midfield\")\n",
    "liga1_player[\"position\"] = liga1_player[\"position\"].replace(\"Goal Keeper\", \"Goalkeeper\")\n",
    "liga1_player[\"position\"] = liga1_player[\"position\"].replace(\"Center-Back\",\"Centre-Back\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-            303\n",
       "2024          56\n",
       "31-May-24     31\n",
       "30-Jun-24     19\n",
       "30-Apr-24     15\n",
       "31-May-25     12\n",
       "30-Jun-25     11\n",
       "31-Dec-23     10\n",
       "31-May-26     10\n",
       "2026           8\n",
       "31-Mar-24      8\n",
       "31-Mar-23      6\n",
       "2025           6\n",
       "30-Apr-25      4\n",
       "30-Jun-26      3\n",
       "30-Apr-27      3\n",
       "31-Dec-25      3\n",
       "1-Jun-24       3\n",
       "16-Jul-05      3\n",
       "28-Feb-23      2\n",
       "30-Apr-26      2\n",
       "1-Jul-24       2\n",
       "31-Dec-24      2\n",
       "5-Oct-24       1\n",
       "1-Apr-26       1\n",
       "27-Apr-24      1\n",
       "20-May-25      1\n",
       "28-Feb-27      1\n",
       "14-May-23      1\n",
       "15-Jun-24      1\n",
       "31-Jan-25      1\n",
       "14-May-24      1\n",
       "27-Jan-25      1\n",
       "14-Jun-26      1\n",
       "30-Jun-23      1\n",
       "Dec, 2023      1\n",
       "3-Oct-24       1\n",
       "20-Jul-23      1\n",
       "1-Mar-25       1\n",
       "Name: contract, dtype: int64"
      ]
     },
     "execution_count": 416,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liga1_player.contract.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "liga1_player[\"contract\"] = liga1_player[\"contract\"].replace(\"2024\",\"31-Dec-24\")\n",
    "liga1_player[\"contract\"] = liga1_player[\"contract\"].replace(\"2025\",\"31-Dec-25\")\n",
    "liga1_player[\"contract\"] = liga1_player[\"contract\"].replace(\"2026\",\"31-Dec-26\")\n",
    "liga1_player[\"contract\"] = liga1_player[\"contract\"].replace(\"Dec, 2023\",\"31-Dec-23\")\n",
    "liga1_player[\"contract\"] = liga1_player[\"contract\"].replace(\"-\",np.nan)\n",
    "liga1_player[\"contract\"] = pd.to_datetime(liga1_player[\"contract\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Acer\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>first</th>\n",
       "      <th>last</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>kit_number</th>\n",
       "      <td>568</td>\n",
       "      <td>93</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>player_name</th>\n",
       "      <td>568</td>\n",
       "      <td>564</td>\n",
       "      <td>Ferdiansyah</td>\n",
       "      <td>2</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>position</th>\n",
       "      <td>568</td>\n",
       "      <td>15</td>\n",
       "      <td>Centre-Back</td>\n",
       "      <td>93</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nationality</th>\n",
       "      <td>568</td>\n",
       "      <td>35</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>461</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height_M</th>\n",
       "      <td>531.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.758173</td>\n",
       "      <td>0.07345</td>\n",
       "      <td>1.58</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.75</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foot</th>\n",
       "      <td>522</td>\n",
       "      <td>4</td>\n",
       "      <td>right</td>\n",
       "      <td>382</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>joined</th>\n",
       "      <td>568</td>\n",
       "      <td>196</td>\n",
       "      <td>2023-07-01 00:00:00</td>\n",
       "      <td>191</td>\n",
       "      <td>1970-01-01</td>\n",
       "      <td>2023-07-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>signed_from</th>\n",
       "      <td>564</td>\n",
       "      <td>197</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>35</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contract</th>\n",
       "      <td>235</td>\n",
       "      <td>35</td>\n",
       "      <td>2024-12-31 00:00:00</td>\n",
       "      <td>58</td>\n",
       "      <td>2005-07-16</td>\n",
       "      <td>2027-04-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>market_value_EUR</th>\n",
       "      <td>504.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>140228.174603</td>\n",
       "      <td>92810.638571</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>club_name</th>\n",
       "      <td>568</td>\n",
       "      <td>18</td>\n",
       "      <td>Bhayangkara FC</td>\n",
       "      <td>35</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>birth_date</th>\n",
       "      <td>550</td>\n",
       "      <td>530</td>\n",
       "      <td>1998-05-04 00:00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>1980-12-31</td>\n",
       "      <td>2007-09-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>25.678182</td>\n",
       "      <td>4.913836</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count unique                  top freq      first  \\\n",
       "kit_number          568     93                   23   17        NaT   \n",
       "player_name         568    564          Ferdiansyah    2        NaT   \n",
       "position            568     15          Centre-Back   93        NaT   \n",
       "nationality         568     35            Indonesia  461        NaT   \n",
       "height_M          531.0    NaN                  NaN  NaN        NaT   \n",
       "foot                522      4                right  382        NaT   \n",
       "joined              568    196  2023-07-01 00:00:00  191 1970-01-01   \n",
       "signed_from         564    197         Without Club   35        NaT   \n",
       "contract            235     35  2024-12-31 00:00:00   58 2005-07-16   \n",
       "market_value_EUR  504.0    NaN                  NaN  NaN        NaT   \n",
       "club_name           568     18       Bhayangkara FC   35        NaT   \n",
       "birth_date          550    530  1998-05-04 00:00:00    2 1980-12-31   \n",
       "age               550.0    NaN                  NaN  NaN        NaT   \n",
       "\n",
       "                       last           mean           std      min      25%  \\\n",
       "kit_number              NaT            NaN           NaN      NaN      NaN   \n",
       "player_name             NaT            NaN           NaN      NaN      NaN   \n",
       "position                NaT            NaN           NaN      NaN      NaN   \n",
       "nationality             NaT            NaN           NaN      NaN      NaN   \n",
       "height_M                NaT       1.758173       0.07345     1.58      1.7   \n",
       "foot                    NaT            NaN           NaN      NaN      NaN   \n",
       "joined           2023-07-20            NaN           NaN      NaN      NaN   \n",
       "signed_from             NaT            NaN           NaN      NaN      NaN   \n",
       "contract         2027-04-30            NaN           NaN      NaN      NaN   \n",
       "market_value_EUR        NaT  140228.174603  92810.638571  10000.0  75000.0   \n",
       "club_name               NaT            NaN           NaN      NaN      NaN   \n",
       "birth_date       2007-09-02            NaN           NaN      NaN      NaN   \n",
       "age                     NaT      25.678182      4.913836     15.0     22.0   \n",
       "\n",
       "                       50%       75%       max  \n",
       "kit_number             NaN       NaN       NaN  \n",
       "player_name            NaN       NaN       NaN  \n",
       "position               NaN       NaN       NaN  \n",
       "nationality            NaN       NaN       NaN  \n",
       "height_M              1.75      1.81      1.98  \n",
       "foot                   NaN       NaN       NaN  \n",
       "joined                 NaN       NaN       NaN  \n",
       "signed_from            NaN       NaN       NaN  \n",
       "contract               NaN       NaN       NaN  \n",
       "market_value_EUR  125000.0  200000.0  600000.0  \n",
       "club_name              NaN       NaN       NaN  \n",
       "birth_date             NaN       NaN       NaN  \n",
       "age                   25.0      29.0      42.0  "
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liga1_player.describe(include=\"all\").T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 568 entries, 0 to 567\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   kit_number        568 non-null    object        \n",
      " 1   player_name       568 non-null    object        \n",
      " 2   position          568 non-null    object        \n",
      " 3   nationality       568 non-null    object        \n",
      " 4   height_M          531 non-null    float64       \n",
      " 5   foot              522 non-null    object        \n",
      " 6   joined            568 non-null    datetime64[ns]\n",
      " 7   signed_from       564 non-null    object        \n",
      " 8   contract          235 non-null    datetime64[ns]\n",
      " 9   market_value_EUR  504 non-null    float64       \n",
      " 10  club_name         568 non-null    object        \n",
      " 11  birth_date        550 non-null    datetime64[ns]\n",
      " 12  age               550 non-null    float64       \n",
      "dtypes: datetime64[ns](3), float64(3), object(7)\n",
      "memory usage: 57.8+ KB\n"
     ]
    }
   ],
   "source": [
    "liga1_player.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "kit_number            0\n",
       "player_name           0\n",
       "position              0\n",
       "nationality           0\n",
       "height_M             37\n",
       "foot                 46\n",
       "joined                0\n",
       "signed_from           4\n",
       "contract            333\n",
       "market_value_EUR     64\n",
       "club_name             0\n",
       "birth_date           18\n",
       "age                  18\n",
       "dtype: int64"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liga1_player.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Player age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adb9c5e688>"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.countplot(data=liga1_player, x=\"age\", palette=\"crest\", )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {
    "scrolled": true
   },
   "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>player_name</th>\n",
       "      <th>position</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club_name</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Beto</td>\n",
       "      <td>Centre-Forward</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Madura United FC</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>Wawan Hendrawan</td>\n",
       "      <td>Goalkeeper</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Madura United FC</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>Maman Abdurahman</td>\n",
       "      <td>Centre-Back</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Persija Jakarta</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>Arkhan Kaka</td>\n",
       "      <td>Centre-Forward</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Persis Solo</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          player_name        position nationality         club_name   age\n",
       "165              Beto  Centre-Forward   Indonesia  Madura United FC  42.0\n",
       "190   Wawan Hendrawan      Goalkeeper   Indonesia  Madura United FC  40.0\n",
       "276  Maman Abdurahman     Centre-Back   Indonesia   Persija Jakarta  41.0\n",
       "375       Arkhan Kaka  Centre-Forward   Indonesia       Persis Solo  15.0"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liga1_player[(liga1_player.age == 15)|(liga1_player.age >= 40)][[\"player_name\",\"position\",\"nationality\",\"club_name\",\"age\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adb9db5d48>"
      ]
     },
     "execution_count": 423,
     "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": [
    "young_player = liga1_player[liga1_player.age <= 21][[\"player_name\",\"position\",\"nationality\",\"club_name\",\"age\"]]\n",
    "sns.countplot(data=young_player, y=\"club_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "liga1_player[\"young_player\"] = liga1_player.apply(lambda x: \"no\" if x['age'] > 21 else \"yes\", axis=1)\n",
    "plt.figure(figsize=(10,10))\n",
    "ax = sns.countplot(data=liga1_player, y=\"club_name\", hue=\"young_player\", palette=\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "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>kit_number</th>\n",
       "      <th>player_name</th>\n",
       "      <th>position</th>\n",
       "      <th>nationality</th>\n",
       "      <th>height_M</th>\n",
       "      <th>foot</th>\n",
       "      <th>joined</th>\n",
       "      <th>signed_from</th>\n",
       "      <th>contract</th>\n",
       "      <th>market_value_EUR</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>age</th>\n",
       "      <th>young_player</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>club_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arema FC</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bali United FC</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhayangkara FC</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borneo FC Samarinda</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dewa United FC</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madura United FC</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PS Barito Putra</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSIS Semarang</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSM Makassar</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSS Sleman</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Persebaya Surabaya</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Persib Bandung</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Persija Jakarta</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Persik Kediri</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Persikabo 1973</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Persis Solo</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Persita Tangerang</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RANS Nusantara FC</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     kit_number  player_name  position  nationality  height_M  \\\n",
       "club_name                                                                       \n",
       "Arema FC                      7            7         7            7         7   \n",
       "Bali United FC                7            7         7            7         7   \n",
       "Bhayangkara FC                9            9         9            9         9   \n",
       "Borneo FC Samarinda          11           11        11           11        11   \n",
       "Dewa United FC                5            5         5            5         5   \n",
       "Madura United FC             10           10        10           10         6   \n",
       "PS Barito Putra               7            7         7            7         6   \n",
       "PSIS Semarang                 9            9         9            9         7   \n",
       "PSM Makassar                 13           13        13           13        11   \n",
       "PSS Sleman                    8            8         8            8         7   \n",
       "Persebaya Surabaya            9            9         9            9         8   \n",
       "Persib Bandung                6            6         6            6         5   \n",
       "Persija Jakarta              12           12        12           12        11   \n",
       "Persik Kediri                 4            4         4            4         4   \n",
       "Persikabo 1973                9            9         9            9         6   \n",
       "Persis Solo                  10           10        10           10        10   \n",
       "Persita Tangerang            13           13        13           13         3   \n",
       "RANS Nusantara FC             6            6         6            6         3   \n",
       "\n",
       "                     foot  joined  signed_from  contract  market_value_EUR  \\\n",
       "club_name                                                                    \n",
       "Arema FC                6       7            7         3                 7   \n",
       "Bali United FC          5       7            7         1                 6   \n",
       "Bhayangkara FC          8       9            9         1                 5   \n",
       "Borneo FC Samarinda     9      11           10        11                 9   \n",
       "Dewa United FC          5       5            5         3                 3   \n",
       "Madura United FC        5      10           10         4                 5   \n",
       "PS Barito Putra         6       7            7         3                 7   \n",
       "PSIS Semarang           6       9            9         0                 5   \n",
       "PSM Makassar           10      13           13         8                11   \n",
       "PSS Sleman              5       8            8         2                 5   \n",
       "Persebaya Surabaya      5       9            9         1                 5   \n",
       "Persib Bandung          5       6            6         0                 5   \n",
       "Persija Jakarta        12      12           11         6                10   \n",
       "Persik Kediri           4       4            4         1                 4   \n",
       "Persikabo 1973          5       9            9         3                 6   \n",
       "Persis Solo            10      10            8         9                 7   \n",
       "Persita Tangerang       5      13           13         0                 7   \n",
       "RANS Nusantara FC       4       6            6         2                 3   \n",
       "\n",
       "                     birth_date  age  young_player  \n",
       "club_name                                           \n",
       "Arema FC                      6    6             7  \n",
       "Bali United FC                6    6             7  \n",
       "Bhayangkara FC                7    7             9  \n",
       "Borneo FC Samarinda          11   11            11  \n",
       "Dewa United FC                4    4             5  \n",
       "Madura United FC              6    6            10  \n",
       "PS Barito Putra               7    7             7  \n",
       "PSIS Semarang                 8    8             9  \n",
       "PSM Makassar                 12   12            13  \n",
       "PSS Sleman                    8    8             8  \n",
       "Persebaya Surabaya            8    8             9  \n",
       "Persib Bandung                5    5             6  \n",
       "Persija Jakarta              12   12            12  \n",
       "Persik Kediri                 4    4             4  \n",
       "Persikabo 1973                7    7             9  \n",
       "Persis Solo                  10   10            10  \n",
       "Persita Tangerang            11   11            13  \n",
       "RANS Nusantara FC             5    5             6  "
      ]
     },
     "execution_count": 425,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liga1_player[liga1_player[\"young_player\"]==\"yes\"].groupby(by=\"club_name\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "liga1_player[\"young_player\"] = liga1_player.apply(lambda x: \"no\" if x['age'] > 21 else \"yes\", axis=1)\n",
    "plt.figure(figsize=(10,10))\n",
    "ax = sns.countplot(data=liga1_player, y=\"club_name\", hue=\"young_player\", palette=\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adb9f8ae08>"
      ]
     },
     "execution_count": 427,
     "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": [
    "sns.boxplot(liga1_player.age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PSM Makassar           12\n",
       "Persija Jakarta        12\n",
       "Borneo FC Samarinda    11\n",
       "Persita Tangerang      11\n",
       "Persis Solo            10\n",
       "PSIS Semarang           8\n",
       "PSS Sleman              8\n",
       "Persebaya Surabaya      8\n",
       "Bhayangkara FC          7\n",
       "Persikabo 1973          7\n",
       "PS Barito Putra         7\n",
       "Arema FC                6\n",
       "Madura United FC        6\n",
       "Bali United FC          6\n",
       "RANS Nusantara FC       5\n",
       "Persib Bandung          5\n",
       "Dewa United FC          4\n",
       "Persik Kediri           4\n",
       "Name: club_name, dtype: int64"
      ]
     },
     "execution_count": 428,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liga1_player[liga1_player.age <= 21][\"club_name\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adba259a88>"
      ]
     },
     "execution_count": 429,
     "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": [
    "# youngest player\n",
    "sns.barplot(data=liga1_player.sort_values(\"age\")[:10], x=\"age\", y=\"player_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adba30e348>"
      ]
     },
     "execution_count": 430,
     "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": [
    "# oldest player\n",
    "sns.barplot(data=liga1_player.sort_values(\"age\", ascending=False)[:5], x=\"age\", y=\"player_name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# nasionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "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>player_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>club_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arema FC</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bali United FC</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhayangkara FC</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Borneo FC Samarinda</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dewa United FC</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Madura United FC</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PS Barito Putra</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSIS Semarang</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSM Makassar</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSS Sleman</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Persebaya Surabaya</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Persib Bandung</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Persija Jakarta</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Persik Kediri</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Persikabo 1973</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Persis Solo</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Persita Tangerang</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RANS Nusantara FC</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     player_name\n",
       "club_name                       \n",
       "Arema FC                       6\n",
       "Bali United FC                 6\n",
       "Bhayangkara FC                 6\n",
       "Borneo FC Samarinda            6\n",
       "Dewa United FC                 6\n",
       "Madura United FC               6\n",
       "PS Barito Putra                6\n",
       "PSIS Semarang                  6\n",
       "PSM Makassar                   6\n",
       "PSS Sleman                     6\n",
       "Persebaya Surabaya             6\n",
       "Persib Bandung                 6\n",
       "Persija Jakarta                5\n",
       "Persik Kediri                  6\n",
       "Persikabo 1973                 6\n",
       "Persis Solo                    6\n",
       "Persita Tangerang              6\n",
       "RANS Nusantara FC              6"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# liga1_player[\"foreign_player\"] = liga1_player.apply(lambda x: \"no\" if x['nationality'] == \"Indonesia\" else \"yes\", axis=1)\n",
    "liga1_player[liga1_player.nationality != \"Indonesia\"].groupby(by=\"club_name\").count()[[\"player_name\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adba37d2c8>"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the top five foreign players\n",
    "plt.figure(figsize=(10,12))\n",
    "sns.barplot(data = pd.DataFrame(liga1_player.nationality.value_counts()[1:]).reset_index(), y=\"index\", x=\"nationality\", palette=\"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "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>index</th>\n",
       "      <th>nationality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Japan</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mali</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Spain</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cape Verde</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Myanmar</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>France</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Korea, South</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Poland</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Croatia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Palestine</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Greece</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>North Macedonia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Malaysia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              index  nationality\n",
       "0            Brazil           31\n",
       "1       Philippines           13\n",
       "2             Japan            8\n",
       "3          Portugal            8\n",
       "4         Argentina            7\n",
       "5       Netherlands            4\n",
       "6              Mali            3\n",
       "7             Spain            3\n",
       "8        Cape Verde            2\n",
       "9            Serbia            2\n",
       "10        Singapore            2\n",
       "11          Lebanon            2\n",
       "12          Uruguay            1\n",
       "13          Myanmar            1\n",
       "14          Ukraine            1\n",
       "15           France            1\n",
       "16      Timor-Leste            1\n",
       "17     Korea, South            1\n",
       "18       Uzbekistan            1\n",
       "19        Australia            1\n",
       "20            Nepal            1\n",
       "21   Czech Republic            1\n",
       "22           Poland            1\n",
       "23          Croatia            1\n",
       "24         Thailand            1\n",
       "25            Gabon            1\n",
       "26         Cameroon            1\n",
       "27           Mexico            1\n",
       "28        Palestine            1\n",
       "29           Greece            1\n",
       "30      Afghanistan            1\n",
       "31  North Macedonia            1\n",
       "32         Malaysia            1\n",
       "33           Angola            1"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liga1_player.nationality.value_counts()[1:].reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# foot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adba6ad708>"
      ]
     },
     "execution_count": 434,
     "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": [
    "sns.countplot(data=liga1_player, x=\"foot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# joined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adba7011c8>"
      ]
     },
     "execution_count": 435,
     "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": [
    "sns.scatterplot(data = liga1_player.joined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adb6920948>"
      ]
     },
     "execution_count": 502,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# error joined database\n",
    "import datetime\n",
    "plt.figure(figsize=(8,6))b\n",
    "sns.countplot(data=liga1_player[liga1_player[\"joined\"] > datetime.datetime(2023,1,1)], y=\"club_name\", color=\"royalblue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RANS Nusantara FC      23\n",
       "Arema FC               21\n",
       "Persebaya Surabaya     19\n",
       "Persikabo 1973         19\n",
       "PSIS Semarang          19\n",
       "PSS Sleman             18\n",
       "Dewa United FC         17\n",
       "Madura United FC       15\n",
       "Borneo FC Samarinda    14\n",
       "Persita Tangerang      13\n",
       "PS Barito Putra        13\n",
       "Persik Kediri          13\n",
       "Bhayangkara FC         12\n",
       "Persija Jakarta        11\n",
       "Bali United FC          8\n",
       "Persis Solo             8\n",
       "Persib Bandung          7\n",
       "PSM Makassar            7\n",
       "Name: club_name, dtype: int64"
      ]
     },
     "execution_count": 503,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liga1_player[liga1_player[\"joined\"] > datetime.datetime(2023,1,1)][\"club_name\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adba9243c8>"
      ]
     },
     "execution_count": 437,
     "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": [
    "sns.scatterplot(data = liga1_player[liga1_player[\"joined\"] > datetime.datetime(2000,1,1)][\"joined\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "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>kit_number</th>\n",
       "      <th>player_name</th>\n",
       "      <th>position</th>\n",
       "      <th>nationality</th>\n",
       "      <th>height_M</th>\n",
       "      <th>foot</th>\n",
       "      <th>joined</th>\n",
       "      <th>signed_from</th>\n",
       "      <th>contract</th>\n",
       "      <th>market_value_EUR</th>\n",
       "      <th>club_name</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>age</th>\n",
       "      <th>young_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>26</td>\n",
       "      <td>Andritany</td>\n",
       "      <td>Goalkeeper</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.78</td>\n",
       "      <td>right</td>\n",
       "      <td>2010-07-01</td>\n",
       "      <td>Sriwijaya FC</td>\n",
       "      <td>2026-06-30</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>Persija Jakarta</td>\n",
       "      <td>1991-12-26</td>\n",
       "      <td>31.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>17</td>\n",
       "      <td>Rasyid Bakri</td>\n",
       "      <td>Central Midfield</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.64</td>\n",
       "      <td>right</td>\n",
       "      <td>2011-07-01</td>\n",
       "      <td>PSM Makassar Youth</td>\n",
       "      <td>2024-12-31</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>PSM Makassar</td>\n",
       "      <td>1991-01-17</td>\n",
       "      <td>32.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>87</td>\n",
       "      <td>Johan Alfarizi</td>\n",
       "      <td>Left-Back</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.73</td>\n",
       "      <td>left</td>\n",
       "      <td>2013-07-01</td>\n",
       "      <td>Arema Indonesia</td>\n",
       "      <td>NaT</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>Arema FC</td>\n",
       "      <td>1990-05-25</td>\n",
       "      <td>33.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>41</td>\n",
       "      <td>Dendi Santoso</td>\n",
       "      <td>Right Winger</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.72</td>\n",
       "      <td>both</td>\n",
       "      <td>2013-07-01</td>\n",
       "      <td>Arema Indonesia</td>\n",
       "      <td>NaT</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>Arema FC</td>\n",
       "      <td>1990-05-16</td>\n",
       "      <td>33.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>23</td>\n",
       "      <td>Wahyu Subo Seto</td>\n",
       "      <td>Attacking Midfield</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.68</td>\n",
       "      <td>right</td>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>Surabaya United Youth</td>\n",
       "      <td>NaT</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>Bhayangkara FC</td>\n",
       "      <td>1993-07-16</td>\n",
       "      <td>29.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>5</td>\n",
       "      <td>Fatchu Rochman</td>\n",
       "      <td>Left-Back</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.70</td>\n",
       "      <td>both</td>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>Persekap Pasuruan</td>\n",
       "      <td>NaT</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>Bhayangkara FC</td>\n",
       "      <td>1995-06-28</td>\n",
       "      <td>28.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>8</td>\n",
       "      <td>Muhammad Hargianto</td>\n",
       "      <td>Defensive Midfield</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.68</td>\n",
       "      <td>right</td>\n",
       "      <td>2014-11-11</td>\n",
       "      <td>Diklat Ragunan</td>\n",
       "      <td>NaT</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>Bhayangkara FC</td>\n",
       "      <td>1996-07-24</td>\n",
       "      <td>26.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>28</td>\n",
       "      <td>Terens Puhiri</td>\n",
       "      <td>Right Winger</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.67</td>\n",
       "      <td>right</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>Persisam Putra Samarinda (- 2014)</td>\n",
       "      <td>2024-04-30</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>Borneo FC Samarinda</td>\n",
       "      <td>1996-10-13</td>\n",
       "      <td>26.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>14</td>\n",
       "      <td>Fadil Sausu</td>\n",
       "      <td>Central Midfield</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.74</td>\n",
       "      <td>left</td>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>Persisam Putra Samarinda (- 2014)</td>\n",
       "      <td>2024-12-31</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>Bali United FC</td>\n",
       "      <td>1985-04-19</td>\n",
       "      <td>38.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>11</td>\n",
       "      <td>Yabes Roni</td>\n",
       "      <td>Left Winger</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.68</td>\n",
       "      <td>both</td>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>NaT</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>Bali United FC</td>\n",
       "      <td>1995-02-06</td>\n",
       "      <td>28.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    kit_number         player_name            position nationality  height_M  \\\n",
       "272         26           Andritany          Goalkeeper   Indonesia      1.78   \n",
       "485         17        Rasyid Bakri    Central Midfield   Indonesia      1.64   \n",
       "30          87      Johan Alfarizi           Left-Back   Indonesia      1.73   \n",
       "24          41       Dendi Santoso        Right Winger   Indonesia      1.72   \n",
       "83          23     Wahyu Subo Seto  Attacking Midfield   Indonesia      1.68   \n",
       "68           5      Fatchu Rochman           Left-Back   Indonesia      1.70   \n",
       "71           8  Muhammad Hargianto  Defensive Midfield   Indonesia      1.68   \n",
       "118         28       Terens Puhiri        Right Winger   Indonesia      1.67   \n",
       "41          14         Fadil Sausu    Central Midfield   Indonesia      1.74   \n",
       "39          11          Yabes Roni         Left Winger   Indonesia      1.68   \n",
       "\n",
       "      foot     joined                        signed_from   contract  \\\n",
       "272  right 2010-07-01                       Sriwijaya FC 2026-06-30   \n",
       "485  right 2011-07-01                 PSM Makassar Youth 2024-12-31   \n",
       "30    left 2013-07-01                    Arema Indonesia        NaT   \n",
       "24    both 2013-07-01                    Arema Indonesia        NaT   \n",
       "83   right 2014-01-01              Surabaya United Youth        NaT   \n",
       "68    both 2014-11-01                  Persekap Pasuruan        NaT   \n",
       "71   right 2014-11-11                     Diklat Ragunan        NaT   \n",
       "118  right 2015-01-01  Persisam Putra Samarinda (- 2014) 2024-04-30   \n",
       "41    left 2015-01-13  Persisam Putra Samarinda (- 2014) 2024-12-31   \n",
       "39    both 2015-01-13                       Without Club        NaT   \n",
       "\n",
       "     market_value_EUR            club_name birth_date   age young_player  \n",
       "272          250000.0      Persija Jakarta 1991-12-26  31.0           no  \n",
       "485          150000.0         PSM Makassar 1991-01-17  32.0           no  \n",
       "30           175000.0             Arema FC 1990-05-25  33.0           no  \n",
       "24           125000.0             Arema FC 1990-05-16  33.0           no  \n",
       "83           200000.0       Bhayangkara FC 1993-07-16  29.0           no  \n",
       "68           125000.0       Bhayangkara FC 1995-06-28  28.0           no  \n",
       "71           225000.0       Bhayangkara FC 1996-07-24  26.0           no  \n",
       "118          200000.0  Borneo FC Samarinda 1996-10-13  26.0           no  \n",
       "41            50000.0       Bali United FC 1985-04-19  38.0           no  \n",
       "39           175000.0       Bali United FC 1995-02-06  28.0           no  "
      ]
     },
     "execution_count": 438,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# veteran player in the club\n",
    "liga1_player.sort_values(by=\"joined\")[5:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kit_number</th>\n",
       "      <th>player_name</th>\n",
       "      <th>position</th>\n",
       "      <th>nationality</th>\n",
       "      <th>height_M</th>\n",
       "      <th>foot</th>\n",
       "      <th>joined</th>\n",
       "      <th>signed_from</th>\n",
       "      <th>contract</th>\n",
       "      <th>market_value_EUR</th>\n",
       "      <th>club_name</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>age</th>\n",
       "      <th>young_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>-</td>\n",
       "      <td>Levy Madinda</td>\n",
       "      <td>Attacking Midfield</td>\n",
       "      <td>Gabon</td>\n",
       "      <td>1.82</td>\n",
       "      <td>right</td>\n",
       "      <td>2023-07-20</td>\n",
       "      <td>Johor Darul Ta'zim</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>Persib Bandung</td>\n",
       "      <td>1992-06-11</td>\n",
       "      <td>31.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>68</td>\n",
       "      <td>Daffa Salman</td>\n",
       "      <td>Centre-Back</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>right</td>\n",
       "      <td>2023-07-20</td>\n",
       "      <td>Persela Lamongan</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PSM Makassar</td>\n",
       "      <td>2002-04-30</td>\n",
       "      <td>21.0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>3</td>\n",
       "      <td>Junior Eldstal</td>\n",
       "      <td>Centre-Back</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>1.91</td>\n",
       "      <td>right</td>\n",
       "      <td>2023-07-20</td>\n",
       "      <td>Johor Darul Ta'zim</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>Dewa United FC</td>\n",
       "      <td>1991-09-16</td>\n",
       "      <td>31.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>22</td>\n",
       "      <td>Oliver Bias</td>\n",
       "      <td>Right Winger</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>1.64</td>\n",
       "      <td>both</td>\n",
       "      <td>2023-07-20</td>\n",
       "      <td>FK Pribram</td>\n",
       "      <td>2024-06-30</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>Persija Jakarta</td>\n",
       "      <td>2001-06-15</td>\n",
       "      <td>22.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>86</td>\n",
       "      <td>Greg Nwokolo</td>\n",
       "      <td>Left Winger</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.75</td>\n",
       "      <td>right</td>\n",
       "      <td>2023-07-20</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>NaT</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>Arema FC</td>\n",
       "      <td>1986-01-03</td>\n",
       "      <td>37.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>16</td>\n",
       "      <td>Kafiatur Rizky</td>\n",
       "      <td>Central Midfield</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.72</td>\n",
       "      <td>left</td>\n",
       "      <td>2023-07-20</td>\n",
       "      <td>Borneo FC Samarinda Youth</td>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dewa United FC</td>\n",
       "      <td>2006-07-17</td>\n",
       "      <td>17.0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>18</td>\n",
       "      <td>Julian Schwarzer</td>\n",
       "      <td>Goalkeeper</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>1.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-07-20</td>\n",
       "      <td>Kuching City</td>\n",
       "      <td>NaT</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>Arema FC</td>\n",
       "      <td>1999-10-26</td>\n",
       "      <td>23.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>91</td>\n",
       "      <td>Felipe Cadenazzi</td>\n",
       "      <td>Centre-Forward</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.91</td>\n",
       "      <td>left</td>\n",
       "      <td>2023-07-20</td>\n",
       "      <td>Magallanes CF</td>\n",
       "      <td>NaT</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>Borneo FC Samarinda</td>\n",
       "      <td>1991-10-12</td>\n",
       "      <td>31.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2</td>\n",
       "      <td>Alef</td>\n",
       "      <td>Centre-Back</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.86</td>\n",
       "      <td>right</td>\n",
       "      <td>2023-07-20</td>\n",
       "      <td>Khon Kaen United</td>\n",
       "      <td>NaT</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>Bhayangkara FC</td>\n",
       "      <td>1993-09-10</td>\n",
       "      <td>29.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>77</td>\n",
       "      <td>Syamil Bahij</td>\n",
       "      <td>Goalkeeper</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-07-14</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>2005-07-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PSM Makassar</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    kit_number       player_name            position  nationality  height_M  \\\n",
       "225          -      Levy Madinda  Attacking Midfield        Gabon      1.82   \n",
       "500         68      Daffa Salman         Centre-Back    Indonesia       NaN   \n",
       "131          3    Junior Eldstal         Centre-Back     Malaysia      1.91   \n",
       "268         22       Oliver Bias        Right Winger  Philippines      1.64   \n",
       "29          86      Greg Nwokolo         Left Winger    Indonesia      1.75   \n",
       "143         16    Kafiatur Rizky    Central Midfield    Indonesia      1.72   \n",
       "12          18  Julian Schwarzer          Goalkeeper  Philippines      1.81   \n",
       "127         91  Felipe Cadenazzi      Centre-Forward    Argentina      1.91   \n",
       "65           2              Alef         Centre-Back       Brazil      1.86   \n",
       "503         77      Syamil Bahij          Goalkeeper    Indonesia      1.80   \n",
       "\n",
       "      foot     joined                signed_from   contract  market_value_EUR  \\\n",
       "225  right 2023-07-20         Johor Darul Ta'zim 2023-12-31          175000.0   \n",
       "500  right 2023-07-20           Persela Lamongan        NaT               NaN   \n",
       "131  right 2023-07-20         Johor Darul Ta'zim 2023-12-31          150000.0   \n",
       "268   both 2023-07-20                 FK Pribram 2024-06-30           75000.0   \n",
       "29   right 2023-07-20               Without Club        NaT          100000.0   \n",
       "143   left 2023-07-20  Borneo FC Samarinda Youth 2024-03-31               NaN   \n",
       "12     NaN 2023-07-20               Kuching City        NaT          100000.0   \n",
       "127   left 2023-07-20              Magallanes CF        NaT          225000.0   \n",
       "65   right 2023-07-20           Khon Kaen United        NaT          250000.0   \n",
       "503    NaN 2023-07-14               Without Club 2005-07-16               NaN   \n",
       "\n",
       "               club_name birth_date   age young_player  \n",
       "225       Persib Bandung 1992-06-11  31.0           no  \n",
       "500         PSM Makassar 2002-04-30  21.0          yes  \n",
       "131       Dewa United FC 1991-09-16  31.0           no  \n",
       "268      Persija Jakarta 2001-06-15  22.0           no  \n",
       "29              Arema FC 1986-01-03  37.0           no  \n",
       "143       Dewa United FC 2006-07-17  17.0          yes  \n",
       "12              Arema FC 1999-10-26  23.0           no  \n",
       "127  Borneo FC Samarinda 1991-10-12  31.0           no  \n",
       "65        Bhayangkara FC 1993-09-10  29.0           no  \n",
       "503         PSM Makassar        NaT   NaN          yes  "
      ]
     },
     "execution_count": 439,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new player in the club\n",
    "liga1_player.sort_values(by=\"joined\",ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kit_number</th>\n",
       "      <th>player_name</th>\n",
       "      <th>position</th>\n",
       "      <th>nationality</th>\n",
       "      <th>height_M</th>\n",
       "      <th>foot</th>\n",
       "      <th>joined</th>\n",
       "      <th>signed_from</th>\n",
       "      <th>contract</th>\n",
       "      <th>market_value_EUR</th>\n",
       "      <th>club_name</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>age</th>\n",
       "      <th>young_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>4</td>\n",
       "      <td>Yuran Fernandes</td>\n",
       "      <td>Centre-Back</td>\n",
       "      <td>Cape Verde</td>\n",
       "      <td>1.98</td>\n",
       "      <td>left</td>\n",
       "      <td>2022-06-04</td>\n",
       "      <td>SC União Torreense</td>\n",
       "      <td>2026-12-31</td>\n",
       "      <td>325000.0</td>\n",
       "      <td>PSM Makassar</td>\n",
       "      <td>1994-10-19</td>\n",
       "      <td>28.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4</td>\n",
       "      <td>Elias Dolah</td>\n",
       "      <td>Centre-Back</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>1.96</td>\n",
       "      <td>right</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>Port FC</td>\n",
       "      <td>2024-05-31</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>Bali United FC</td>\n",
       "      <td>1993-04-24</td>\n",
       "      <td>30.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>93</td>\n",
       "      <td>Lucão</td>\n",
       "      <td>Centre-Back</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.96</td>\n",
       "      <td>right</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>Persikabo 1973</td>\n",
       "      <td>NaT</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>PSIS Semarang</td>\n",
       "      <td>1993-07-10</td>\n",
       "      <td>29.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>55</td>\n",
       "      <td>Erlangga Setyo</td>\n",
       "      <td>Goalkeeper</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.95</td>\n",
       "      <td>right</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>PERSIS Solo Youth</td>\n",
       "      <td>2026-05-31</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>Persis Solo</td>\n",
       "      <td>2003-04-16</td>\n",
       "      <td>20.0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>Adilson Maringá</td>\n",
       "      <td>Goalkeeper</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.94</td>\n",
       "      <td>right</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>Arema FC</td>\n",
       "      <td>NaT</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>Bali United FC</td>\n",
       "      <td>1990-08-22</td>\n",
       "      <td>32.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    kit_number      player_name     position nationality  height_M   foot  \\\n",
       "475          4  Yuran Fernandes  Centre-Back  Cape Verde      1.98   left   \n",
       "34           4      Elias Dolah  Centre-Back    Thailand      1.96  right   \n",
       "471         93            Lucão  Centre-Back      Brazil      1.96  right   \n",
       "373         55   Erlangga Setyo   Goalkeeper   Indonesia      1.95  right   \n",
       "32           1  Adilson Maringá   Goalkeeper      Brazil      1.94  right   \n",
       "\n",
       "        joined         signed_from   contract  market_value_EUR  \\\n",
       "475 2022-06-04  SC União Torreense 2026-12-31          325000.0   \n",
       "34  2023-07-01             Port FC 2024-05-31          250000.0   \n",
       "471 2023-07-01      Persikabo 1973        NaT          225000.0   \n",
       "373 2023-01-01   PERSIS Solo Youth 2026-05-31           25000.0   \n",
       "32  2023-07-01            Arema FC        NaT          300000.0   \n",
       "\n",
       "          club_name birth_date   age young_player  \n",
       "475    PSM Makassar 1994-10-19  28.0           no  \n",
       "34   Bali United FC 1993-04-24  30.0           no  \n",
       "471   PSIS Semarang 1993-07-10  29.0           no  \n",
       "373     Persis Solo 2003-04-16  20.0          yes  \n",
       "32   Bali United FC 1990-08-22  32.0           no  "
      ]
     },
     "execution_count": 440,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the heightest player\n",
    "liga1_player.sort_values(by=\"height_M\", ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kit_number</th>\n",
       "      <th>player_name</th>\n",
       "      <th>position</th>\n",
       "      <th>nationality</th>\n",
       "      <th>height_M</th>\n",
       "      <th>foot</th>\n",
       "      <th>joined</th>\n",
       "      <th>signed_from</th>\n",
       "      <th>contract</th>\n",
       "      <th>market_value_EUR</th>\n",
       "      <th>club_name</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>age</th>\n",
       "      <th>young_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>91</td>\n",
       "      <td>Felipe Cadenazzi</td>\n",
       "      <td>Centre-Forward</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>1.91</td>\n",
       "      <td>left</td>\n",
       "      <td>2023-07-20</td>\n",
       "      <td>Magallanes CF</td>\n",
       "      <td>NaT</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>Borneo FC Samarinda</td>\n",
       "      <td>1991-10-12</td>\n",
       "      <td>31.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>20</td>\n",
       "      <td>Adilson Silva</td>\n",
       "      <td>Centre-Forward</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>1.91</td>\n",
       "      <td>right</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>Real SC</td>\n",
       "      <td>2024-12-31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PSM Makassar</td>\n",
       "      <td>1997-07-30</td>\n",
       "      <td>25.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>18</td>\n",
       "      <td>Crislan</td>\n",
       "      <td>Centre-Forward</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.90</td>\n",
       "      <td>left</td>\n",
       "      <td>2023-07-06</td>\n",
       "      <td>Swat Cat</td>\n",
       "      <td>NaT</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>Bhayangkara FC</td>\n",
       "      <td>1992-03-13</td>\n",
       "      <td>31.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>9</td>\n",
       "      <td>Jefinho</td>\n",
       "      <td>Centre-Forward</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.90</td>\n",
       "      <td>right</td>\n",
       "      <td>2023-07-11</td>\n",
       "      <td>Nejmeh SC</td>\n",
       "      <td>NaT</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>Persik Kediri</td>\n",
       "      <td>1994-05-25</td>\n",
       "      <td>29.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>10</td>\n",
       "      <td>Jelle Goselink</td>\n",
       "      <td>Centre-Forward</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>1.89</td>\n",
       "      <td>both</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>Helmond Sport</td>\n",
       "      <td>NaT</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>Borneo FC Samarinda</td>\n",
       "      <td>1999-07-31</td>\n",
       "      <td>23.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    kit_number       player_name        position  nationality  height_M  \\\n",
       "127         91  Felipe Cadenazzi  Centre-Forward    Argentina      1.91   \n",
       "488         20     Adilson Silva  Centre-Forward     Portugal      1.91   \n",
       "79          18           Crislan  Centre-Forward       Brazil      1.90   \n",
       "288          9           Jefinho  Centre-Forward       Brazil      1.90   \n",
       "104         10    Jelle Goselink  Centre-Forward  Netherlands      1.89   \n",
       "\n",
       "      foot     joined    signed_from   contract  market_value_EUR  \\\n",
       "127   left 2023-07-20  Magallanes CF        NaT          225000.0   \n",
       "488  right 2023-07-01        Real SC 2024-12-31               NaN   \n",
       "79    left 2023-07-06       Swat Cat        NaT          300000.0   \n",
       "288  right 2023-07-11      Nejmeh SC        NaT          150000.0   \n",
       "104   both 2023-07-01  Helmond Sport        NaT          225000.0   \n",
       "\n",
       "               club_name birth_date   age young_player  \n",
       "127  Borneo FC Samarinda 1991-10-12  31.0           no  \n",
       "488         PSM Makassar 1997-07-30  25.0           no  \n",
       "79        Bhayangkara FC 1992-03-13  31.0           no  \n",
       "288        Persik Kediri 1994-05-25  29.0           no  \n",
       "104  Borneo FC Samarinda 1999-07-31  23.0           no  "
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the heightest attak player\n",
    "attaker = liga1_player[(liga1_player[\"position\"] == \"Centre-Forward\") | (liga1_player[\"position\"] == \"Attack\") | (liga1_player[\"position\"] == \"Second Striker\")]\n",
    "attaker.sort_values(by=\"height_M\", ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Funfact: the hightest attacker is foreign player"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Signed from"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Without Club           35\n",
       "Arema FC               16\n",
       "Persija Jakarta        15\n",
       "Borneo FC Samarinda    15\n",
       "Bhayangkara FC         14\n",
       "Persebaya Surabaya     13\n",
       "PSM Makassar Youth     13\n",
       "Madura United FC       13\n",
       "PSM Makassar           12\n",
       "PSS Sleman             11\n",
       "Name: signed_from, dtype: int64"
      ]
     },
     "execution_count": 442,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liga1_player.signed_from.value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [],
   "source": [
    "# insight: signfrom no club with highest market_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "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>kit_number</th>\n",
       "      <th>player_name</th>\n",
       "      <th>position</th>\n",
       "      <th>nationality</th>\n",
       "      <th>height_M</th>\n",
       "      <th>foot</th>\n",
       "      <th>joined</th>\n",
       "      <th>signed_from</th>\n",
       "      <th>contract</th>\n",
       "      <th>market_value_EUR</th>\n",
       "      <th>club_name</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>age</th>\n",
       "      <th>young_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>95</td>\n",
       "      <td>Junior Brandão</td>\n",
       "      <td>Centre-Forward</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.87</td>\n",
       "      <td>right</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>NaT</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>Madura United FC</td>\n",
       "      <td>1995-01-07</td>\n",
       "      <td>28.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>24</td>\n",
       "      <td>Ricky Fajrin</td>\n",
       "      <td>Left-Back</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.77</td>\n",
       "      <td>left</td>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>NaT</td>\n",
       "      <td>275000.0</td>\n",
       "      <td>Bali United FC</td>\n",
       "      <td>1995-09-06</td>\n",
       "      <td>27.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>11</td>\n",
       "      <td>Lulinha</td>\n",
       "      <td>Left Winger</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.70</td>\n",
       "      <td>right</td>\n",
       "      <td>2022-05-09</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>2024-12-31</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>Madura United FC</td>\n",
       "      <td>1990-04-10</td>\n",
       "      <td>33.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>99</td>\n",
       "      <td>Bruno</td>\n",
       "      <td>Left Winger</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>1.78</td>\n",
       "      <td>both</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>NaT</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>Persebaya Surabaya</td>\n",
       "      <td>1999-04-08</td>\n",
       "      <td>24.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>5</td>\n",
       "      <td>Risto Mitrevski</td>\n",
       "      <td>Centre-Back</td>\n",
       "      <td>North Macedonia</td>\n",
       "      <td>1.87</td>\n",
       "      <td>both</td>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>2024-04-30</td>\n",
       "      <td>225000.0</td>\n",
       "      <td>Dewa United FC</td>\n",
       "      <td>1991-10-05</td>\n",
       "      <td>31.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>11</td>\n",
       "      <td>Yabes Roni</td>\n",
       "      <td>Left Winger</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.68</td>\n",
       "      <td>both</td>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>NaT</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>Bali United FC</td>\n",
       "      <td>1995-02-06</td>\n",
       "      <td>28.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>88</td>\n",
       "      <td>Fadilla Akbar</td>\n",
       "      <td>Defensive Midfield</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.73</td>\n",
       "      <td>left</td>\n",
       "      <td>2021-04-14</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>2024-12-31</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>RANS Nusantara FC</td>\n",
       "      <td>2001-09-08</td>\n",
       "      <td>21.0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>1</td>\n",
       "      <td>Anthony Pinthus</td>\n",
       "      <td>Goalkeeper</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>1.82</td>\n",
       "      <td>right</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>NaT</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>PSS Sleman</td>\n",
       "      <td>1998-04-04</td>\n",
       "      <td>25.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>37</td>\n",
       "      <td>Agil Munawar</td>\n",
       "      <td>Right-Back</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.69</td>\n",
       "      <td>right</td>\n",
       "      <td>2021-04-29</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>2024-04-30</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>Persik Kediri</td>\n",
       "      <td>1996-06-10</td>\n",
       "      <td>27.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>20</td>\n",
       "      <td>Simen Lyngbø</td>\n",
       "      <td>Right-Back</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>1.77</td>\n",
       "      <td>both</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>NaT</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>Persik Kediri</td>\n",
       "      <td>1998-02-18</td>\n",
       "      <td>25.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    kit_number      player_name            position      nationality  \\\n",
       "192         95   Junior Brandão      Centre-Forward           Brazil   \n",
       "46          24     Ricky Fajrin           Left-Back        Indonesia   \n",
       "167         11          Lulinha         Left Winger           Brazil   \n",
       "224         99            Bruno         Left Winger           Brazil   \n",
       "133          5  Risto Mitrevski         Centre-Back  North Macedonia   \n",
       "39          11       Yabes Roni         Left Winger        Indonesia   \n",
       "564         88    Fadilla Akbar  Defensive Midfield        Indonesia   \n",
       "505          1  Anthony Pinthus          Goalkeeper      Philippines   \n",
       "303         37     Agil Munawar          Right-Back        Indonesia   \n",
       "295         20     Simen Lyngbø          Right-Back      Philippines   \n",
       "\n",
       "     height_M   foot     joined   signed_from   contract  market_value_EUR  \\\n",
       "192      1.87  right 2023-07-01  Without Club        NaT          300000.0   \n",
       "46       1.77   left 2015-01-13  Without Club        NaT          275000.0   \n",
       "167      1.70  right 2022-05-09  Without Club 2024-12-31          250000.0   \n",
       "224      1.78   both 2023-07-01  Without Club        NaT          250000.0   \n",
       "133      1.87   both 2022-05-01  Without Club 2024-04-30          225000.0   \n",
       "39       1.68   both 2015-01-13  Without Club        NaT          175000.0   \n",
       "564      1.73   left 2021-04-14  Without Club 2024-12-31          150000.0   \n",
       "505      1.82  right 2023-07-01  Without Club        NaT          150000.0   \n",
       "303      1.69  right 2021-04-29  Without Club 2024-04-30          150000.0   \n",
       "295      1.77   both 2023-07-01  Without Club        NaT          125000.0   \n",
       "\n",
       "              club_name birth_date   age young_player  \n",
       "192    Madura United FC 1995-01-07  28.0           no  \n",
       "46       Bali United FC 1995-09-06  27.0           no  \n",
       "167    Madura United FC 1990-04-10  33.0           no  \n",
       "224  Persebaya Surabaya 1999-04-08  24.0           no  \n",
       "133      Dewa United FC 1991-10-05  31.0           no  \n",
       "39       Bali United FC 1995-02-06  28.0           no  \n",
       "564   RANS Nusantara FC 2001-09-08  21.0          yes  \n",
       "505          PSS Sleman 1998-04-04  25.0           no  \n",
       "303       Persik Kediri 1996-06-10  27.0           no  \n",
       "295       Persik Kediri 1998-02-18  25.0           no  "
      ]
     },
     "execution_count": 444,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liga1_player[liga1_player[\"signed_from\"]==\"Without Club\"].sort_values(by=\"market_value_EUR\", ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# contract\n",
    "\n",
    "contract has 333 missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adba99fa08>"
      ]
     },
     "execution_count": 445,
     "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": [
    "sns.scatterplot(data = liga1_player.contract)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "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>kit_number</th>\n",
       "      <th>player_name</th>\n",
       "      <th>position</th>\n",
       "      <th>nationality</th>\n",
       "      <th>height_M</th>\n",
       "      <th>foot</th>\n",
       "      <th>joined</th>\n",
       "      <th>signed_from</th>\n",
       "      <th>contract</th>\n",
       "      <th>market_value_EUR</th>\n",
       "      <th>club_name</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>age</th>\n",
       "      <th>young_player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>74</td>\n",
       "      <td>Mohammed Rashid</td>\n",
       "      <td>Defensive Midfield</td>\n",
       "      <td>Palestine</td>\n",
       "      <td>1.83</td>\n",
       "      <td>right</td>\n",
       "      <td>2023-07-13</td>\n",
       "      <td>Jabal Al-Mokaber</td>\n",
       "      <td>2005-07-16</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>Bali United FC</td>\n",
       "      <td>1995-07-03</td>\n",
       "      <td>28.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>99</td>\n",
       "      <td>Yandi Sofyan</td>\n",
       "      <td>Centre-Forward</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.76</td>\n",
       "      <td>right</td>\n",
       "      <td>2022-05-26</td>\n",
       "      <td>Persikota Tangerang</td>\n",
       "      <td>2005-07-16</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>Persikabo 1973</td>\n",
       "      <td>1992-05-25</td>\n",
       "      <td>31.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>77</td>\n",
       "      <td>Syamil Bahij</td>\n",
       "      <td>Goalkeeper</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>1.80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-07-14</td>\n",
       "      <td>Without Club</td>\n",
       "      <td>2005-07-16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PSM Makassar</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    kit_number      player_name            position nationality  height_M  \\\n",
       "58          74  Mohammed Rashid  Defensive Midfield   Palestine      1.83   \n",
       "346         99     Yandi Sofyan      Centre-Forward   Indonesia      1.76   \n",
       "503         77     Syamil Bahij          Goalkeeper   Indonesia      1.80   \n",
       "\n",
       "      foot     joined          signed_from   contract  market_value_EUR  \\\n",
       "58   right 2023-07-13     Jabal Al-Mokaber 2005-07-16          250000.0   \n",
       "346  right 2022-05-26  Persikota Tangerang 2005-07-16           75000.0   \n",
       "503    NaN 2023-07-14         Without Club 2005-07-16               NaN   \n",
       "\n",
       "          club_name birth_date   age young_player  \n",
       "58   Bali United FC 1995-07-03  28.0           no  \n",
       "346  Persikabo 1973 1992-05-25  31.0           no  \n",
       "503    PSM Makassar        NaT   NaN          yes  "
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liga1_player[liga1_player.contract < datetime.datetime(2012,1,1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adbaa13508>"
      ]
     },
     "execution_count": 447,
     "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": [
    "sns.scatterplot(data= liga1_player[liga1_player.contract > datetime.datetime(2020,1,1)][\"contract\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1       979.0\n",
       "2         NaN\n",
       "3         NaN\n",
       "4         NaN\n",
       "        ...  \n",
       "563       NaN\n",
       "564    1357.0\n",
       "565       NaN\n",
       "566     986.0\n",
       "567       NaN\n",
       "Length: 568, dtype: float64"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(liga1_player.contract - liga1_player.joined).dt.days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           NaT\n",
       "1      979 days\n",
       "2           NaT\n",
       "3           NaT\n",
       "4           NaT\n",
       "         ...   \n",
       "563         NaT\n",
       "564   1357 days\n",
       "565         NaT\n",
       "566    986 days\n",
       "567         NaT\n",
       "Length: 568, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liga1_player.contract - liga1_player.joined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adbc8dae48>"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# player with longest contract_length\n",
    "liga1_player[\"contract_length_days\"] = (liga1_player.contract - liga1_player.joined).dt.days\n",
    "vet_player = liga1_player.sort_values(by=\"contract_length_days\", ascending=False)\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.barplot(data=vet_player[3:13], x=\"contract_length_days\", y=\"player_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2adbcbe9888>"
      ]
     },
     "execution_count": 485,
     "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": [
    "sns.countplot(data=vet_player[3:33][[\"player_name\",\"position\",\"nationality\",\"club_name\",\"age\",\"joined\",\"contract\",\"contract_length_days\"]], y=\"club_name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# market_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.barplot(data=liga1_player.sort_values(by = \"market_value_EUR\", ascending=False)[:5], x=\"market_value_EUR\", y=\"player_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "liga1_player.sort_values(by = \"market_value_EUR\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "indonesian_player = liga1_player[liga1_player.nationality == \"Indonesia\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.barplot(data = indonesian_player.sort_values(by=\"market_value_EUR\", ascending=False)[:5], x=\"market_value_EUR\", y=\"player_name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# club name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of player from every club\n",
    "sns.countplot(data=liga1_player, y=\"club_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.barplot(data= liga1_player.groupby(by=\"club_name\").sum(), x=\"market_value_EUR\")\n",
    "sns.barplot(data = pd.DataFrame(liga1_player.groupby(by=\"club_name\").sum()).reset_index(), x=\"market_value_EUR\", y=\"club_name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.kdeplot(data=liga1_player, x=\"market_value_EUR\", hue=\"club_name\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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