FIFA Dataset Data Cleaning and Transformation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "47c6167c",
   "metadata": {},
   "source": [
    "# FIFA Dataset Data Cleaning and Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e6b0fe6",
   "metadata": {},
   "source": [
    "# Import all libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9a6e54f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d9d2c5",
   "metadata": {},
   "source": [
    "# Read data from CSV File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4fd0883b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Football = pd.read_csv(\"fifa21_raw_data.csv\", low_memory = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2c0d692a",
   "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>photoUrl</th>\n",
       "      <th>LongName</th>\n",
       "      <th>playerUrl</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Positions</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>↓OVA</th>\n",
       "      <th>POT</th>\n",
       "      <th>Team &amp; Contract</th>\n",
       "      <th>...</th>\n",
       "      <th>A/W</th>\n",
       "      <th>D/W</th>\n",
       "      <th>IR</th>\n",
       "      <th>PAC</th>\n",
       "      <th>SHO</th>\n",
       "      <th>PAS</th>\n",
       "      <th>DRI</th>\n",
       "      <th>DEF</th>\n",
       "      <th>PHY</th>\n",
       "      <th>Hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>https://cdn.sofifa.com/players/158/023/21_60.png</td>\n",
       "      <td>Lionel Messi</td>\n",
       "      <td>http://sofifa.com/player/158023/lionel-messi/2...</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>RW ST CF</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>33</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>\\n\\n\\n\\nFC Barcelona\\n2004 ~ 2021\\n\\n</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>5 ★</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "      <td>95</td>\n",
       "      <td>38</td>\n",
       "      <td>65</td>\n",
       "      <td>\\n372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://cdn.sofifa.com/players/020/801/21_60.png</td>\n",
       "      <td>C. Ronaldo dos Santos Aveiro</td>\n",
       "      <td>http://sofifa.com/player/20801/c-ronaldo-dos-s...</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>ST LW</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>35</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>\\n\\n\\n\\nJuventus\\n2018 ~ 2022\\n\\n</td>\n",
       "      <td>...</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>5 ★</td>\n",
       "      <td>89</td>\n",
       "      <td>93</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "      <td>35</td>\n",
       "      <td>77</td>\n",
       "      <td>\\n344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://cdn.sofifa.com/players/200/389/21_60.png</td>\n",
       "      <td>Jan Oblak</td>\n",
       "      <td>http://sofifa.com/player/200389/jan-oblak/210005/</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>GK</td>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>\\n\\n\\n\\nAtlético Madrid\\n2014 ~ 2023\\n\\n</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>3 ★</td>\n",
       "      <td>87</td>\n",
       "      <td>92</td>\n",
       "      <td>78</td>\n",
       "      <td>90</td>\n",
       "      <td>52</td>\n",
       "      <td>90</td>\n",
       "      <td>\\n86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://cdn.sofifa.com/players/192/985/21_60.png</td>\n",
       "      <td>Kevin De Bruyne</td>\n",
       "      <td>http://sofifa.com/player/192985/kevin-de-bruyn...</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>CAM CM</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>29</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>\\n\\n\\n\\nManchester City\\n2015 ~ 2023\\n\\n</td>\n",
       "      <td>...</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>4 ★</td>\n",
       "      <td>76</td>\n",
       "      <td>86</td>\n",
       "      <td>93</td>\n",
       "      <td>88</td>\n",
       "      <td>64</td>\n",
       "      <td>78</td>\n",
       "      <td>\\n163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://cdn.sofifa.com/players/190/871/21_60.png</td>\n",
       "      <td>Neymar da Silva Santos Jr.</td>\n",
       "      <td>http://sofifa.com/player/190871/neymar-da-silv...</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>LW CAM</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>28</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>\\n\\n\\n\\nParis Saint-Germain\\n2017 ~ 2022\\n\\n</td>\n",
       "      <td>...</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>5 ★</td>\n",
       "      <td>91</td>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "      <td>94</td>\n",
       "      <td>36</td>\n",
       "      <td>59</td>\n",
       "      <td>\\n273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           photoUrl  \\\n",
       "0  https://cdn.sofifa.com/players/158/023/21_60.png   \n",
       "1  https://cdn.sofifa.com/players/020/801/21_60.png   \n",
       "2  https://cdn.sofifa.com/players/200/389/21_60.png   \n",
       "3  https://cdn.sofifa.com/players/192/985/21_60.png   \n",
       "4  https://cdn.sofifa.com/players/190/871/21_60.png   \n",
       "\n",
       "                       LongName  \\\n",
       "0                  Lionel Messi   \n",
       "1  C. Ronaldo dos Santos Aveiro   \n",
       "2                     Jan Oblak   \n",
       "3               Kevin De Bruyne   \n",
       "4    Neymar da Silva Santos Jr.   \n",
       "\n",
       "                                           playerUrl Nationality Positions  \\\n",
       "0  http://sofifa.com/player/158023/lionel-messi/2...   Argentina  RW ST CF   \n",
       "1  http://sofifa.com/player/20801/c-ronaldo-dos-s...    Portugal     ST LW   \n",
       "2  http://sofifa.com/player/200389/jan-oblak/210005/    Slovenia        GK   \n",
       "3  http://sofifa.com/player/192985/kevin-de-bruyn...     Belgium    CAM CM   \n",
       "4  http://sofifa.com/player/190871/neymar-da-silv...      Brazil    LW CAM   \n",
       "\n",
       "                Name  Age  ↓OVA  POT  \\\n",
       "0           L. Messi   33    93   93   \n",
       "1  Cristiano Ronaldo   35    92   92   \n",
       "2           J. Oblak   27    91   93   \n",
       "3       K. De Bruyne   29    91   91   \n",
       "4          Neymar Jr   28    91   91   \n",
       "\n",
       "                                Team & Contract  ...     A/W     D/W   IR PAC  \\\n",
       "0         \\n\\n\\n\\nFC Barcelona\\n2004 ~ 2021\\n\\n  ...  Medium     Low  5 ★  85   \n",
       "1             \\n\\n\\n\\nJuventus\\n2018 ~ 2022\\n\\n  ...    High     Low  5 ★  89   \n",
       "2      \\n\\n\\n\\nAtlético Madrid\\n2014 ~ 2023\\n\\n  ...  Medium  Medium  3 ★  87   \n",
       "3      \\n\\n\\n\\nManchester City\\n2015 ~ 2023\\n\\n  ...    High    High  4 ★  76   \n",
       "4  \\n\\n\\n\\nParis Saint-Germain\\n2017 ~ 2022\\n\\n  ...    High  Medium  5 ★  91   \n",
       "\n",
       "   SHO PAS  DRI DEF PHY   Hits  \n",
       "0   92  91   95  38  65  \\n372  \n",
       "1   93  81   89  35  77  \\n344  \n",
       "2   92  78   90  52  90   \\n86  \n",
       "3   86  93   88  64  78  \\n163  \n",
       "4   85  86   94  36  59  \\n273  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df = pd.DataFrame(Football)\n",
    "Fifa_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0dd8a43b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18979 entries, 0 to 18978\n",
      "Data columns (total 77 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   photoUrl          18979 non-null  object\n",
      " 1   LongName          18979 non-null  object\n",
      " 2   playerUrl         18979 non-null  object\n",
      " 3   Nationality       18979 non-null  object\n",
      " 4   Positions         18979 non-null  object\n",
      " 5   Name              18979 non-null  object\n",
      " 6   Age               18979 non-null  int64 \n",
      " 7   ↓OVA              18979 non-null  int64 \n",
      " 8   POT               18979 non-null  int64 \n",
      " 9   Team & Contract   18979 non-null  object\n",
      " 10  ID                18979 non-null  int64 \n",
      " 11  Height            18979 non-null  object\n",
      " 12  Weight            18979 non-null  object\n",
      " 13  foot              18979 non-null  object\n",
      " 14  BOV               18979 non-null  int64 \n",
      " 15  BP                18979 non-null  object\n",
      " 16  Growth            18979 non-null  int64 \n",
      " 17  Joined            18979 non-null  object\n",
      " 18  Loan Date End     1013 non-null   object\n",
      " 19  Value             18979 non-null  object\n",
      " 20  Wage              18979 non-null  object\n",
      " 21  Release Clause    18979 non-null  object\n",
      " 22  Attacking         18979 non-null  int64 \n",
      " 23  Crossing          18979 non-null  int64 \n",
      " 24  Finishing         18979 non-null  int64 \n",
      " 25  Heading Accuracy  18979 non-null  int64 \n",
      " 26  Short Passing     18979 non-null  int64 \n",
      " 27  Volleys           18979 non-null  int64 \n",
      " 28  Skill             18979 non-null  int64 \n",
      " 29  Dribbling         18979 non-null  int64 \n",
      " 30  Curve             18979 non-null  int64 \n",
      " 31  FK Accuracy       18979 non-null  int64 \n",
      " 32  Long Passing      18979 non-null  int64 \n",
      " 33  Ball Control      18979 non-null  int64 \n",
      " 34  Movement          18979 non-null  int64 \n",
      " 35  Acceleration      18979 non-null  int64 \n",
      " 36  Sprint Speed      18979 non-null  int64 \n",
      " 37  Agility           18979 non-null  int64 \n",
      " 38  Reactions         18979 non-null  int64 \n",
      " 39  Balance           18979 non-null  int64 \n",
      " 40  Power             18979 non-null  int64 \n",
      " 41  Shot Power        18979 non-null  int64 \n",
      " 42  Jumping           18979 non-null  int64 \n",
      " 43  Stamina           18979 non-null  int64 \n",
      " 44  Strength          18979 non-null  int64 \n",
      " 45  Long Shots        18979 non-null  int64 \n",
      " 46  Mentality         18979 non-null  int64 \n",
      " 47  Aggression        18979 non-null  int64 \n",
      " 48  Interceptions     18979 non-null  int64 \n",
      " 49  Positioning       18979 non-null  int64 \n",
      " 50  Vision            18979 non-null  int64 \n",
      " 51  Penalties         18979 non-null  int64 \n",
      " 52  Composure         18979 non-null  int64 \n",
      " 53  Defending         18979 non-null  int64 \n",
      " 54  Marking           18979 non-null  int64 \n",
      " 55  Standing Tackle   18979 non-null  int64 \n",
      " 56  Sliding Tackle    18979 non-null  int64 \n",
      " 57  Goalkeeping       18979 non-null  int64 \n",
      " 58  GK Diving         18979 non-null  int64 \n",
      " 59  GK Handling       18979 non-null  int64 \n",
      " 60  GK Kicking        18979 non-null  int64 \n",
      " 61  GK Positioning    18979 non-null  int64 \n",
      " 62  GK Reflexes       18979 non-null  int64 \n",
      " 63  Total Stats       18979 non-null  int64 \n",
      " 64  Base Stats        18979 non-null  int64 \n",
      " 65  W/F               18979 non-null  object\n",
      " 66  SM                18979 non-null  object\n",
      " 67  A/W               18979 non-null  object\n",
      " 68  D/W               18979 non-null  object\n",
      " 69  IR                18979 non-null  object\n",
      " 70  PAC               18979 non-null  int64 \n",
      " 71  SHO               18979 non-null  int64 \n",
      " 72  PAS               18979 non-null  int64 \n",
      " 73  DRI               18979 non-null  int64 \n",
      " 74  DEF               18979 non-null  int64 \n",
      " 75  PHY               18979 non-null  int64 \n",
      " 76  Hits              18979 non-null  object\n",
      "dtypes: int64(55), object(22)\n",
      "memory usage: 11.1+ MB\n"
     ]
    }
   ],
   "source": [
    "Fifa_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62f987c3",
   "metadata": {},
   "source": [
    "# Data Cleaning and Manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ccd024d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove unnecessary column\n",
    "Fifa_df = Fifa_df.drop([\"photoUrl\",\"playerUrl\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dba162ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18979 entries, 0 to 18978\n",
      "Data columns (total 75 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   LongName          18979 non-null  object\n",
      " 1   Nationality       18979 non-null  object\n",
      " 2   Positions         18979 non-null  object\n",
      " 3   Name              18979 non-null  object\n",
      " 4   Age               18979 non-null  int64 \n",
      " 5   ↓OVA              18979 non-null  int64 \n",
      " 6   POT               18979 non-null  int64 \n",
      " 7   Team & Contract   18979 non-null  object\n",
      " 8   ID                18979 non-null  int64 \n",
      " 9   Height            18979 non-null  object\n",
      " 10  Weight            18979 non-null  object\n",
      " 11  foot              18979 non-null  object\n",
      " 12  BOV               18979 non-null  int64 \n",
      " 13  BP                18979 non-null  object\n",
      " 14  Growth            18979 non-null  int64 \n",
      " 15  Joined            18979 non-null  object\n",
      " 16  Loan Date End     1013 non-null   object\n",
      " 17  Value             18979 non-null  object\n",
      " 18  Wage              18979 non-null  object\n",
      " 19  Release Clause    18979 non-null  object\n",
      " 20  Attacking         18979 non-null  int64 \n",
      " 21  Crossing          18979 non-null  int64 \n",
      " 22  Finishing         18979 non-null  int64 \n",
      " 23  Heading Accuracy  18979 non-null  int64 \n",
      " 24  Short Passing     18979 non-null  int64 \n",
      " 25  Volleys           18979 non-null  int64 \n",
      " 26  Skill             18979 non-null  int64 \n",
      " 27  Dribbling         18979 non-null  int64 \n",
      " 28  Curve             18979 non-null  int64 \n",
      " 29  FK Accuracy       18979 non-null  int64 \n",
      " 30  Long Passing      18979 non-null  int64 \n",
      " 31  Ball Control      18979 non-null  int64 \n",
      " 32  Movement          18979 non-null  int64 \n",
      " 33  Acceleration      18979 non-null  int64 \n",
      " 34  Sprint Speed      18979 non-null  int64 \n",
      " 35  Agility           18979 non-null  int64 \n",
      " 36  Reactions         18979 non-null  int64 \n",
      " 37  Balance           18979 non-null  int64 \n",
      " 38  Power             18979 non-null  int64 \n",
      " 39  Shot Power        18979 non-null  int64 \n",
      " 40  Jumping           18979 non-null  int64 \n",
      " 41  Stamina           18979 non-null  int64 \n",
      " 42  Strength          18979 non-null  int64 \n",
      " 43  Long Shots        18979 non-null  int64 \n",
      " 44  Mentality         18979 non-null  int64 \n",
      " 45  Aggression        18979 non-null  int64 \n",
      " 46  Interceptions     18979 non-null  int64 \n",
      " 47  Positioning       18979 non-null  int64 \n",
      " 48  Vision            18979 non-null  int64 \n",
      " 49  Penalties         18979 non-null  int64 \n",
      " 50  Composure         18979 non-null  int64 \n",
      " 51  Defending         18979 non-null  int64 \n",
      " 52  Marking           18979 non-null  int64 \n",
      " 53  Standing Tackle   18979 non-null  int64 \n",
      " 54  Sliding Tackle    18979 non-null  int64 \n",
      " 55  Goalkeeping       18979 non-null  int64 \n",
      " 56  GK Diving         18979 non-null  int64 \n",
      " 57  GK Handling       18979 non-null  int64 \n",
      " 58  GK Kicking        18979 non-null  int64 \n",
      " 59  GK Positioning    18979 non-null  int64 \n",
      " 60  GK Reflexes       18979 non-null  int64 \n",
      " 61  Total Stats       18979 non-null  int64 \n",
      " 62  Base Stats        18979 non-null  int64 \n",
      " 63  W/F               18979 non-null  object\n",
      " 64  SM                18979 non-null  object\n",
      " 65  A/W               18979 non-null  object\n",
      " 66  D/W               18979 non-null  object\n",
      " 67  IR                18979 non-null  object\n",
      " 68  PAC               18979 non-null  int64 \n",
      " 69  SHO               18979 non-null  int64 \n",
      " 70  PAS               18979 non-null  int64 \n",
      " 71  DRI               18979 non-null  int64 \n",
      " 72  DEF               18979 non-null  int64 \n",
      " 73  PHY               18979 non-null  int64 \n",
      " 74  Hits              18979 non-null  object\n",
      "dtypes: int64(55), object(20)\n",
      "memory usage: 10.9+ MB\n"
     ]
    }
   ],
   "source": [
    "Fifa_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a62fc17c",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LongName</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Positions</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>↓OVA</th>\n",
       "      <th>POT</th>\n",
       "      <th>Team &amp; Contract</th>\n",
       "      <th>ID</th>\n",
       "      <th>Height</th>\n",
       "      <th>...</th>\n",
       "      <th>A/W</th>\n",
       "      <th>D/W</th>\n",
       "      <th>IR</th>\n",
       "      <th>PAC</th>\n",
       "      <th>SHO</th>\n",
       "      <th>PAS</th>\n",
       "      <th>DRI</th>\n",
       "      <th>DEF</th>\n",
       "      <th>PHY</th>\n",
       "      <th>Hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lionel Messi</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>RW ST CF</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>33</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>\\n\\n\\n\\nFC Barcelona\\n2004 ~ 2021\\n\\n</td>\n",
       "      <td>158023</td>\n",
       "      <td>5'7\"</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>5 ★</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "      <td>95</td>\n",
       "      <td>38</td>\n",
       "      <td>65</td>\n",
       "      <td>\\n372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C. Ronaldo dos Santos Aveiro</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>ST LW</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>35</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>\\n\\n\\n\\nJuventus\\n2018 ~ 2022\\n\\n</td>\n",
       "      <td>20801</td>\n",
       "      <td>6'2\"</td>\n",
       "      <td>...</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>5 ★</td>\n",
       "      <td>89</td>\n",
       "      <td>93</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "      <td>35</td>\n",
       "      <td>77</td>\n",
       "      <td>\\n344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jan Oblak</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>GK</td>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>\\n\\n\\n\\nAtlético Madrid\\n2014 ~ 2023\\n\\n</td>\n",
       "      <td>200389</td>\n",
       "      <td>6'2\"</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>3 ★</td>\n",
       "      <td>87</td>\n",
       "      <td>92</td>\n",
       "      <td>78</td>\n",
       "      <td>90</td>\n",
       "      <td>52</td>\n",
       "      <td>90</td>\n",
       "      <td>\\n86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kevin De Bruyne</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>CAM CM</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>29</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>\\n\\n\\n\\nManchester City\\n2015 ~ 2023\\n\\n</td>\n",
       "      <td>192985</td>\n",
       "      <td>5'11\"</td>\n",
       "      <td>...</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>4 ★</td>\n",
       "      <td>76</td>\n",
       "      <td>86</td>\n",
       "      <td>93</td>\n",
       "      <td>88</td>\n",
       "      <td>64</td>\n",
       "      <td>78</td>\n",
       "      <td>\\n163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Neymar da Silva Santos Jr.</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>LW CAM</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>28</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>\\n\\n\\n\\nParis Saint-Germain\\n2017 ~ 2022\\n\\n</td>\n",
       "      <td>190871</td>\n",
       "      <td>5'9\"</td>\n",
       "      <td>...</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>5 ★</td>\n",
       "      <td>91</td>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "      <td>94</td>\n",
       "      <td>36</td>\n",
       "      <td>59</td>\n",
       "      <td>\\n273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       LongName Nationality Positions               Name  Age  \\\n",
       "0                  Lionel Messi   Argentina  RW ST CF           L. Messi   33   \n",
       "1  C. Ronaldo dos Santos Aveiro    Portugal     ST LW  Cristiano Ronaldo   35   \n",
       "2                     Jan Oblak    Slovenia        GK           J. Oblak   27   \n",
       "3               Kevin De Bruyne     Belgium    CAM CM       K. De Bruyne   29   \n",
       "4    Neymar da Silva Santos Jr.      Brazil    LW CAM          Neymar Jr   28   \n",
       "\n",
       "   ↓OVA  POT                               Team & Contract      ID Height  \\\n",
       "0    93   93         \\n\\n\\n\\nFC Barcelona\\n2004 ~ 2021\\n\\n  158023   5'7\"   \n",
       "1    92   92             \\n\\n\\n\\nJuventus\\n2018 ~ 2022\\n\\n   20801   6'2\"   \n",
       "2    91   93      \\n\\n\\n\\nAtlético Madrid\\n2014 ~ 2023\\n\\n  200389   6'2\"   \n",
       "3    91   91      \\n\\n\\n\\nManchester City\\n2015 ~ 2023\\n\\n  192985  5'11\"   \n",
       "4    91   91  \\n\\n\\n\\nParis Saint-Germain\\n2017 ~ 2022\\n\\n  190871   5'9\"   \n",
       "\n",
       "   ...     A/W     D/W   IR PAC  SHO PAS DRI DEF PHY   Hits  \n",
       "0  ...  Medium     Low  5 ★  85   92  91  95  38  65  \\n372  \n",
       "1  ...    High     Low  5 ★  89   93  81  89  35  77  \\n344  \n",
       "2  ...  Medium  Medium  3 ★  87   92  78  90  52  90   \\n86  \n",
       "3  ...    High    High  4 ★  76   86  93  88  64  78  \\n163  \n",
       "4  ...    High  Medium  5 ★  91   85  86  94  36  59  \\n273  \n",
       "\n",
       "[5 rows x 75 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e549a739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18851"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"LongName\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09ce6fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LongName</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Positions</th>\n",
       "      <th>Name</th>\n",
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       "      <th>↓OVA</th>\n",
       "      <th>POT</th>\n",
       "      <th>Team &amp; Contract</th>\n",
       "      <th>ID</th>\n",
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       "      <th>IR</th>\n",
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       "      <th>DEF</th>\n",
       "      <th>PHY</th>\n",
       "      <th>Hits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>944</th>\n",
       "      <td>Kevin Berlaso</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>RB</td>\n",
       "      <td>K. Berlaso</td>\n",
       "      <td>32</td>\n",
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       "      <td>...</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2 ★</td>\n",
       "      <td>78</td>\n",
       "      <td>56</td>\n",
       "      <td>69</td>\n",
       "      <td>77</td>\n",
       "      <td>72</td>\n",
       "      <td>68</td>\n",
       "      <td>\\n12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1187</th>\n",
       "      <td>Lisandro López</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>CB</td>\n",
       "      <td>L. López</td>\n",
       "      <td>30</td>\n",
       "      <td>76</td>\n",
       "      <td>76</td>\n",
       "      <td>\\n\\n\\n\\nBoca Juniors\\n2020 ~ 2023\\n\\n</td>\n",
       "      <td>215051</td>\n",
       "      <td>6'2\"</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>3 ★</td>\n",
       "      <td>62</td>\n",
       "      <td>51</td>\n",
       "      <td>50</td>\n",
       "      <td>61</td>\n",
       "      <td>77</td>\n",
       "      <td>74</td>\n",
       "      <td>\\n7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2516</th>\n",
       "      <td>Gonzalo Castro</td>\n",
       "      <td>Germany</td>\n",
       "      <td>CM LB</td>\n",
       "      <td>G. Castro</td>\n",
       "      <td>33</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>\\n\\n\\n\\nVfB Stuttgart\\n2018 ~ 2021\\n\\n</td>\n",
       "      <td>167431</td>\n",
       "      <td>5'8\"</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2 ★</td>\n",
       "      <td>55</td>\n",
       "      <td>67</td>\n",
       "      <td>75</td>\n",
       "      <td>74</td>\n",
       "      <td>67</td>\n",
       "      <td>63</td>\n",
       "      <td>\\n15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2562</th>\n",
       "      <td>Ben Davies</td>\n",
       "      <td>England</td>\n",
       "      <td>CB</td>\n",
       "      <td>B. Davies</td>\n",
       "      <td>24</td>\n",
       "      <td>73</td>\n",
       "      <td>79</td>\n",
       "      <td>\\n\\n\\n\\nPreston North End\\n2013 ~ 2021\\n\\n</td>\n",
       "      <td>213017</td>\n",
       "      <td>6'1\"</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1 ★</td>\n",
       "      <td>74</td>\n",
       "      <td>39</td>\n",
       "      <td>58</td>\n",
       "      <td>63</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>\\n38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2955</th>\n",
       "      <td>Claudio Bravo</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>LB</td>\n",
       "      <td>C. Bravo</td>\n",
       "      <td>23</td>\n",
       "      <td>73</td>\n",
       "      <td>82</td>\n",
       "      <td>\\n\\n\\n\\nClub Atlético Banfield\\n2016 ~ 2021\\n\\n</td>\n",
       "      <td>232646</td>\n",
       "      <td>5'7\"</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1 ★</td>\n",
       "      <td>74</td>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "      <td>73</td>\n",
       "      <td>71</td>\n",
       "      <td>65</td>\n",
       "      <td>\\n40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18703</th>\n",
       "      <td>Yuan Zhang</td>\n",
       "      <td>China PR</td>\n",
       "      <td>CM</td>\n",
       "      <td>Zhang Yuan</td>\n",
       "      <td>23</td>\n",
       "      <td>51</td>\n",
       "      <td>56</td>\n",
       "      <td>\\n\\n\\n\\nShenzhen FC\\n2020 ~ 2023\\n\\n</td>\n",
       "      <td>241341</td>\n",
       "      <td>6'0\"</td>\n",
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       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1 ★</td>\n",
       "      <td>73</td>\n",
       "      <td>45</td>\n",
       "      <td>48</td>\n",
       "      <td>56</td>\n",
       "      <td>50</td>\n",
       "      <td>59</td>\n",
       "      <td>\\n2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18741</th>\n",
       "      <td>Alfie Jones</td>\n",
       "      <td>England</td>\n",
       "      <td>GK</td>\n",
       "      <td>A. Jones</td>\n",
       "      <td>19</td>\n",
       "      <td>50</td>\n",
       "      <td>66</td>\n",
       "      <td>\\n\\n\\n\\nCrawley Town\\n2019 ~ 2021\\n\\n</td>\n",
       "      <td>243202</td>\n",
       "      <td>6'1\"</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1 ★</td>\n",
       "      <td>56</td>\n",
       "      <td>48</td>\n",
       "      <td>45</td>\n",
       "      <td>52</td>\n",
       "      <td>22</td>\n",
       "      <td>44</td>\n",
       "      <td>\\n3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18813</th>\n",
       "      <td>Joe Wright</td>\n",
       "      <td>England</td>\n",
       "      <td>GK</td>\n",
       "      <td>J. Wright</td>\n",
       "      <td>19</td>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "      <td>\\n\\n\\n\\nMillwall\\n2019 ~ 2024\\n\\n</td>\n",
       "      <td>251563</td>\n",
       "      <td>6'1\"</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1 ★</td>\n",
       "      <td>53</td>\n",
       "      <td>54</td>\n",
       "      <td>47</td>\n",
       "      <td>48</td>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "      <td>\\n1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18834</th>\n",
       "      <td>Mark Byrne</td>\n",
       "      <td>Republic of Ireland</td>\n",
       "      <td>CAM LW RW</td>\n",
       "      <td>M. Byrne</td>\n",
       "      <td>19</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>\\n\\n\\n\\nSligo Rovers\\n2020 ~ 2020\\n\\n</td>\n",
       "      <td>255948</td>\n",
       "      <td>5'10\"</td>\n",
       "      <td>...</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1 ★</td>\n",
       "      <td>60</td>\n",
       "      <td>36</td>\n",
       "      <td>53</td>\n",
       "      <td>50</td>\n",
       "      <td>37</td>\n",
       "      <td>50</td>\n",
       "      <td>\\n1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18909</th>\n",
       "      <td>Wei Zhang</td>\n",
       "      <td>China PR</td>\n",
       "      <td>RM RW</td>\n",
       "      <td>Zhang Wei</td>\n",
       "      <td>20</td>\n",
       "      <td>49</td>\n",
       "      <td>57</td>\n",
       "      <td>\\n\\n\\n\\nHebei China Fortune FC\\n2019 ~ 2024\\n\\n</td>\n",
       "      <td>252332</td>\n",
       "      <td>6'1\"</td>\n",
       "      <td>...</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1 ★</td>\n",
       "      <td>57</td>\n",
       "      <td>38</td>\n",
       "      <td>47</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>51</td>\n",
       "      <td>\\n1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>127 rows × 75 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             LongName          Nationality  Positions        Name  Age  ↓OVA  \\\n",
       "944     Kevin Berlaso              Ecuador         RB  K. Berlaso   32    77   \n",
       "1187   Lisandro López            Argentina         CB    L. López   30    76   \n",
       "2516   Gonzalo Castro              Germany      CM LB   G. Castro   33    73   \n",
       "2562       Ben Davies              England         CB   B. Davies   24    73   \n",
       "2955    Claudio Bravo            Argentina         LB    C. Bravo   23    73   \n",
       "...               ...                  ...        ...         ...  ...   ...   \n",
       "18703      Yuan Zhang             China PR         CM  Zhang Yuan   23    51   \n",
       "18741     Alfie Jones              England         GK    A. Jones   19    50   \n",
       "18813      Joe Wright              England         GK   J. Wright   19    50   \n",
       "18834      Mark Byrne  Republic of Ireland  CAM LW RW    M. Byrne   19    50   \n",
       "18909       Wei Zhang             China PR      RM RW   Zhang Wei   20    49   \n",
       "\n",
       "       POT                                  Team & Contract      ID Height  \\\n",
       "944     77                             \\n Ecuador\\nFree\\n\\n  251698   5'8\"   \n",
       "1187    76            \\n\\n\\n\\nBoca Juniors\\n2020 ~ 2023\\n\\n  215051   6'2\"   \n",
       "2516    73           \\n\\n\\n\\nVfB Stuttgart\\n2018 ~ 2021\\n\\n  167431   5'8\"   \n",
       "2562    79       \\n\\n\\n\\nPreston North End\\n2013 ~ 2021\\n\\n  213017   6'1\"   \n",
       "2955    82  \\n\\n\\n\\nClub Atlético Banfield\\n2016 ~ 2021\\n\\n  232646   5'7\"   \n",
       "...    ...                                              ...     ...    ...   \n",
       "18703   56             \\n\\n\\n\\nShenzhen FC\\n2020 ~ 2023\\n\\n  241341   6'0\"   \n",
       "18741   66            \\n\\n\\n\\nCrawley Town\\n2019 ~ 2021\\n\\n  243202   6'1\"   \n",
       "18813   64                \\n\\n\\n\\nMillwall\\n2019 ~ 2024\\n\\n  251563   6'1\"   \n",
       "18834   65            \\n\\n\\n\\nSligo Rovers\\n2020 ~ 2020\\n\\n  255948  5'10\"   \n",
       "18909   57  \\n\\n\\n\\nHebei China Fortune FC\\n2019 ~ 2024\\n\\n  252332   6'1\"   \n",
       "\n",
       "       ...     A/W     D/W   IR PAC  SHO PAS DRI DEF PHY  Hits  \n",
       "944    ...    High  Medium  2 ★  78   56  69  77  72  68  \\n12  \n",
       "1187   ...  Medium    High  3 ★  62   51  50  61  77  74   \\n7  \n",
       "2516   ...  Medium  Medium  2 ★  55   67  75  74  67  63  \\n15  \n",
       "2562   ...  Medium  Medium  1 ★  74   39  58  63  75  75  \\n38  \n",
       "2955   ...  Medium  Medium  1 ★  74   50  64  73  71  65  \\n40  \n",
       "...    ...     ...     ...  ...  ..  ...  ..  ..  ..  ..   ...  \n",
       "18703  ...  Medium  Medium  1 ★  73   45  48  56  50  59   \\n2  \n",
       "18741  ...  Medium  Medium  1 ★  56   48  45  52  22  44   \\n3  \n",
       "18813  ...  Medium  Medium  1 ★  53   54  47  48  18  46   \\n1  \n",
       "18834  ...    High  Medium  1 ★  60   36  53  50  37  50   \\n1  \n",
       "18909  ...  Medium  Medium  1 ★  57   38  47  48  48  51   \\n1  \n",
       "\n",
       "[127 rows x 75 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check duplicate rows\n",
    "duplicate_rows = Fifa_df[Fifa_df.duplicated(subset=[\"LongName\", \"Name\"])]\n",
    "duplicate_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c01fca50",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove duplicate rows\n",
    "Fifa_df = Fifa_df[~Fifa_df.duplicated(subset=[\"LongName\",\"Name\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e5ccc3b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18852 entries, 0 to 18978\n",
      "Data columns (total 75 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   LongName          18852 non-null  object\n",
      " 1   Nationality       18852 non-null  object\n",
      " 2   Positions         18852 non-null  object\n",
      " 3   Name              18852 non-null  object\n",
      " 4   Age               18852 non-null  int64 \n",
      " 5   ↓OVA              18852 non-null  int64 \n",
      " 6   POT               18852 non-null  int64 \n",
      " 7   Team & Contract   18852 non-null  object\n",
      " 8   ID                18852 non-null  int64 \n",
      " 9   Height            18852 non-null  object\n",
      " 10  Weight            18852 non-null  object\n",
      " 11  foot              18852 non-null  object\n",
      " 12  BOV               18852 non-null  int64 \n",
      " 13  BP                18852 non-null  object\n",
      " 14  Growth            18852 non-null  int64 \n",
      " 15  Joined            18852 non-null  object\n",
      " 16  Loan Date End     1003 non-null   object\n",
      " 17  Value             18852 non-null  object\n",
      " 18  Wage              18852 non-null  object\n",
      " 19  Release Clause    18852 non-null  object\n",
      " 20  Attacking         18852 non-null  int64 \n",
      " 21  Crossing          18852 non-null  int64 \n",
      " 22  Finishing         18852 non-null  int64 \n",
      " 23  Heading Accuracy  18852 non-null  int64 \n",
      " 24  Short Passing     18852 non-null  int64 \n",
      " 25  Volleys           18852 non-null  int64 \n",
      " 26  Skill             18852 non-null  int64 \n",
      " 27  Dribbling         18852 non-null  int64 \n",
      " 28  Curve             18852 non-null  int64 \n",
      " 29  FK Accuracy       18852 non-null  int64 \n",
      " 30  Long Passing      18852 non-null  int64 \n",
      " 31  Ball Control      18852 non-null  int64 \n",
      " 32  Movement          18852 non-null  int64 \n",
      " 33  Acceleration      18852 non-null  int64 \n",
      " 34  Sprint Speed      18852 non-null  int64 \n",
      " 35  Agility           18852 non-null  int64 \n",
      " 36  Reactions         18852 non-null  int64 \n",
      " 37  Balance           18852 non-null  int64 \n",
      " 38  Power             18852 non-null  int64 \n",
      " 39  Shot Power        18852 non-null  int64 \n",
      " 40  Jumping           18852 non-null  int64 \n",
      " 41  Stamina           18852 non-null  int64 \n",
      " 42  Strength          18852 non-null  int64 \n",
      " 43  Long Shots        18852 non-null  int64 \n",
      " 44  Mentality         18852 non-null  int64 \n",
      " 45  Aggression        18852 non-null  int64 \n",
      " 46  Interceptions     18852 non-null  int64 \n",
      " 47  Positioning       18852 non-null  int64 \n",
      " 48  Vision            18852 non-null  int64 \n",
      " 49  Penalties         18852 non-null  int64 \n",
      " 50  Composure         18852 non-null  int64 \n",
      " 51  Defending         18852 non-null  int64 \n",
      " 52  Marking           18852 non-null  int64 \n",
      " 53  Standing Tackle   18852 non-null  int64 \n",
      " 54  Sliding Tackle    18852 non-null  int64 \n",
      " 55  Goalkeeping       18852 non-null  int64 \n",
      " 56  GK Diving         18852 non-null  int64 \n",
      " 57  GK Handling       18852 non-null  int64 \n",
      " 58  GK Kicking        18852 non-null  int64 \n",
      " 59  GK Positioning    18852 non-null  int64 \n",
      " 60  GK Reflexes       18852 non-null  int64 \n",
      " 61  Total Stats       18852 non-null  int64 \n",
      " 62  Base Stats        18852 non-null  int64 \n",
      " 63  W/F               18852 non-null  object\n",
      " 64  SM                18852 non-null  object\n",
      " 65  A/W               18852 non-null  object\n",
      " 66  D/W               18852 non-null  object\n",
      " 67  IR                18852 non-null  object\n",
      " 68  PAC               18852 non-null  int64 \n",
      " 69  SHO               18852 non-null  int64 \n",
      " 70  PAS               18852 non-null  int64 \n",
      " 71  DRI               18852 non-null  int64 \n",
      " 72  DEF               18852 non-null  int64 \n",
      " 73  PHY               18852 non-null  int64 \n",
      " 74  Hits              18852 non-null  object\n",
      "dtypes: int64(55), object(20)\n",
      "memory usage: 10.9+ MB\n"
     ]
    }
   ],
   "source": [
    "Fifa_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "29fea151",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      RW ST CF\n",
       "1         ST LW\n",
       "2            GK\n",
       "3        CAM CM\n",
       "4        LW CAM\n",
       "5            ST\n",
       "6      ST LW RW\n",
       "7            GK\n",
       "8            RW\n",
       "9            LW\n",
       "10           CB\n",
       "11           GK\n",
       "12          CDM\n",
       "13           GK\n",
       "14           GK\n",
       "15        CF ST\n",
       "16           CB\n",
       "17           ST\n",
       "18       CDM CM\n",
       "19       CDM RB\n",
       "20       CF CAM\n",
       "21           GK\n",
       "22        LW RW\n",
       "23           ST\n",
       "24           CB\n",
       "25        LW ST\n",
       "26           CM\n",
       "27           GK\n",
       "28    RM LM CAM\n",
       "29           RB\n",
       "30    RW CAM CM\n",
       "31           LB\n",
       "32           CB\n",
       "33       CAM CM\n",
       "34          CDM\n",
       "35           CF\n",
       "36        LM CF\n",
       "37     ST CF LW\n",
       "38           GK\n",
       "39           ST\n",
       "40       CDM CM\n",
       "41        ST LW\n",
       "42           GK\n",
       "43        RW LW\n",
       "44           CM\n",
       "45           ST\n",
       "46           GK\n",
       "47           CB\n",
       "48           ST\n",
       "49           RB\n",
       "50           CB\n",
       "51       CM CDM\n",
       "52       CM CDM\n",
       "53           GK\n",
       "54    CAM RM RW\n",
       "55           LB\n",
       "56       CDM CM\n",
       "57           CB\n",
       "58           GK\n",
       "59       CAM CM\n",
       "Name: Positions, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Positions\"].head(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3dd4ca62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [RW, ST, CF]\n",
       "1            [ST, LW]\n",
       "2                [GK]\n",
       "3           [CAM, CM]\n",
       "4           [LW, CAM]\n",
       "             ...     \n",
       "18974            [CB]\n",
       "18975            [ST]\n",
       "18976            [CB]\n",
       "18977            [CM]\n",
       "18978           [CDM]\n",
       "Name: Positions, Length: 18852, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#split position using split function\n",
    "position_tokens = Fifa_df[\"Positions\"].str.split()\n",
    "position_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1272c161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CAM',\n",
       " 'CB',\n",
       " 'CDM',\n",
       " 'CF',\n",
       " 'CM',\n",
       " 'GK',\n",
       " 'LB',\n",
       " 'LM',\n",
       " 'LW',\n",
       " 'LWB',\n",
       " 'RB',\n",
       " 'RM',\n",
       " 'RW',\n",
       " 'RWB',\n",
       " 'ST'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make empty set to store unique positions\n",
    "positions = set()\n",
    "#update each unique positions in set\n",
    "for position in position_tokens:\n",
    "    positions.update(position)\n",
    "positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9010391b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make new dataframe to store encoded values\n",
    "encoded = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "96270c9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        0\n",
       "2        0\n",
       "3        0\n",
       "4        0\n",
       "        ..\n",
       "18974    0\n",
       "18975    0\n",
       "18976    0\n",
       "18977    0\n",
       "18978    0\n",
       "Name: RW, Length: 18852, dtype: int32"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#one hot encoded\n",
    "for token in positions:\n",
    "    encoded[token] = Fifa_df[\"Positions\"].str.contains(token, regex = False).astype(int)\n",
    "encoded[token]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a3b2324b",
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CM</th>\n",
       "      <th>ST</th>\n",
       "      <th>RB</th>\n",
       "      <th>GK</th>\n",
       "      <th>CB</th>\n",
       "      <th>RWB</th>\n",
       "      <th>LB</th>\n",
       "      <th>RM</th>\n",
       "      <th>LM</th>\n",
       "      <th>LW</th>\n",
       "      <th>CDM</th>\n",
       "      <th>CAM</th>\n",
       "      <th>LWB</th>\n",
       "      <th>CF</th>\n",
       "      <th>RW</th>\n",
       "    </tr>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>18974</th>\n",
       "      <td>0</td>\n",
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       "      <th>18976</th>\n",
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       "    <tr>\n",
       "      <th>18977</th>\n",
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       "    <tr>\n",
       "      <th>18978</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18852 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CM  ST  RB  GK  CB  RWB  LB  RM  LM  LW  CDM  CAM  LWB  CF  RW\n",
       "0       0   1   0   0   0    0   0   0   0   0    0    0    0   1   1\n",
       "1       0   1   0   0   0    0   0   0   0   1    0    0    0   0   0\n",
       "2       0   0   0   1   0    0   0   0   0   0    0    0    0   0   0\n",
       "3       1   0   0   0   0    0   0   0   0   0    0    1    0   0   0\n",
       "4       0   0   0   0   0    0   0   0   0   1    0    1    0   0   0\n",
       "...    ..  ..  ..  ..  ..  ...  ..  ..  ..  ..  ...  ...  ...  ..  ..\n",
       "18974   0   0   0   0   1    0   0   0   0   0    0    0    0   0   0\n",
       "18975   0   1   0   0   0    0   0   0   0   0    0    0    0   0   0\n",
       "18976   0   0   0   0   1    0   0   0   0   0    0    0    0   0   0\n",
       "18977   1   0   0   0   0    0   0   0   0   0    0    0    0   0   0\n",
       "18978   0   0   0   0   0    0   0   0   0   0    1    0    0   0   0\n",
       "\n",
       "[18852 rows x 15 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "68f96c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Rename column names\n",
    "Fifa_df = Fifa_df.rename(columns = {\"↓OVA\":\"Overall Rating\", \n",
    "                                    \"POT\": \"Potential\",\n",
    "                                    \"BOV\": \"Best Overall\",\n",
    "                                    \"BP\": \"Best Position\",\n",
    "                                    \"W/F\": \"Weak Foot\",\n",
    "                                    \"SM\": \"Skill Moves\",\n",
    "                                    \"A/W\": \"Attacking Work Rate\",\n",
    "                                    \"D/W\": \"Defensive Work Rate\",\n",
    "                                    \"IR\": \"International Reputation\",\n",
    "                                    \"PAC\": \"Pace\",\n",
    "                                    \"SHO\": \"Shooting\",\n",
    "                                    \"PAS\": \"Passing\",\n",
    "                                    \"DRI\": \"Dribbling\",\n",
    "                                    \"DEF\": \"Defense\",\n",
    "                                    \"PHY\": \"Physicality\" \n",
    "                        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "00696f88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18852 entries, 0 to 18978\n",
      "Data columns (total 75 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   LongName                  18852 non-null  object\n",
      " 1   Nationality               18852 non-null  object\n",
      " 2   Positions                 18852 non-null  object\n",
      " 3   Name                      18852 non-null  object\n",
      " 4   Age                       18852 non-null  int64 \n",
      " 5   Overall Rating            18852 non-null  int64 \n",
      " 6   Potential                 18852 non-null  int64 \n",
      " 7   Team & Contract           18852 non-null  object\n",
      " 8   ID                        18852 non-null  int64 \n",
      " 9   Height                    18852 non-null  object\n",
      " 10  Weight                    18852 non-null  object\n",
      " 11  foot                      18852 non-null  object\n",
      " 12  Best Overall              18852 non-null  int64 \n",
      " 13  Best Position             18852 non-null  object\n",
      " 14  Growth                    18852 non-null  int64 \n",
      " 15  Joined                    18852 non-null  object\n",
      " 16  Loan Date End             1003 non-null   object\n",
      " 17  Value                     18852 non-null  object\n",
      " 18  Wage                      18852 non-null  object\n",
      " 19  Release Clause            18852 non-null  object\n",
      " 20  Attacking                 18852 non-null  int64 \n",
      " 21  Crossing                  18852 non-null  int64 \n",
      " 22  Finishing                 18852 non-null  int64 \n",
      " 23  Heading Accuracy          18852 non-null  int64 \n",
      " 24  Short Passing             18852 non-null  int64 \n",
      " 25  Volleys                   18852 non-null  int64 \n",
      " 26  Skill                     18852 non-null  int64 \n",
      " 27  Dribbling                 18852 non-null  int64 \n",
      " 28  Curve                     18852 non-null  int64 \n",
      " 29  FK Accuracy               18852 non-null  int64 \n",
      " 30  Long Passing              18852 non-null  int64 \n",
      " 31  Ball Control              18852 non-null  int64 \n",
      " 32  Movement                  18852 non-null  int64 \n",
      " 33  Acceleration              18852 non-null  int64 \n",
      " 34  Sprint Speed              18852 non-null  int64 \n",
      " 35  Agility                   18852 non-null  int64 \n",
      " 36  Reactions                 18852 non-null  int64 \n",
      " 37  Balance                   18852 non-null  int64 \n",
      " 38  Power                     18852 non-null  int64 \n",
      " 39  Shot Power                18852 non-null  int64 \n",
      " 40  Jumping                   18852 non-null  int64 \n",
      " 41  Stamina                   18852 non-null  int64 \n",
      " 42  Strength                  18852 non-null  int64 \n",
      " 43  Long Shots                18852 non-null  int64 \n",
      " 44  Mentality                 18852 non-null  int64 \n",
      " 45  Aggression                18852 non-null  int64 \n",
      " 46  Interceptions             18852 non-null  int64 \n",
      " 47  Positioning               18852 non-null  int64 \n",
      " 48  Vision                    18852 non-null  int64 \n",
      " 49  Penalties                 18852 non-null  int64 \n",
      " 50  Composure                 18852 non-null  int64 \n",
      " 51  Defending                 18852 non-null  int64 \n",
      " 52  Marking                   18852 non-null  int64 \n",
      " 53  Standing Tackle           18852 non-null  int64 \n",
      " 54  Sliding Tackle            18852 non-null  int64 \n",
      " 55  Goalkeeping               18852 non-null  int64 \n",
      " 56  GK Diving                 18852 non-null  int64 \n",
      " 57  GK Handling               18852 non-null  int64 \n",
      " 58  GK Kicking                18852 non-null  int64 \n",
      " 59  GK Positioning            18852 non-null  int64 \n",
      " 60  GK Reflexes               18852 non-null  int64 \n",
      " 61  Total Stats               18852 non-null  int64 \n",
      " 62  Base Stats                18852 non-null  int64 \n",
      " 63  Weak Foot                 18852 non-null  object\n",
      " 64  Skill Moves               18852 non-null  object\n",
      " 65  Attacking Work Rate       18852 non-null  object\n",
      " 66  Defensive Work Rate       18852 non-null  object\n",
      " 67  International Reputation  18852 non-null  object\n",
      " 68  Pace                      18852 non-null  int64 \n",
      " 69  Shooting                  18852 non-null  int64 \n",
      " 70  Passing                   18852 non-null  int64 \n",
      " 71  Dribbling                 18852 non-null  int64 \n",
      " 72  Defense                   18852 non-null  int64 \n",
      " 73  Physicality               18852 non-null  int64 \n",
      " 74  Hits                      18852 non-null  object\n",
      "dtypes: int64(55), object(20)\n",
      "memory usage: 10.9+ MB\n"
     ]
    }
   ],
   "source": [
    "Fifa_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "22cfed9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Overall Rating</th>\n",
       "      <th>Potential</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18852.000000</td>\n",
       "      <td>18852.000000</td>\n",
       "      <td>18852.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25.199926</td>\n",
       "      <td>65.745916</td>\n",
       "      <td>71.157384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.710073</td>\n",
       "      <td>6.967116</td>\n",
       "      <td>6.111418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>53.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age  Overall Rating     Potential\n",
       "count  18852.000000    18852.000000  18852.000000\n",
       "mean      25.199926       65.745916     71.157384\n",
       "std        4.710073        6.967116      6.111418\n",
       "min       16.000000       47.000000     47.000000\n",
       "25%       21.000000       61.000000     67.000000\n",
       "50%       25.000000       66.000000     71.000000\n",
       "75%       29.000000       70.000000     75.000000\n",
       "max       53.000000       93.000000     95.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[[\"Age\", \"Overall Rating\", \"Potential\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6f7db1d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                    \\n\\n\\n\\nFC Barcelona\\n2004 ~ 2021\\n\\n\n",
       "1                        \\n\\n\\n\\nJuventus\\n2018 ~ 2022\\n\\n\n",
       "2                 \\n\\n\\n\\nAtlético Madrid\\n2014 ~ 2023\\n\\n\n",
       "3                 \\n\\n\\n\\nManchester City\\n2015 ~ 2023\\n\\n\n",
       "4             \\n\\n\\n\\nParis Saint-Germain\\n2017 ~ 2022\\n\\n\n",
       "                               ...                        \n",
       "18974    \\n\\n\\n\\nChongqing Dangdai Lifan FC SWM Team\\n2...\n",
       "18975             \\n\\n\\n\\nOldham Athletic\\n2020 ~ 2021\\n\\n\n",
       "18976                  \\n\\n\\n\\nWuhan Zall\\n2018 ~ 2022\\n\\n\n",
       "18977             \\n\\n\\n\\nOldham Athletic\\n2020 ~ 2021\\n\\n\n",
       "18978                \\n\\n\\n\\nClub Bolívar\\n2020 ~ 2024\\n\\n\n",
       "Name: Team & Contract, Length: 18852, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Team & Contract\"]"
   ]
  },
  {
   "cell_type": "raw",
   "id": "072c1a96",
   "metadata": {},
   "source": [
    "here 3 columns are extracted 1. team \n",
    "                             2. start year\n",
    "                             3. End year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7cd8e3af",
   "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>Team</th>\n",
       "      <th>Contract</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\\n\\n\\n\\nFC Barcelona</td>\n",
       "      <td>2004 ~ 2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\\n\\n\\n\\nJuventus</td>\n",
       "      <td>2018 ~ 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>\\n\\n\\n\\nAtlético Madrid</td>\n",
       "      <td>2014 ~ 2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\\n\\n\\n\\nManchester City</td>\n",
       "      <td>2015 ~ 2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Germain</td>\n",
       "      <td>2017 ~ 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>\\n\\n\\n\\nFC Bayern München</td>\n",
       "      <td>2014 ~ 2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Germain</td>\n",
       "      <td>2018 ~ 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>\\n\\n\\n\\nLiverpool</td>\n",
       "      <td>2018 ~ 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>\\n\\n\\n\\nLiverpool</td>\n",
       "      <td>2017 ~ 2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>\\n\\n\\n\\nLiverpool</td>\n",
       "      <td>2016 ~ 2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>\\n\\n\\n\\nLiverpool</td>\n",
       "      <td>2018 ~ 2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>\\n\\n\\n\\nFC Barcelona</td>\n",
       "      <td>2014 ~ 2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>\\n\\n\\n\\nReal Madrid</td>\n",
       "      <td>2013 ~ 2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>\\n\\n\\n\\nReal Madrid</td>\n",
       "      <td>2018 ~ 2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>\\n\\n\\n\\nFC Bayern München</td>\n",
       "      <td>2011 ~ 2023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Team     Contract\n",
       "0        \\n\\n\\n\\nFC Barcelona  2004 ~ 2021\n",
       "1            \\n\\n\\n\\nJuventus  2018 ~ 2022\n",
       "2     \\n\\n\\n\\nAtlético Madrid  2014 ~ 2023\n",
       "3     \\n\\n\\n\\nManchester City  2015 ~ 2023\n",
       "4                     Germain  2017 ~ 2022\n",
       "5   \\n\\n\\n\\nFC Bayern München  2014 ~ 2023\n",
       "6                     Germain  2018 ~ 2022\n",
       "7           \\n\\n\\n\\nLiverpool  2018 ~ 2024\n",
       "8           \\n\\n\\n\\nLiverpool  2017 ~ 2023\n",
       "9           \\n\\n\\n\\nLiverpool  2016 ~ 2023\n",
       "10          \\n\\n\\n\\nLiverpool  2018 ~ 2023\n",
       "11       \\n\\n\\n\\nFC Barcelona  2014 ~ 2022\n",
       "12        \\n\\n\\n\\nReal Madrid  2013 ~ 2023\n",
       "13        \\n\\n\\n\\nReal Madrid  2018 ~ 2024\n",
       "14  \\n\\n\\n\\nFC Bayern München  2011 ~ 2023"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract using regex\n",
    "Fifa_df[[\"Team\",\"Contract\"]]=Fifa_df[\"Team & Contract\"].str.extract(r'([\\w\\s]+)\\n(\\d+ ~ \\d+)')\n",
    "Fifa_df[[\"Team\",\"Contract\"]].head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "deee6371",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove leading and trailing whitespace and newline characters\n",
    "Fifa_df[\"Team\"] =Fifa_df[\"Team\"].str.strip()\n",
    "Fifa_df[\"Contract\"] = Fifa_df[\"Contract\"].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "de1c48bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2004 ~ 2021\n",
       "1        2018 ~ 2022\n",
       "2        2014 ~ 2023\n",
       "3        2015 ~ 2023\n",
       "4        2017 ~ 2022\n",
       "            ...     \n",
       "18974    2020 ~ 2020\n",
       "18975    2020 ~ 2021\n",
       "18976    2018 ~ 2022\n",
       "18977    2020 ~ 2021\n",
       "18978    2020 ~ 2024\n",
       "Name: Contract, Length: 18852, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Contract\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5e24e47f",
   "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>Start Year</th>\n",
       "      <th>End Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Start Year End Year\n",
       "0      2004      2021\n",
       "1      2018      2022\n",
       "2      2014      2023\n",
       "3      2015      2023\n",
       "4      2017      2022"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[['Start Year', 'End Year']] = Fifa_df[\"Contract\"].str.split(\"~\", expand = True)\n",
    "Fifa_df[['Start Year', 'End Year']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1dadab00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove leading and trailing whitespace and newline characters\n",
    "Fifa_df[\"Start Year\"] = Fifa_df[\"Start Year\"].str.strip()\n",
    "Fifa_df[\"End Year\"] = Fifa_df[\"End Year\"].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cedb29e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove column\n",
    "Fifa_df.drop(columns=[\"Team & Contract\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a46a96dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove column\n",
    "Fifa_df.drop(columns = [\"Contract\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "944c85d7",
   "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>LongName</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Positions</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Overall Rating</th>\n",
       "      <th>Potential</th>\n",
       "      <th>ID</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>...</th>\n",
       "      <th>Pace</th>\n",
       "      <th>Shooting</th>\n",
       "      <th>Passing</th>\n",
       "      <th>Dribbling</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Physicality</th>\n",
       "      <th>Hits</th>\n",
       "      <th>Team</th>\n",
       "      <th>Start Year</th>\n",
       "      <th>End Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lionel Messi</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>RW ST CF</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>33</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>158023</td>\n",
       "      <td>5'7\"</td>\n",
       "      <td>159lbs</td>\n",
       "      <td>...</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "      <td>95</td>\n",
       "      <td>38</td>\n",
       "      <td>65</td>\n",
       "      <td>\\n372</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>2004</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C. Ronaldo dos Santos Aveiro</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>ST LW</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>35</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>20801</td>\n",
       "      <td>6'2\"</td>\n",
       "      <td>183lbs</td>\n",
       "      <td>...</td>\n",
       "      <td>89</td>\n",
       "      <td>93</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "      <td>35</td>\n",
       "      <td>77</td>\n",
       "      <td>\\n344</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>2018</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jan Oblak</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>GK</td>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>200389</td>\n",
       "      <td>6'2\"</td>\n",
       "      <td>192lbs</td>\n",
       "      <td>...</td>\n",
       "      <td>87</td>\n",
       "      <td>92</td>\n",
       "      <td>78</td>\n",
       "      <td>90</td>\n",
       "      <td>52</td>\n",
       "      <td>90</td>\n",
       "      <td>\\n86</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>2014</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kevin De Bruyne</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>CAM CM</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>29</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>192985</td>\n",
       "      <td>5'11\"</td>\n",
       "      <td>154lbs</td>\n",
       "      <td>...</td>\n",
       "      <td>76</td>\n",
       "      <td>86</td>\n",
       "      <td>93</td>\n",
       "      <td>88</td>\n",
       "      <td>64</td>\n",
       "      <td>78</td>\n",
       "      <td>\\n163</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>2015</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Neymar da Silva Santos Jr.</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>LW CAM</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>28</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>190871</td>\n",
       "      <td>5'9\"</td>\n",
       "      <td>150lbs</td>\n",
       "      <td>...</td>\n",
       "      <td>91</td>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "      <td>94</td>\n",
       "      <td>36</td>\n",
       "      <td>59</td>\n",
       "      <td>\\n273</td>\n",
       "      <td>Germain</td>\n",
       "      <td>2017</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       LongName Nationality Positions               Name  Age  \\\n",
       "0                  Lionel Messi   Argentina  RW ST CF           L. Messi   33   \n",
       "1  C. Ronaldo dos Santos Aveiro    Portugal     ST LW  Cristiano Ronaldo   35   \n",
       "2                     Jan Oblak    Slovenia        GK           J. Oblak   27   \n",
       "3               Kevin De Bruyne     Belgium    CAM CM       K. De Bruyne   29   \n",
       "4    Neymar da Silva Santos Jr.      Brazil    LW CAM          Neymar Jr   28   \n",
       "\n",
       "   Overall Rating  Potential      ID Height  Weight  ... Pace  Shooting  \\\n",
       "0              93         93  158023   5'7\"  159lbs  ...   85        92   \n",
       "1              92         92   20801   6'2\"  183lbs  ...   89        93   \n",
       "2              91         93  200389   6'2\"  192lbs  ...   87        92   \n",
       "3              91         91  192985  5'11\"  154lbs  ...   76        86   \n",
       "4              91         91  190871   5'9\"  150lbs  ...   91        85   \n",
       "\n",
       "  Passing  Dribbling Defense Physicality   Hits             Team Start Year  \\\n",
       "0      91         95      38          65  \\n372     FC Barcelona       2004   \n",
       "1      81         89      35          77  \\n344         Juventus       2018   \n",
       "2      78         90      52          90   \\n86  Atlético Madrid       2014   \n",
       "3      93         88      64          78  \\n163  Manchester City       2015   \n",
       "4      86         94      36          59  \\n273          Germain       2017   \n",
       "\n",
       "   End Year  \n",
       "0      2021  \n",
       "1      2022  \n",
       "2      2023  \n",
       "3      2023  \n",
       "4      2022  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9af7a658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5'7\"\n",
       "1      6'2\"\n",
       "2      6'2\"\n",
       "3     5'11\"\n",
       "4      5'9\"\n",
       "5      6'0\"\n",
       "6     5'10\"\n",
       "7      6'3\"\n",
       "8      5'9\"\n",
       "9      5'9\"\n",
       "10     6'4\"\n",
       "11     6'2\"\n",
       "12     6'1\"\n",
       "13     6'6\"\n",
       "14     6'4\"\n",
       "Name: Height, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Height\"].head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "981bb5e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#extract digits from height\n",
    "pattern = r'(\\d+)\\'(\\d+)\\\"'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "609a4db4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         7\n",
       "1         2\n",
       "2         2\n",
       "3        11\n",
       "4         9\n",
       "         ..\n",
       "18974    10\n",
       "18975     7\n",
       "18976    10\n",
       "18977     9\n",
       "18978     9\n",
       "Name: Inches, Length: 18852, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[[\"Feet\",\"Inches\"]] = Fifa_df[\"Height\"].str.extract(pattern)\n",
    "Fifa_df[[\"Feet\",\"Inches\"]]\n",
    "Fifa_df[\"Feet\"].str.strip()\n",
    "Fifa_df[\"Inches\"].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "92d583ae",
   "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>Feet</th>\n",
       "      <th>Inches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18974</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18975</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18976</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18977</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18978</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18852 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feet Inches\n",
       "0        5      7\n",
       "1        6      2\n",
       "2        6      2\n",
       "3        5     11\n",
       "4        5      9\n",
       "...    ...    ...\n",
       "18974    5     10\n",
       "18975    5      7\n",
       "18976    5     10\n",
       "18977    5      9\n",
       "18978    5      9\n",
       "\n",
       "[18852 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[[\"Feet\",\"Inches\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9742705d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Fifa_df.drop(columns = [\"Height\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "04ababf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert into int \n",
    "Fifa_df['Feet'] = pd.to_numeric(Fifa_df['Feet'], errors='coerce').fillna(0).astype(int)\n",
    "Fifa_df['Inches'] = pd.to_numeric(Fifa_df['Inches'], errors='coerce').fillna(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "03b82139",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18852 entries, 0 to 18978\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   Feet    18852 non-null  int32\n",
      " 1   Inches  18852 non-null  int32\n",
      "dtypes: int32(2)\n",
      "memory usage: 294.6 KB\n"
     ]
    }
   ],
   "source": [
    "Fifa_df[[\"Feet\",\"Inches\"]].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2c5bcf55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18852 entries, 0 to 18978\n",
      "Data columns (total 78 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   LongName                  18852 non-null  object\n",
      " 1   Nationality               18852 non-null  object\n",
      " 2   Positions                 18852 non-null  object\n",
      " 3   Name                      18852 non-null  object\n",
      " 4   Age                       18852 non-null  int64 \n",
      " 5   Overall Rating            18852 non-null  int64 \n",
      " 6   Potential                 18852 non-null  int64 \n",
      " 7   ID                        18852 non-null  int64 \n",
      " 8   Weight                    18852 non-null  object\n",
      " 9   foot                      18852 non-null  object\n",
      " 10  Best Overall              18852 non-null  int64 \n",
      " 11  Best Position             18852 non-null  object\n",
      " 12  Growth                    18852 non-null  int64 \n",
      " 13  Joined                    18852 non-null  object\n",
      " 14  Loan Date End             1003 non-null   object\n",
      " 15  Value                     18852 non-null  object\n",
      " 16  Wage                      18852 non-null  object\n",
      " 17  Release Clause            18852 non-null  object\n",
      " 18  Attacking                 18852 non-null  int64 \n",
      " 19  Crossing                  18852 non-null  int64 \n",
      " 20  Finishing                 18852 non-null  int64 \n",
      " 21  Heading Accuracy          18852 non-null  int64 \n",
      " 22  Short Passing             18852 non-null  int64 \n",
      " 23  Volleys                   18852 non-null  int64 \n",
      " 24  Skill                     18852 non-null  int64 \n",
      " 25  Dribbling                 18852 non-null  int64 \n",
      " 26  Curve                     18852 non-null  int64 \n",
      " 27  FK Accuracy               18852 non-null  int64 \n",
      " 28  Long Passing              18852 non-null  int64 \n",
      " 29  Ball Control              18852 non-null  int64 \n",
      " 30  Movement                  18852 non-null  int64 \n",
      " 31  Acceleration              18852 non-null  int64 \n",
      " 32  Sprint Speed              18852 non-null  int64 \n",
      " 33  Agility                   18852 non-null  int64 \n",
      " 34  Reactions                 18852 non-null  int64 \n",
      " 35  Balance                   18852 non-null  int64 \n",
      " 36  Power                     18852 non-null  int64 \n",
      " 37  Shot Power                18852 non-null  int64 \n",
      " 38  Jumping                   18852 non-null  int64 \n",
      " 39  Stamina                   18852 non-null  int64 \n",
      " 40  Strength                  18852 non-null  int64 \n",
      " 41  Long Shots                18852 non-null  int64 \n",
      " 42  Mentality                 18852 non-null  int64 \n",
      " 43  Aggression                18852 non-null  int64 \n",
      " 44  Interceptions             18852 non-null  int64 \n",
      " 45  Positioning               18852 non-null  int64 \n",
      " 46  Vision                    18852 non-null  int64 \n",
      " 47  Penalties                 18852 non-null  int64 \n",
      " 48  Composure                 18852 non-null  int64 \n",
      " 49  Defending                 18852 non-null  int64 \n",
      " 50  Marking                   18852 non-null  int64 \n",
      " 51  Standing Tackle           18852 non-null  int64 \n",
      " 52  Sliding Tackle            18852 non-null  int64 \n",
      " 53  Goalkeeping               18852 non-null  int64 \n",
      " 54  GK Diving                 18852 non-null  int64 \n",
      " 55  GK Handling               18852 non-null  int64 \n",
      " 56  GK Kicking                18852 non-null  int64 \n",
      " 57  GK Positioning            18852 non-null  int64 \n",
      " 58  GK Reflexes               18852 non-null  int64 \n",
      " 59  Total Stats               18852 non-null  int64 \n",
      " 60  Base Stats                18852 non-null  int64 \n",
      " 61  Weak Foot                 18852 non-null  object\n",
      " 62  Skill Moves               18852 non-null  object\n",
      " 63  Attacking Work Rate       18852 non-null  object\n",
      " 64  Defensive Work Rate       18852 non-null  object\n",
      " 65  International Reputation  18852 non-null  object\n",
      " 66  Pace                      18852 non-null  int64 \n",
      " 67  Shooting                  18852 non-null  int64 \n",
      " 68  Passing                   18852 non-null  int64 \n",
      " 69  Dribbling                 18852 non-null  int64 \n",
      " 70  Defense                   18852 non-null  int64 \n",
      " 71  Physicality               18852 non-null  int64 \n",
      " 72  Hits                      18852 non-null  object\n",
      " 73  Team                      17433 non-null  object\n",
      " 74  Start Year                17433 non-null  object\n",
      " 75  End Year                  17433 non-null  object\n",
      " 76  Feet                      18852 non-null  int32 \n",
      " 77  Inches                    18852 non-null  int32 \n",
      "dtypes: int32(2), int64(55), object(21)\n",
      "memory usage: 11.2+ MB\n"
     ]
    }
   ],
   "source": [
    "Fifa_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4428710b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        159.40\n",
       "1        184.88\n",
       "2        184.88\n",
       "3        163.40\n",
       "4        161.40\n",
       "          ...  \n",
       "18974    162.40\n",
       "18975    159.40\n",
       "18976    162.40\n",
       "18977    161.40\n",
       "18978    161.40\n",
       "Name: Height, Length: 18852, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# conversion of height into cm\n",
    "Fifa_df[\"Height\"] = Fifa_df[\"Feet\"] * 30.48 + Fifa_df[\"Inches\"]\n",
    "Fifa_df[\"Height\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "24dfe373",
   "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>LongName</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Positions</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Overall Rating</th>\n",
       "      <th>Potential</th>\n",
       "      <th>ID</th>\n",
       "      <th>Weight</th>\n",
       "      <th>foot</th>\n",
       "      <th>...</th>\n",
       "      <th>Shooting</th>\n",
       "      <th>Passing</th>\n",
       "      <th>Dribbling</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Physicality</th>\n",
       "      <th>Hits</th>\n",
       "      <th>Team</th>\n",
       "      <th>Start Year</th>\n",
       "      <th>End Year</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lionel Messi</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>RW ST CF</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>33</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>158023</td>\n",
       "      <td>159lbs</td>\n",
       "      <td>Left</td>\n",
       "      <td>...</td>\n",
       "      <td>92</td>\n",
       "      <td>91</td>\n",
       "      <td>95</td>\n",
       "      <td>38</td>\n",
       "      <td>65</td>\n",
       "      <td>\\n372</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>2004</td>\n",
       "      <td>2021</td>\n",
       "      <td>159.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C. Ronaldo dos Santos Aveiro</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>ST LW</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>35</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>20801</td>\n",
       "      <td>183lbs</td>\n",
       "      <td>Right</td>\n",
       "      <td>...</td>\n",
       "      <td>93</td>\n",
       "      <td>81</td>\n",
       "      <td>89</td>\n",
       "      <td>35</td>\n",
       "      <td>77</td>\n",
       "      <td>\\n344</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>2018</td>\n",
       "      <td>2022</td>\n",
       "      <td>184.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jan Oblak</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>GK</td>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>200389</td>\n",
       "      <td>192lbs</td>\n",
       "      <td>Right</td>\n",
       "      <td>...</td>\n",
       "      <td>92</td>\n",
       "      <td>78</td>\n",
       "      <td>90</td>\n",
       "      <td>52</td>\n",
       "      <td>90</td>\n",
       "      <td>\\n86</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>2014</td>\n",
       "      <td>2023</td>\n",
       "      <td>184.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kevin De Bruyne</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>CAM CM</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>29</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>192985</td>\n",
       "      <td>154lbs</td>\n",
       "      <td>Right</td>\n",
       "      <td>...</td>\n",
       "      <td>86</td>\n",
       "      <td>93</td>\n",
       "      <td>88</td>\n",
       "      <td>64</td>\n",
       "      <td>78</td>\n",
       "      <td>\\n163</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>2015</td>\n",
       "      <td>2023</td>\n",
       "      <td>163.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Neymar da Silva Santos Jr.</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>LW CAM</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>28</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>190871</td>\n",
       "      <td>150lbs</td>\n",
       "      <td>Right</td>\n",
       "      <td>...</td>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "      <td>94</td>\n",
       "      <td>36</td>\n",
       "      <td>59</td>\n",
       "      <td>\\n273</td>\n",
       "      <td>Germain</td>\n",
       "      <td>2017</td>\n",
       "      <td>2022</td>\n",
       "      <td>161.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       LongName Nationality Positions               Name  Age  \\\n",
       "0                  Lionel Messi   Argentina  RW ST CF           L. Messi   33   \n",
       "1  C. Ronaldo dos Santos Aveiro    Portugal     ST LW  Cristiano Ronaldo   35   \n",
       "2                     Jan Oblak    Slovenia        GK           J. Oblak   27   \n",
       "3               Kevin De Bruyne     Belgium    CAM CM       K. De Bruyne   29   \n",
       "4    Neymar da Silva Santos Jr.      Brazil    LW CAM          Neymar Jr   28   \n",
       "\n",
       "   Overall Rating  Potential      ID  Weight   foot  ...  Shooting Passing  \\\n",
       "0              93         93  158023  159lbs   Left  ...        92      91   \n",
       "1              92         92   20801  183lbs  Right  ...        93      81   \n",
       "2              91         93  200389  192lbs  Right  ...        92      78   \n",
       "3              91         91  192985  154lbs  Right  ...        86      93   \n",
       "4              91         91  190871  150lbs  Right  ...        85      86   \n",
       "\n",
       "   Dribbling Defense Physicality   Hits             Team Start Year  End Year  \\\n",
       "0         95      38          65  \\n372     FC Barcelona       2004      2021   \n",
       "1         89      35          77  \\n344         Juventus       2018      2022   \n",
       "2         90      52          90   \\n86  Atlético Madrid       2014      2023   \n",
       "3         88      64          78  \\n163  Manchester City       2015      2023   \n",
       "4         94      36          59  \\n273          Germain       2017      2022   \n",
       "\n",
       "   Height  \n",
       "0  159.40  \n",
       "1  184.88  \n",
       "2  184.88  \n",
       "3  163.40  \n",
       "4  161.40  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove extra column after manipulation\n",
    "Fifa_df.drop(columns = [\"Feet\"], inplace = True)\n",
    "Fifa_df.drop(columns = [\"Inches\"], inplace = True)\n",
    "Fifa_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a8d26d88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        159lbs\n",
       "1        183lbs\n",
       "2        192lbs\n",
       "3        154lbs\n",
       "4        150lbs\n",
       "          ...  \n",
       "18974    154lbs\n",
       "18975    128lbs\n",
       "18976    146lbs\n",
       "18977    143lbs\n",
       "18978    150lbs\n",
       "Name: Weight, Length: 18852, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Weight\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "51cc4359",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert string into int\n",
    "Fifa_df[\"Weight\"] = Fifa_df[\"Weight\"].str.extract('(\\d+)').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c4c0b3a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Int64Index: 18852 entries, 0 to 18978\n",
      "Series name: Weight\n",
      "Non-Null Count  Dtype\n",
      "--------------  -----\n",
      "18852 non-null  int32\n",
      "dtypes: int32(1)\n",
      "memory usage: 220.9 KB\n"
     ]
    }
   ],
   "source": [
    "Fifa_df[\"Weight\"].info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c4747dd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        72.121128\n",
       "1        83.007336\n",
       "2        87.089664\n",
       "3        69.853168\n",
       "4        68.038800\n",
       "           ...    \n",
       "18974    69.853168\n",
       "18975    58.059776\n",
       "18976    66.224432\n",
       "18977    64.863656\n",
       "18978    68.038800\n",
       "Name: Weight, Length: 18852, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert lbs to kg\n",
    "Fifa_df[\"Weight\"] = Fifa_df[\"Weight\"]*0.453592 \n",
    "Fifa_df[\"Weight\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ddafe786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Best Overall\"].isna().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "41728a2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     RW\n",
       "1     ST\n",
       "2     GK\n",
       "3    CAM\n",
       "4     LW\n",
       "Name: Best Position, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Best Position\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "df92a0e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18852 entries, 0 to 18978\n",
      "Data columns (total 77 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   LongName                  18852 non-null  object \n",
      " 1   Nationality               18852 non-null  object \n",
      " 2   Positions                 18852 non-null  object \n",
      " 3   Name                      18852 non-null  object \n",
      " 4   Age                       18852 non-null  int64  \n",
      " 5   Overall Rating            18852 non-null  int64  \n",
      " 6   Potential                 18852 non-null  int64  \n",
      " 7   ID                        18852 non-null  int64  \n",
      " 8   Weight                    18852 non-null  float64\n",
      " 9   foot                      18852 non-null  object \n",
      " 10  Best Overall              18852 non-null  int64  \n",
      " 11  Best Position             18852 non-null  object \n",
      " 12  Growth                    18852 non-null  int64  \n",
      " 13  Joined                    18852 non-null  object \n",
      " 14  Loan Date End             1003 non-null   object \n",
      " 15  Value                     18852 non-null  object \n",
      " 16  Wage                      18852 non-null  object \n",
      " 17  Release Clause            18852 non-null  object \n",
      " 18  Attacking                 18852 non-null  int64  \n",
      " 19  Crossing                  18852 non-null  int64  \n",
      " 20  Finishing                 18852 non-null  int64  \n",
      " 21  Heading Accuracy          18852 non-null  int64  \n",
      " 22  Short Passing             18852 non-null  int64  \n",
      " 23  Volleys                   18852 non-null  int64  \n",
      " 24  Skill                     18852 non-null  int64  \n",
      " 25  Dribbling                 18852 non-null  int64  \n",
      " 26  Curve                     18852 non-null  int64  \n",
      " 27  FK Accuracy               18852 non-null  int64  \n",
      " 28  Long Passing              18852 non-null  int64  \n",
      " 29  Ball Control              18852 non-null  int64  \n",
      " 30  Movement                  18852 non-null  int64  \n",
      " 31  Acceleration              18852 non-null  int64  \n",
      " 32  Sprint Speed              18852 non-null  int64  \n",
      " 33  Agility                   18852 non-null  int64  \n",
      " 34  Reactions                 18852 non-null  int64  \n",
      " 35  Balance                   18852 non-null  int64  \n",
      " 36  Power                     18852 non-null  int64  \n",
      " 37  Shot Power                18852 non-null  int64  \n",
      " 38  Jumping                   18852 non-null  int64  \n",
      " 39  Stamina                   18852 non-null  int64  \n",
      " 40  Strength                  18852 non-null  int64  \n",
      " 41  Long Shots                18852 non-null  int64  \n",
      " 42  Mentality                 18852 non-null  int64  \n",
      " 43  Aggression                18852 non-null  int64  \n",
      " 44  Interceptions             18852 non-null  int64  \n",
      " 45  Positioning               18852 non-null  int64  \n",
      " 46  Vision                    18852 non-null  int64  \n",
      " 47  Penalties                 18852 non-null  int64  \n",
      " 48  Composure                 18852 non-null  int64  \n",
      " 49  Defending                 18852 non-null  int64  \n",
      " 50  Marking                   18852 non-null  int64  \n",
      " 51  Standing Tackle           18852 non-null  int64  \n",
      " 52  Sliding Tackle            18852 non-null  int64  \n",
      " 53  Goalkeeping               18852 non-null  int64  \n",
      " 54  GK Diving                 18852 non-null  int64  \n",
      " 55  GK Handling               18852 non-null  int64  \n",
      " 56  GK Kicking                18852 non-null  int64  \n",
      " 57  GK Positioning            18852 non-null  int64  \n",
      " 58  GK Reflexes               18852 non-null  int64  \n",
      " 59  Total Stats               18852 non-null  int64  \n",
      " 60  Base Stats                18852 non-null  int64  \n",
      " 61  Weak Foot                 18852 non-null  object \n",
      " 62  Skill Moves               18852 non-null  object \n",
      " 63  Attacking Work Rate       18852 non-null  object \n",
      " 64  Defensive Work Rate       18852 non-null  object \n",
      " 65  International Reputation  18852 non-null  object \n",
      " 66  Pace                      18852 non-null  int64  \n",
      " 67  Shooting                  18852 non-null  int64  \n",
      " 68  Passing                   18852 non-null  int64  \n",
      " 69  Dribbling                 18852 non-null  int64  \n",
      " 70  Defense                   18852 non-null  int64  \n",
      " 71  Physicality               18852 non-null  int64  \n",
      " 72  Hits                      18852 non-null  object \n",
      " 73  Team                      17433 non-null  object \n",
      " 74  Start Year                17433 non-null  object \n",
      " 75  End Year                  17433 non-null  object \n",
      " 76  Height                    18852 non-null  float64\n",
      "dtypes: float64(2), int64(55), object(20)\n",
      "memory usage: 11.2+ MB\n"
     ]
    }
   ],
   "source": [
    "Fifa_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "08c26ffa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18852.000000\n",
       "mean         5.411468\n",
       "std          5.662399\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          4.000000\n",
       "75%          9.000000\n",
       "max         26.000000\n",
       "Name: Growth, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Growth\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "85d8612d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         Jul 1, 2004\n",
       "1        Jul 10, 2018\n",
       "2        Jul 16, 2014\n",
       "3        Aug 30, 2015\n",
       "4         Aug 3, 2017\n",
       "             ...     \n",
       "18974     Aug 1, 2020\n",
       "18975     Aug 1, 2020\n",
       "18976    Jul 13, 2018\n",
       "18977     Aug 1, 2020\n",
       "18978     Jan 1, 2020\n",
       "Name: Joined, Length: 18852, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df['Joined']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2e249d57",
   "metadata": {},
   "outputs": [],
   "source": [
    "Fifa_df[\"Date\"] = pd.to_datetime(Fifa_df[\"Joined\"], format='%b %d, %Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "07ce23cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "Fifa_df.drop(columns = [\"Joined\"],inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a99428fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Jun 30, 2021', 'Dec 31, 2020', 'Jan 30, 2021',\n",
       "       'Jun 30, 2022', 'May 31, 2021', 'Jul 5, 2021', 'Dec 31, 2021',\n",
       "       'Jul 1, 2021', 'Jan 1, 2021', 'Aug 31, 2021', 'Jan 31, 2021',\n",
       "       'Dec 30, 2021', 'Jun 23, 2021', 'Jan 3, 2021', 'Nov 27, 2021',\n",
       "       'Jan 17, 2021', 'Jun 30, 2023', 'Jul 31, 2021', 'Nov 22, 2020',\n",
       "       'May 31, 2022', 'Dec 30, 2020', 'Jan 4, 2021', 'Nov 30, 2020',\n",
       "       'Aug 1, 2021'], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Loan Date End\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a0404c46",
   "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>Value</th>\n",
       "      <th>Wage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>€67.5M</td>\n",
       "      <td>€560K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>€46M</td>\n",
       "      <td>€220K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>€75M</td>\n",
       "      <td>€125K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>€87M</td>\n",
       "      <td>€370K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>€90M</td>\n",
       "      <td>€270K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18974</th>\n",
       "      <td>€35K</td>\n",
       "      <td>€1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18975</th>\n",
       "      <td>€60K</td>\n",
       "      <td>€500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18976</th>\n",
       "      <td>€40K</td>\n",
       "      <td>€1K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18977</th>\n",
       "      <td>€60K</td>\n",
       "      <td>€500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18978</th>\n",
       "      <td>€60K</td>\n",
       "      <td>€500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18852 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Value   Wage\n",
       "0      €67.5M  €560K\n",
       "1        €46M  €220K\n",
       "2        €75M  €125K\n",
       "3        €87M  €370K\n",
       "4        €90M  €270K\n",
       "...       ...    ...\n",
       "18974    €35K    €1K\n",
       "18975    €60K   €500\n",
       "18976    €40K    €1K\n",
       "18977    €60K   €500\n",
       "18978    €60K   €500\n",
       "\n",
       "[18852 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[[\"Value\", \"Wage\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a02a85cf",
   "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>Value</th>\n",
       "      <th>Wage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67500000.0</td>\n",
       "      <td>560000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46000000.0</td>\n",
       "      <td>220000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75000000.0</td>\n",
       "      <td>125000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>87000000.0</td>\n",
       "      <td>370000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90000000.0</td>\n",
       "      <td>270000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18974</th>\n",
       "      <td>35000.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18975</th>\n",
       "      <td>60000.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18976</th>\n",
       "      <td>40000.0</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18977</th>\n",
       "      <td>60000.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18978</th>\n",
       "      <td>60000.0</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18852 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Value      Wage\n",
       "0      67500000.0  560000.0\n",
       "1      46000000.0  220000.0\n",
       "2      75000000.0  125000.0\n",
       "3      87000000.0  370000.0\n",
       "4      90000000.0  270000.0\n",
       "...           ...       ...\n",
       "18974     35000.0    1000.0\n",
       "18975     60000.0     500.0\n",
       "18976     40000.0    1000.0\n",
       "18977     60000.0     500.0\n",
       "18978     60000.0     500.0\n",
       "\n",
       "[18852 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating function to convert string to numeric\n",
    "def convert_to_numeric(value_str):\n",
    "    #Remove currency symbol\n",
    "    value_str = value_str.replace('€', '').replace(',', '')\n",
    "    if 'K' in value_str:\n",
    "        return float(value_str.replace('K', '')) * 1000\n",
    "    elif 'M' in value_str:\n",
    "        return float(value_str.replace('M', '')) * 1000000\n",
    "    else:\n",
    "        return float(value_str)\n",
    "# Apply the conversion function to the \"Wage\" and \"Value\" columns\n",
    "Fifa_df[\"Wage\"] = Fifa_df[\"Wage\"].apply(convert_to_numeric)\n",
    "Fifa_df[\"Value\"] = Fifa_df[\"Value\"].apply(convert_to_numeric)\n",
    "Fifa_df[[\"Value\", \"Wage\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1622a24b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        €138.4M\n",
       "1         €75.9M\n",
       "2        €159.4M\n",
       "3          €161M\n",
       "4        €166.5M\n",
       "          ...   \n",
       "18974       €57K\n",
       "18975      €165K\n",
       "18976       €70K\n",
       "18977      €165K\n",
       "18978      €167K\n",
       "Name: Release Clause, Length: 18852, dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Release Clause\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c9672ae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        138400000.0\n",
       "1         75900000.0\n",
       "2        159400000.0\n",
       "3        161000000.0\n",
       "4        166500000.0\n",
       "            ...     \n",
       "18974        57000.0\n",
       "18975       165000.0\n",
       "18976        70000.0\n",
       "18977       165000.0\n",
       "18978       167000.0\n",
       "Name: Release Clause, Length: 18852, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#apply convert_to_numeric function\n",
    "Fifa_df[\"Release Clause\"] = Fifa_df[\"Release Clause\"].apply(convert_to_numeric)\n",
    "Fifa_df[\"Release Clause\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "28bc7554",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18852 entries, 0 to 18978\n",
      "Data columns (total 77 columns):\n",
      " #   Column                    Non-Null Count  Dtype         \n",
      "---  ------                    --------------  -----         \n",
      " 0   LongName                  18852 non-null  object        \n",
      " 1   Nationality               18852 non-null  object        \n",
      " 2   Positions                 18852 non-null  object        \n",
      " 3   Name                      18852 non-null  object        \n",
      " 4   Age                       18852 non-null  int64         \n",
      " 5   Overall Rating            18852 non-null  int64         \n",
      " 6   Potential                 18852 non-null  int64         \n",
      " 7   ID                        18852 non-null  int64         \n",
      " 8   Weight                    18852 non-null  float64       \n",
      " 9   foot                      18852 non-null  object        \n",
      " 10  Best Overall              18852 non-null  int64         \n",
      " 11  Best Position             18852 non-null  object        \n",
      " 12  Growth                    18852 non-null  int64         \n",
      " 13  Loan Date End             1003 non-null   object        \n",
      " 14  Value                     18852 non-null  float64       \n",
      " 15  Wage                      18852 non-null  float64       \n",
      " 16  Release Clause            18852 non-null  float64       \n",
      " 17  Attacking                 18852 non-null  int64         \n",
      " 18  Crossing                  18852 non-null  int64         \n",
      " 19  Finishing                 18852 non-null  int64         \n",
      " 20  Heading Accuracy          18852 non-null  int64         \n",
      " 21  Short Passing             18852 non-null  int64         \n",
      " 22  Volleys                   18852 non-null  int64         \n",
      " 23  Skill                     18852 non-null  int64         \n",
      " 24  Dribbling                 18852 non-null  int64         \n",
      " 25  Curve                     18852 non-null  int64         \n",
      " 26  FK Accuracy               18852 non-null  int64         \n",
      " 27  Long Passing              18852 non-null  int64         \n",
      " 28  Ball Control              18852 non-null  int64         \n",
      " 29  Movement                  18852 non-null  int64         \n",
      " 30  Acceleration              18852 non-null  int64         \n",
      " 31  Sprint Speed              18852 non-null  int64         \n",
      " 32  Agility                   18852 non-null  int64         \n",
      " 33  Reactions                 18852 non-null  int64         \n",
      " 34  Balance                   18852 non-null  int64         \n",
      " 35  Power                     18852 non-null  int64         \n",
      " 36  Shot Power                18852 non-null  int64         \n",
      " 37  Jumping                   18852 non-null  int64         \n",
      " 38  Stamina                   18852 non-null  int64         \n",
      " 39  Strength                  18852 non-null  int64         \n",
      " 40  Long Shots                18852 non-null  int64         \n",
      " 41  Mentality                 18852 non-null  int64         \n",
      " 42  Aggression                18852 non-null  int64         \n",
      " 43  Interceptions             18852 non-null  int64         \n",
      " 44  Positioning               18852 non-null  int64         \n",
      " 45  Vision                    18852 non-null  int64         \n",
      " 46  Penalties                 18852 non-null  int64         \n",
      " 47  Composure                 18852 non-null  int64         \n",
      " 48  Defending                 18852 non-null  int64         \n",
      " 49  Marking                   18852 non-null  int64         \n",
      " 50  Standing Tackle           18852 non-null  int64         \n",
      " 51  Sliding Tackle            18852 non-null  int64         \n",
      " 52  Goalkeeping               18852 non-null  int64         \n",
      " 53  GK Diving                 18852 non-null  int64         \n",
      " 54  GK Handling               18852 non-null  int64         \n",
      " 55  GK Kicking                18852 non-null  int64         \n",
      " 56  GK Positioning            18852 non-null  int64         \n",
      " 57  GK Reflexes               18852 non-null  int64         \n",
      " 58  Total Stats               18852 non-null  int64         \n",
      " 59  Base Stats                18852 non-null  int64         \n",
      " 60  Weak Foot                 18852 non-null  object        \n",
      " 61  Skill Moves               18852 non-null  object        \n",
      " 62  Attacking Work Rate       18852 non-null  object        \n",
      " 63  Defensive Work Rate       18852 non-null  object        \n",
      " 64  International Reputation  18852 non-null  object        \n",
      " 65  Pace                      18852 non-null  int64         \n",
      " 66  Shooting                  18852 non-null  int64         \n",
      " 67  Passing                   18852 non-null  int64         \n",
      " 68  Dribbling                 18852 non-null  int64         \n",
      " 69  Defense                   18852 non-null  int64         \n",
      " 70  Physicality               18852 non-null  int64         \n",
      " 71  Hits                      18852 non-null  object        \n",
      " 72  Team                      17433 non-null  object        \n",
      " 73  Start Year                17433 non-null  object        \n",
      " 74  End Year                  17433 non-null  object        \n",
      " 75  Height                    18852 non-null  float64       \n",
      " 76  Date                      18852 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(5), int64(55), object(16)\n",
      "memory usage: 11.2+ MB\n"
     ]
    }
   ],
   "source": [
    "Fifa_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dcf8882c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                               FC Barcelona\n",
       "1                                   Juventus\n",
       "2                            Atlético Madrid\n",
       "3                            Manchester City\n",
       "4                                    Germain\n",
       "                        ...                 \n",
       "18974    Chongqing Dangdai Lifan FC SWM Team\n",
       "18975                        Oldham Athletic\n",
       "18976                             Wuhan Zall\n",
       "18977                        Oldham Athletic\n",
       "18978                           Club Bolívar\n",
       "Name: Team, Length: 18852, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Team\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7267352a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4 ★', '3 ★', '5 ★', '2 ★', '1 ★'], dtype=object)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Weak Foot\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1e706d53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['4★', '5★', '1★', '2★', '3★'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Skill Moves\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "84a32675",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Medium', 'High', 'Low'], dtype=object)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Attacking Work Rate\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ae62ddaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Low', 'Medium', 'High'], dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Defensive Work Rate\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "15982148",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['5 ★', '3 ★', '4 ★', '2 ★', '1 ★'], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"International Reputation\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6c9842d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        372\n",
       "1        344\n",
       "2         86\n",
       "3        163\n",
       "4        273\n",
       "        ... \n",
       "18974      2\n",
       "18975      3\n",
       "18976      3\n",
       "18977      5\n",
       "18978      2\n",
       "Name: Hits, Length: 18852, dtype: int32"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert string to numeric by extract method using regural expression\n",
    "Fifa_df[\"Hits\"]= Fifa_df[\"Hits\"].str.extract('(\\d+)').astype(int)\n",
    "Fifa_df[\"Hits\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ad8de1b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18852.000000\n",
       "mean        15.605347\n",
       "std         48.501187\n",
       "min          1.000000\n",
       "25%          2.000000\n",
       "50%          3.000000\n",
       "75%         10.000000\n",
       "max        984.000000\n",
       "Name: Hits, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df['Hits'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0f1a445c",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_fill = [\"Hits\", \"Team\", \"Start Year\", \"End Year\"]\n",
    "Fifa_df[columns_to_fill] = Fifa_df[columns_to_fill].fillna(\"N/A\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e433062b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18852 entries, 0 to 18978\n",
      "Data columns (total 77 columns):\n",
      " #   Column                    Non-Null Count  Dtype         \n",
      "---  ------                    --------------  -----         \n",
      " 0   LongName                  18852 non-null  object        \n",
      " 1   Nationality               18852 non-null  object        \n",
      " 2   Positions                 18852 non-null  object        \n",
      " 3   Name                      18852 non-null  object        \n",
      " 4   Age                       18852 non-null  int64         \n",
      " 5   Overall Rating            18852 non-null  int64         \n",
      " 6   Potential                 18852 non-null  int64         \n",
      " 7   ID                        18852 non-null  int64         \n",
      " 8   Weight                    18852 non-null  float64       \n",
      " 9   foot                      18852 non-null  object        \n",
      " 10  Best Overall              18852 non-null  int64         \n",
      " 11  Best Position             18852 non-null  object        \n",
      " 12  Growth                    18852 non-null  int64         \n",
      " 13  Loan Date End             1003 non-null   object        \n",
      " 14  Value                     18852 non-null  float64       \n",
      " 15  Wage                      18852 non-null  float64       \n",
      " 16  Release Clause            18852 non-null  float64       \n",
      " 17  Attacking                 18852 non-null  int64         \n",
      " 18  Crossing                  18852 non-null  int64         \n",
      " 19  Finishing                 18852 non-null  int64         \n",
      " 20  Heading Accuracy          18852 non-null  int64         \n",
      " 21  Short Passing             18852 non-null  int64         \n",
      " 22  Volleys                   18852 non-null  int64         \n",
      " 23  Skill                     18852 non-null  int64         \n",
      " 24  Dribbling                 18852 non-null  int64         \n",
      " 25  Curve                     18852 non-null  int64         \n",
      " 26  FK Accuracy               18852 non-null  int64         \n",
      " 27  Long Passing              18852 non-null  int64         \n",
      " 28  Ball Control              18852 non-null  int64         \n",
      " 29  Movement                  18852 non-null  int64         \n",
      " 30  Acceleration              18852 non-null  int64         \n",
      " 31  Sprint Speed              18852 non-null  int64         \n",
      " 32  Agility                   18852 non-null  int64         \n",
      " 33  Reactions                 18852 non-null  int64         \n",
      " 34  Balance                   18852 non-null  int64         \n",
      " 35  Power                     18852 non-null  int64         \n",
      " 36  Shot Power                18852 non-null  int64         \n",
      " 37  Jumping                   18852 non-null  int64         \n",
      " 38  Stamina                   18852 non-null  int64         \n",
      " 39  Strength                  18852 non-null  int64         \n",
      " 40  Long Shots                18852 non-null  int64         \n",
      " 41  Mentality                 18852 non-null  int64         \n",
      " 42  Aggression                18852 non-null  int64         \n",
      " 43  Interceptions             18852 non-null  int64         \n",
      " 44  Positioning               18852 non-null  int64         \n",
      " 45  Vision                    18852 non-null  int64         \n",
      " 46  Penalties                 18852 non-null  int64         \n",
      " 47  Composure                 18852 non-null  int64         \n",
      " 48  Defending                 18852 non-null  int64         \n",
      " 49  Marking                   18852 non-null  int64         \n",
      " 50  Standing Tackle           18852 non-null  int64         \n",
      " 51  Sliding Tackle            18852 non-null  int64         \n",
      " 52  Goalkeeping               18852 non-null  int64         \n",
      " 53  GK Diving                 18852 non-null  int64         \n",
      " 54  GK Handling               18852 non-null  int64         \n",
      " 55  GK Kicking                18852 non-null  int64         \n",
      " 56  GK Positioning            18852 non-null  int64         \n",
      " 57  GK Reflexes               18852 non-null  int64         \n",
      " 58  Total Stats               18852 non-null  int64         \n",
      " 59  Base Stats                18852 non-null  int64         \n",
      " 60  Weak Foot                 18852 non-null  object        \n",
      " 61  Skill Moves               18852 non-null  object        \n",
      " 62  Attacking Work Rate       18852 non-null  object        \n",
      " 63  Defensive Work Rate       18852 non-null  object        \n",
      " 64  International Reputation  18852 non-null  object        \n",
      " 65  Pace                      18852 non-null  int64         \n",
      " 66  Shooting                  18852 non-null  int64         \n",
      " 67  Passing                   18852 non-null  int64         \n",
      " 68  Dribbling                 18852 non-null  int64         \n",
      " 69  Defense                   18852 non-null  int64         \n",
      " 70  Physicality               18852 non-null  int64         \n",
      " 71  Hits                      18852 non-null  int32         \n",
      " 72  Team                      18852 non-null  object        \n",
      " 73  Start Year                18852 non-null  object        \n",
      " 74  End Year                  18852 non-null  object        \n",
      " 75  Height                    18852 non-null  float64       \n",
      " 76  Date                      18852 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(5), int32(1), int64(55), object(15)\n",
      "memory usage: 11.1+ MB\n"
     ]
    }
   ],
   "source": [
    "Fifa_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3f907e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2004-07-01\n",
       "1       2018-07-10\n",
       "2       2014-07-16\n",
       "3       2015-08-30\n",
       "4       2017-08-03\n",
       "           ...    \n",
       "18974   2020-08-01\n",
       "18975   2020-08-01\n",
       "18976   2018-07-13\n",
       "18977   2020-08-01\n",
       "18978   2020-01-01\n",
       "Name: Date, Length: 18852, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[\"Date\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3023215b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Fifa_df[\"Year\"] = Fifa_df[\"Date\"].dt.year\n",
    "Fifa_df[\"Month\"] = Fifa_df[\"Date\"].dt.month\n",
    "Fifa_df[\"Day\"] = Fifa_df[\"Date\"].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "98f576d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2004-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-07-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>2014-07-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>2015-08-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2017-08-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18974</th>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18975</th>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18976</th>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>2018-07-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18977</th>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18978</th>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18852 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year  Month  Day       Date\n",
       "0      2004      7    1 2004-07-01\n",
       "1      2018      7   10 2018-07-10\n",
       "2      2014      7   16 2014-07-16\n",
       "3      2015      8   30 2015-08-30\n",
       "4      2017      8    3 2017-08-03\n",
       "...     ...    ...  ...        ...\n",
       "18974  2020      8    1 2020-08-01\n",
       "18975  2020      8    1 2020-08-01\n",
       "18976  2018      7   13 2018-07-13\n",
       "18977  2020      8    1 2020-08-01\n",
       "18978  2020      1    1 2020-01-01\n",
       "\n",
       "[18852 rows x 4 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df[['Year','Month','Day', 'Date']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c3506c42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18852 entries, 0 to 18978\n",
      "Data columns (total 80 columns):\n",
      " #   Column                    Non-Null Count  Dtype         \n",
      "---  ------                    --------------  -----         \n",
      " 0   LongName                  18852 non-null  object        \n",
      " 1   Nationality               18852 non-null  object        \n",
      " 2   Positions                 18852 non-null  object        \n",
      " 3   Name                      18852 non-null  object        \n",
      " 4   Age                       18852 non-null  int64         \n",
      " 5   Overall Rating            18852 non-null  int64         \n",
      " 6   Potential                 18852 non-null  int64         \n",
      " 7   ID                        18852 non-null  int64         \n",
      " 8   Weight                    18852 non-null  float64       \n",
      " 9   foot                      18852 non-null  object        \n",
      " 10  Best Overall              18852 non-null  int64         \n",
      " 11  Best Position             18852 non-null  object        \n",
      " 12  Growth                    18852 non-null  int64         \n",
      " 13  Loan Date End             1003 non-null   object        \n",
      " 14  Value                     18852 non-null  float64       \n",
      " 15  Wage                      18852 non-null  float64       \n",
      " 16  Release Clause            18852 non-null  float64       \n",
      " 17  Attacking                 18852 non-null  int64         \n",
      " 18  Crossing                  18852 non-null  int64         \n",
      " 19  Finishing                 18852 non-null  int64         \n",
      " 20  Heading Accuracy          18852 non-null  int64         \n",
      " 21  Short Passing             18852 non-null  int64         \n",
      " 22  Volleys                   18852 non-null  int64         \n",
      " 23  Skill                     18852 non-null  int64         \n",
      " 24  Dribbling                 18852 non-null  int64         \n",
      " 25  Curve                     18852 non-null  int64         \n",
      " 26  FK Accuracy               18852 non-null  int64         \n",
      " 27  Long Passing              18852 non-null  int64         \n",
      " 28  Ball Control              18852 non-null  int64         \n",
      " 29  Movement                  18852 non-null  int64         \n",
      " 30  Acceleration              18852 non-null  int64         \n",
      " 31  Sprint Speed              18852 non-null  int64         \n",
      " 32  Agility                   18852 non-null  int64         \n",
      " 33  Reactions                 18852 non-null  int64         \n",
      " 34  Balance                   18852 non-null  int64         \n",
      " 35  Power                     18852 non-null  int64         \n",
      " 36  Shot Power                18852 non-null  int64         \n",
      " 37  Jumping                   18852 non-null  int64         \n",
      " 38  Stamina                   18852 non-null  int64         \n",
      " 39  Strength                  18852 non-null  int64         \n",
      " 40  Long Shots                18852 non-null  int64         \n",
      " 41  Mentality                 18852 non-null  int64         \n",
      " 42  Aggression                18852 non-null  int64         \n",
      " 43  Interceptions             18852 non-null  int64         \n",
      " 44  Positioning               18852 non-null  int64         \n",
      " 45  Vision                    18852 non-null  int64         \n",
      " 46  Penalties                 18852 non-null  int64         \n",
      " 47  Composure                 18852 non-null  int64         \n",
      " 48  Defending                 18852 non-null  int64         \n",
      " 49  Marking                   18852 non-null  int64         \n",
      " 50  Standing Tackle           18852 non-null  int64         \n",
      " 51  Sliding Tackle            18852 non-null  int64         \n",
      " 52  Goalkeeping               18852 non-null  int64         \n",
      " 53  GK Diving                 18852 non-null  int64         \n",
      " 54  GK Handling               18852 non-null  int64         \n",
      " 55  GK Kicking                18852 non-null  int64         \n",
      " 56  GK Positioning            18852 non-null  int64         \n",
      " 57  GK Reflexes               18852 non-null  int64         \n",
      " 58  Total Stats               18852 non-null  int64         \n",
      " 59  Base Stats                18852 non-null  int64         \n",
      " 60  Weak Foot                 18852 non-null  object        \n",
      " 61  Skill Moves               18852 non-null  object        \n",
      " 62  Attacking Work Rate       18852 non-null  object        \n",
      " 63  Defensive Work Rate       18852 non-null  object        \n",
      " 64  International Reputation  18852 non-null  object        \n",
      " 65  Pace                      18852 non-null  int64         \n",
      " 66  Shooting                  18852 non-null  int64         \n",
      " 67  Passing                   18852 non-null  int64         \n",
      " 68  Dribbling                 18852 non-null  int64         \n",
      " 69  Defense                   18852 non-null  int64         \n",
      " 70  Physicality               18852 non-null  int64         \n",
      " 71  Hits                      18852 non-null  int32         \n",
      " 72  Team                      18852 non-null  object        \n",
      " 73  Start Year                18852 non-null  object        \n",
      " 74  End Year                  18852 non-null  object        \n",
      " 75  Height                    18852 non-null  float64       \n",
      " 76  Date                      18852 non-null  datetime64[ns]\n",
      " 77  Year                      18852 non-null  int64         \n",
      " 78  Month                     18852 non-null  int64         \n",
      " 79  Day                       18852 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(5), int32(1), int64(58), object(15)\n",
      "memory usage: 11.6+ MB\n"
     ]
    }
   ],
   "source": [
    "Fifa_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "97c7730a",
   "metadata": {},
   "outputs": [],
   "source": [
    "Fifa_df.drop(columns = [\"Loan Date End\"], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "872c631d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LongName</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Positions</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Overall Rating</th>\n",
       "      <th>Potential</th>\n",
       "      <th>ID</th>\n",
       "      <th>Weight</th>\n",
       "      <th>foot</th>\n",
       "      <th>...</th>\n",
       "      <th>Physicality</th>\n",
       "      <th>Hits</th>\n",
       "      <th>Team</th>\n",
       "      <th>Start Year</th>\n",
       "      <th>End Year</th>\n",
       "      <th>Height</th>\n",
       "      <th>Date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lionel Messi</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>RW ST CF</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>33</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>158023</td>\n",
       "      <td>72.121128</td>\n",
       "      <td>Left</td>\n",
       "      <td>...</td>\n",
       "      <td>65</td>\n",
       "      <td>372</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>2004</td>\n",
       "      <td>2021</td>\n",
       "      <td>159.40</td>\n",
       "      <td>2004-07-01</td>\n",
       "      <td>2004</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C. Ronaldo dos Santos Aveiro</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>ST LW</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>35</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>20801</td>\n",
       "      <td>83.007336</td>\n",
       "      <td>Right</td>\n",
       "      <td>...</td>\n",
       "      <td>77</td>\n",
       "      <td>344</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>2018</td>\n",
       "      <td>2022</td>\n",
       "      <td>184.88</td>\n",
       "      <td>2018-07-10</td>\n",
       "      <td>2018</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jan Oblak</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>GK</td>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>27</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>200389</td>\n",
       "      <td>87.089664</td>\n",
       "      <td>Right</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>86</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>2014</td>\n",
       "      <td>2023</td>\n",
       "      <td>184.88</td>\n",
       "      <td>2014-07-16</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kevin De Bruyne</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>CAM CM</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>29</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>192985</td>\n",
       "      <td>69.853168</td>\n",
       "      <td>Right</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>163</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>2015</td>\n",
       "      <td>2023</td>\n",
       "      <td>163.40</td>\n",
       "      <td>2015-08-30</td>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Neymar da Silva Santos Jr.</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>LW CAM</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>28</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>190871</td>\n",
       "      <td>68.038800</td>\n",
       "      <td>Right</td>\n",
       "      <td>...</td>\n",
       "      <td>59</td>\n",
       "      <td>273</td>\n",
       "      <td>Germain</td>\n",
       "      <td>2017</td>\n",
       "      <td>2022</td>\n",
       "      <td>161.40</td>\n",
       "      <td>2017-08-03</td>\n",
       "      <td>2017</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       LongName Nationality Positions               Name  Age  \\\n",
       "0                  Lionel Messi   Argentina  RW ST CF           L. Messi   33   \n",
       "1  C. Ronaldo dos Santos Aveiro    Portugal     ST LW  Cristiano Ronaldo   35   \n",
       "2                     Jan Oblak    Slovenia        GK           J. Oblak   27   \n",
       "3               Kevin De Bruyne     Belgium    CAM CM       K. De Bruyne   29   \n",
       "4    Neymar da Silva Santos Jr.      Brazil    LW CAM          Neymar Jr   28   \n",
       "\n",
       "   Overall Rating  Potential      ID     Weight   foot  ...  Physicality Hits  \\\n",
       "0              93         93  158023  72.121128   Left  ...           65  372   \n",
       "1              92         92   20801  83.007336  Right  ...           77  344   \n",
       "2              91         93  200389  87.089664  Right  ...           90   86   \n",
       "3              91         91  192985  69.853168  Right  ...           78  163   \n",
       "4              91         91  190871  68.038800  Right  ...           59  273   \n",
       "\n",
       "              Team  Start Year  End Year  Height       Date  Year  Month  Day  \n",
       "0     FC Barcelona        2004      2021  159.40 2004-07-01  2004      7    1  \n",
       "1         Juventus        2018      2022  184.88 2018-07-10  2018      7   10  \n",
       "2  Atlético Madrid        2014      2023  184.88 2014-07-16  2014      7   16  \n",
       "3  Manchester City        2015      2023  163.40 2015-08-30  2015      8   30  \n",
       "4          Germain        2017      2022  161.40 2017-08-03  2017      8    3  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fifa_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5097e03e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>CM</th>\n",
       "      <th>ST</th>\n",
       "      <th>RB</th>\n",
       "      <th>GK</th>\n",
       "      <th>CB</th>\n",
       "      <th>RWB</th>\n",
       "      <th>LB</th>\n",
       "      <th>RM</th>\n",
       "      <th>LM</th>\n",
       "      <th>LW</th>\n",
       "      <th>CDM</th>\n",
       "      <th>CAM</th>\n",
       "      <th>LWB</th>\n",
       "      <th>CF</th>\n",
       "      <th>RW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18974</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18975</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18976</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18977</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18978</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18852 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CM  ST  RB  GK  CB  RWB  LB  RM  LM  LW  CDM  CAM  LWB  CF  RW\n",
       "0       0   1   0   0   0    0   0   0   0   0    0    0    0   1   1\n",
       "1       0   1   0   0   0    0   0   0   0   1    0    0    0   0   0\n",
       "2       0   0   0   1   0    0   0   0   0   0    0    0    0   0   0\n",
       "3       1   0   0   0   0    0   0   0   0   0    0    1    0   0   0\n",
       "4       0   0   0   0   0    0   0   0   0   1    0    1    0   0   0\n",
       "...    ..  ..  ..  ..  ..  ...  ..  ..  ..  ..  ...  ...  ...  ..  ..\n",
       "18974   0   0   0   0   1    0   0   0   0   0    0    0    0   0   0\n",
       "18975   0   1   0   0   0    0   0   0   0   0    0    0    0   0   0\n",
       "18976   0   0   0   0   1    0   0   0   0   0    0    0    0   0   0\n",
       "18977   1   0   0   0   0    0   0   0   0   0    0    0    0   0   0\n",
       "18978   0   0   0   0   0    0   0   0   0   0    1    0    0   0   0\n",
       "\n",
       "[18852 rows x 15 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "3e1d9556",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18852 entries, 0 to 18978\n",
      "Data columns (total 79 columns):\n",
      " #   Column                    Non-Null Count  Dtype         \n",
      "---  ------                    --------------  -----         \n",
      " 0   LongName                  18852 non-null  object        \n",
      " 1   Nationality               18852 non-null  object        \n",
      " 2   Positions                 18852 non-null  object        \n",
      " 3   Name                      18852 non-null  object        \n",
      " 4   Age                       18852 non-null  int64         \n",
      " 5   Overall Rating            18852 non-null  int64         \n",
      " 6   Potential                 18852 non-null  int64         \n",
      " 7   ID                        18852 non-null  int64         \n",
      " 8   Weight                    18852 non-null  float64       \n",
      " 9   foot                      18852 non-null  object        \n",
      " 10  Best Overall              18852 non-null  int64         \n",
      " 11  Best Position             18852 non-null  object        \n",
      " 12  Growth                    18852 non-null  int64         \n",
      " 13  Value                     18852 non-null  float64       \n",
      " 14  Wage                      18852 non-null  float64       \n",
      " 15  Release Clause            18852 non-null  float64       \n",
      " 16  Attacking                 18852 non-null  int64         \n",
      " 17  Crossing                  18852 non-null  int64         \n",
      " 18  Finishing                 18852 non-null  int64         \n",
      " 19  Heading Accuracy          18852 non-null  int64         \n",
      " 20  Short Passing             18852 non-null  int64         \n",
      " 21  Volleys                   18852 non-null  int64         \n",
      " 22  Skill                     18852 non-null  int64         \n",
      " 23  Dribbling                 18852 non-null  int64         \n",
      " 24  Curve                     18852 non-null  int64         \n",
      " 25  FK Accuracy               18852 non-null  int64         \n",
      " 26  Long Passing              18852 non-null  int64         \n",
      " 27  Ball Control              18852 non-null  int64         \n",
      " 28  Movement                  18852 non-null  int64         \n",
      " 29  Acceleration              18852 non-null  int64         \n",
      " 30  Sprint Speed              18852 non-null  int64         \n",
      " 31  Agility                   18852 non-null  int64         \n",
      " 32  Reactions                 18852 non-null  int64         \n",
      " 33  Balance                   18852 non-null  int64         \n",
      " 34  Power                     18852 non-null  int64         \n",
      " 35  Shot Power                18852 non-null  int64         \n",
      " 36  Jumping                   18852 non-null  int64         \n",
      " 37  Stamina                   18852 non-null  int64         \n",
      " 38  Strength                  18852 non-null  int64         \n",
      " 39  Long Shots                18852 non-null  int64         \n",
      " 40  Mentality                 18852 non-null  int64         \n",
      " 41  Aggression                18852 non-null  int64         \n",
      " 42  Interceptions             18852 non-null  int64         \n",
      " 43  Positioning               18852 non-null  int64         \n",
      " 44  Vision                    18852 non-null  int64         \n",
      " 45  Penalties                 18852 non-null  int64         \n",
      " 46  Composure                 18852 non-null  int64         \n",
      " 47  Defending                 18852 non-null  int64         \n",
      " 48  Marking                   18852 non-null  int64         \n",
      " 49  Standing Tackle           18852 non-null  int64         \n",
      " 50  Sliding Tackle            18852 non-null  int64         \n",
      " 51  Goalkeeping               18852 non-null  int64         \n",
      " 52  GK Diving                 18852 non-null  int64         \n",
      " 53  GK Handling               18852 non-null  int64         \n",
      " 54  GK Kicking                18852 non-null  int64         \n",
      " 55  GK Positioning            18852 non-null  int64         \n",
      " 56  GK Reflexes               18852 non-null  int64         \n",
      " 57  Total Stats               18852 non-null  int64         \n",
      " 58  Base Stats                18852 non-null  int64         \n",
      " 59  Weak Foot                 18852 non-null  object        \n",
      " 60  Skill Moves               18852 non-null  object        \n",
      " 61  Attacking Work Rate       18852 non-null  object        \n",
      " 62  Defensive Work Rate       18852 non-null  object        \n",
      " 63  International Reputation  18852 non-null  object        \n",
      " 64  Pace                      18852 non-null  int64         \n",
      " 65  Shooting                  18852 non-null  int64         \n",
      " 66  Passing                   18852 non-null  int64         \n",
      " 67  Dribbling                 18852 non-null  int64         \n",
      " 68  Defense                   18852 non-null  int64         \n",
      " 69  Physicality               18852 non-null  int64         \n",
      " 70  Hits                      18852 non-null  int32         \n",
      " 71  Team                      18852 non-null  object        \n",
      " 72  Start Year                18852 non-null  object        \n",
      " 73  End Year                  18852 non-null  object        \n",
      " 74  Height                    18852 non-null  float64       \n",
      " 75  Date                      18852 non-null  datetime64[ns]\n",
      " 76  Year                      18852 non-null  int64         \n",
      " 77  Month                     18852 non-null  int64         \n",
      " 78  Day                       18852 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(5), int32(1), int64(58), object(14)\n",
      "memory usage: 11.4+ MB\n"
     ]
    }
   ],
   "source": [
    "Fifa_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7967d6ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 18852 entries, 0 to 18978\n",
      "Data columns (total 15 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   CM      18852 non-null  int32\n",
      " 1   ST      18852 non-null  int32\n",
      " 2   RB      18852 non-null  int32\n",
      " 3   GK      18852 non-null  int32\n",
      " 4   CB      18852 non-null  int32\n",
      " 5   RWB     18852 non-null  int32\n",
      " 6   LB      18852 non-null  int32\n",
      " 7   RM      18852 non-null  int32\n",
      " 8   LM      18852 non-null  int32\n",
      " 9   LW      18852 non-null  int32\n",
      " 10  CDM     18852 non-null  int32\n",
      " 11  CAM     18852 non-null  int32\n",
      " 12  LWB     18852 non-null  int32\n",
      " 13  CF      18852 non-null  int32\n",
      " 14  RW      18852 non-null  int32\n",
      "dtypes: int32(15)\n",
      "memory usage: 1.2 MB\n"
     ]
    }
   ],
   "source": [
    "encoded.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f353610d",
   "metadata": {},
   "source": [
    "# Store into excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "11570cc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "excel_file = \"combined_data.xlsx\"\n",
    "# Create an ExcelWriter object\n",
    "with pd.ExcelWriter(excel_file, engine='xlsxwriter') as writer:\n",
    "    # Save each DataFrame to a separate sheet\n",
    "    Fifa_df.to_excel(writer, sheet_name=\"Sheet1\", index=False)\n",
    "    encoded.to_excel(writer, sheet_name=\"Sheet2\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67d2d9bc",
   "metadata": {},
   "source": [
    "In this project, we will walk through the process of cleaning and transforming a FIFA dataset. The objective is to prepare the data for analysis by removing inconsistencies and converting it into a more usable format. The following key tasks were undertaken:\n",
    "\n",
    "## Task 1: Removing URLs\n",
    "\n",
    "We started by removing URLs from the dataset, ensuring that hyperlinks or website references do not interfere with the analysis.\n",
    "\n",
    "## Task 2: Removing Duplicates\n",
    "\n",
    "Duplicate records were identified and removed to maintain data integrity and avoid redundancy.\n",
    "\n",
    "## Task 3: Converting Positions into One-Hot Encoding\n",
    "\n",
    "The \"Positions\" column was converted into one-hot encoding to facilitate further analysis by representing player positions as binary values.\n",
    "\n",
    "## Task 4: Separating \"Team & Contract\" into Multiple Columns\n",
    "\n",
    "We split the \"Team & Contract\" column into three distinct columns: \"Team,\" \"Contract Start Year,\" and \"Contract End Year\" for better data organization.\n",
    "\n",
    "## Task 5: Converting Data Types of Height and Weight\n",
    "\n",
    "The data types of the \"Height\" and \"Weight\" columns were converted, and heights were transformed from feet (ft) to centimeters (cm), while weights were converted from pounds (lbs) to kilograms (kg).\n",
    "\n",
    "## Task 6: Converting Date Strings into Datetime Format\n",
    "\n",
    "We converted date strings into the datetime data type, making it easier to work with time-related information. Additionally, we extracted and stored the month, year, and day in separate columns for more detailed analysis.\n",
    "\n",
    "## Task 7: Converting String into Numeric Money Values\n",
    "\n",
    "Monetary values within the dataset were converted from strings into numeric formats to enable financial calculations.\n",
    "\n",
    "## Task 8: Checking for Missing Values\n",
    "\n",
    "We performed a comprehensive check for missing values within the dataset and filled them as needed to ensure data completeness.\n",
    "\n",
    "## Saving the Cleaned Data\n",
    "\n",
    "The cleaned dataset was saved to an Excel workbook with two separate sheets. The first sheet contains the cleaned data, and the second sheet contains the one-hot encoded player positions.\n",
    "\n",
    "This data preparation is a crucial step that paves the way for subsequent data analysis and modeling, allowing us to draw meaningful insights and make informed decisions.\n",
    "\n",
    "Stay tuned for further analysis and exploration of the FIFA dataset!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d77848d",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "In this project, we embarked on a comprehensive data cleaning and transformation journey with the FIFA dataset. Our primary objective was to prepare the data for analysis by addressing inconsistencies, refining data types, and enhancing its usability. We can now conclude our data preparation journey with several key takeaways:\n",
    "\n",
    "## Data Quality Improvement\n",
    "\n",
    "- The removal of URLs and duplicate records ensured data integrity and consistency, creating a clean foundation for analysis.\n",
    "\n",
    "- Converting positions into one-hot encoding offered a structured representation of player positions, simplifying the analysis.\n",
    "\n",
    "- Separating the \"Team & Contract\" column into multiple columns improved data organization and usability.\n",
    "\n",
    "- The conversion of height and weight data, along with the conversion of financial values, allowed for standardized and accurate measurements.\n",
    "\n",
    "- Transforming date strings into datetime data types enabled in-depth time-related analysis.\n",
    "\n",
    "## Data Completeness\n",
    "\n",
    "- A comprehensive check for missing values was conducted, and necessary values were filled, ensuring that our dataset is complete and ready for further analysis.\n",
    "\n",
    "## Enhanced Usability\n",
    "\n",
    "- The data transformation steps have significantly improved the usability and interpretability of the dataset, making it more convenient for analysts and researchers.\n",
    "\n",
    "In conclusion, data cleaning and transformation are foundational steps in any data analysis project. By addressing inconsistencies, refining data types, and enhancing data quality, we have set the stage for more meaningful and insightful analyses. The clean and structured dataset is now well-equipped for advanced analytics, visualizations, and modeling.\n",
    "\n",
    "This project has not only improved the quality of our data but has also provided a strong foundation for any subsequent tasks, including exploratory data analysis, predictive modeling, and reporting. We look forward to leveraging this clean dataset to extract valuable insights and make informed decisions in the world of FIFA data analysis.\n"
   ]
  }
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