
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt


fifa21_rawdata = pd.read_csv('fifa21_raw_data_v2.csv')
fifa21_rawdata
# fifa21_rawdata.shape

C:\Users\imgal\AppData\Local\Temp\ipykernel_11392\2118387280.py:1: DtypeWarning: Columns (76) have mixed types. Specify dtype option on import or set low_memory=False.
  fifa21_rawdata = pd.read_csv('fifa21_raw_data_v2.csv')


# View the data
fifa21_rawdata.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18979 entries, 0 to 18978
Data columns (total 77 columns):
 #   Column            Non-Null Count  Dtype 
---  ------            --------------  ----- 
 0   ID                18979 non-null  int64 
 1   Name              18979 non-null  object
 2   LongName          18979 non-null  object
 3   photoUrl          18979 non-null  object
 4   playerUrl         18979 non-null  object
 5   Nationality       18979 non-null  object
 6   Age               18979 non-null  int64 
 7   ↓OVA              18979 non-null  int64 
 8   POT               18979 non-null  int64 
 9   Club              18979 non-null  object
 10  Contract          18979 non-null  object
 11  Positions         18979 non-null  object
 12  Height            18979 non-null  object
 13  Weight            18979 non-null  object
 14  Preferred Foot    18979 non-null  object
 15  BOV               18979 non-null  int64 
 16  Best Position     18979 non-null  object
 17  Joined            18979 non-null  object
 18  Loan Date End     1013 non-null   object
 19  Value             18979 non-null  object
 20  Wage              18979 non-null  object
 21  Release Clause    18979 non-null  object
 22  Attacking         18979 non-null  int64 
 23  Crossing          18979 non-null  int64 
 24  Finishing         18979 non-null  int64 
 25  Heading Accuracy  18979 non-null  int64 
 26  Short Passing     18979 non-null  int64 
 27  Volleys           18979 non-null  int64 
 28  Skill             18979 non-null  int64 
 29  Dribbling         18979 non-null  int64 
 30  Curve             18979 non-null  int64 
 31  FK Accuracy       18979 non-null  int64 
 32  Long Passing      18979 non-null  int64 
 33  Ball Control      18979 non-null  int64 
 34  Movement          18979 non-null  int64 
 35  Acceleration      18979 non-null  int64 
 36  Sprint Speed      18979 non-null  int64 
 37  Agility           18979 non-null  int64 
 38  Reactions         18979 non-null  int64 
 39  Balance           18979 non-null  int64 
 40  Power             18979 non-null  int64 
 41  Shot Power        18979 non-null  int64 
 42  Jumping           18979 non-null  int64 
 43  Stamina           18979 non-null  int64 
 44  Strength          18979 non-null  int64 
 45  Long Shots        18979 non-null  int64 
 46  Mentality         18979 non-null  int64 
 47  Aggression        18979 non-null  int64 
 48  Interceptions     18979 non-null  int64 
 49  Positioning       18979 non-null  int64 
 50  Vision            18979 non-null  int64 
 51  Penalties         18979 non-null  int64 
 52  Composure         18979 non-null  int64 
 53  Defending         18979 non-null  int64 
 54  Marking           18979 non-null  int64 
 55  Standing Tackle   18979 non-null  int64 
 56  Sliding Tackle    18979 non-null  int64 
 57  Goalkeeping       18979 non-null  int64 
 58  GK Diving         18979 non-null  int64 
 59  GK Handling       18979 non-null  int64 
 60  GK Kicking        18979 non-null  int64 
 61  GK Positioning    18979 non-null  int64 
 62  GK Reflexes       18979 non-null  int64 
 63  Total Stats       18979 non-null  int64 
 64  Base Stats        18979 non-null  int64 
 65  W/F               18979 non-null  object
 66  SM                18979 non-null  object
 67  A/W               18979 non-null  object
 68  D/W               18979 non-null  object
 69  IR                18979 non-null  object
 70  PAC               18979 non-null  int64 
 71  SHO               18979 non-null  int64 
 72  PAS               18979 non-null  int64 
 73  DRI               18979 non-null  int64 
 74  DEF               18979 non-null  int64 
 75  PHY               18979 non-null  int64 
 76  Hits              16384 non-null  object
dtypes: int64(54), object(23)
memory usage: 11.1+ MB


# f21_copy = fifa21_rawdata.copy()
fdc = fifa21_rawdata


fdc['LongName'].dtype

dtype('O')


# The name of the column was changed to better identify the values in it
fdc = fdc.rename(columns={'LongName':'Surname'})

def split_names(df_3):
    # Split the 'LongName' column into two columns using the space character
    df_3[['TempName', 'Surname']] = df_3['Surname'].str.split(n=1, expand=True)
    
    # For the 'Name' column, keeping only the first part after splitting
    df_3['Name'] = df_3['Name'].str.split().str[0]
    
    # The pattern is used to identify the names similar to L. or K.
    pattern = r'^[A-Z]\.'
    
    # Checking the pattern of the names to modify them if necessary
    condition = (df_3['Name'].str.contains(pattern)) | (df_3['Name'] == df_3['Surname'])
    
    # Use loc to update values based on the condition
    df_3.loc[condition, 'Name'] = df_3['TempName']
    
    # Drop the temporary column when is no more needed
    df_3 = df_3.drop(columns=['TempName'])
    
    return df_3

fdc = split_names(fdc)
fdc[['Name', 'Surname']].head(10)


fdc = fdc.drop(['photoUrl','playerUrl'], axis=1)


fdc['Nationality'].dtype

dtype('O')


fdc['Nationality'].unique()

array(['Argentina', 'Portugal', 'Slovenia', 'Belgium', 'Brazil', 'Poland',
       'Egypt', 'France', 'Germany', 'Netherlands', 'Senegal', 'Spain',
       'England', 'Scotland', 'Korea Republic', 'Costa Rica', 'Italy',
       'Gabon', 'Croatia', 'Uruguay', 'Switzerland', 'Serbia', 'Slovakia',
       'Morocco', 'Algeria', 'Denmark', 'Hungary', 'Bosnia Herzegovina',
       'Nigeria', 'Cameroon', 'Norway', 'Ghana', 'Mexico', 'Austria',
       'Albania', 'Colombia', 'Chile', 'Ivory Coast', 'Greece', 'Finland',
       'Wales', 'Sweden', 'Togo', 'Czech Republic', 'Russia', 'Venezuela',
       'Canada', 'United States', 'Guinea', 'Montenegro', 'Israel',
       'Republic of Ireland', 'Ukraine', 'Turkey', 'Ecuador', 'Jamaica',
       'DR Congo', 'Australia', 'China PR', 'Armenia', 'Northern Ireland',
       'North Macedonia', 'Kosovo', 'Mali', 'Peru',
       'Central African Republic', 'Iceland', 'Burkina Faso', 'Paraguay',
       'Japan', 'Romania', 'New Zealand', 'Iran', 'Angola', 'Tunisia',
       'Syria', 'Dominican Republic', 'Cape Verde', 'Equatorial Guinea',
       'Kenya', 'Georgia', 'Panama', 'Zambia', 'Tanzania', 'Zimbabwe',
       'Congo', 'South Africa', 'Moldova', 'Mozambique', 'Iraq',
       'Guinea Bissau', 'Honduras', 'Cuba', 'Cyprus', 'Lithuania',
       'Estonia', 'Madagascar', 'Benin', 'Curacao', 'Saudi Arabia',
       'Gambia', 'Uzbekistan', 'Chad', 'United Arab Emirates',
       'Saint Kitts and Nevis', 'Libya', 'Sierra Leone', 'Philippines',
       'Liberia', 'Bulgaria', 'Comoros', 'Namibia', 'Luxembourg',
       'Trinidad & Tobago', 'Bermuda', 'Thailand', 'Burundi',
       'New Caledonia', 'Puerto Rico', 'Bolivia', 'Kazakhstan',
       'Antigua & Barbuda', 'Latvia', 'Malawi', 'Montserrat',
       'São Tomé & Príncipe', 'El Salvador', 'Mauritania', 'Jordan',
       'Eritrea', 'Aruba', 'Uganda', 'Chinese Taipei', 'Azerbaijan',
       'Afghanistan', 'Faroe Islands', 'Haiti', 'Sudan', 'Grenada',
       'Lebanon', 'Guam', 'Palestine', 'Belarus', 'Guyana', 'Rwanda',
       'Liechtenstein', 'Saint Lucia', 'Papua New Guinea', 'India',
       'Ethiopia', 'Belize', 'Andorra', 'Guatemala', 'Malta', 'Niger',
       'Korea DPR', 'Barbados', 'Macau', 'South Sudan', 'Singapore',
       'Hong Kong', 'Nicaragua', 'Malaysia', 'Indonesia'], dtype=object)


fdc['Age'].dtype

dtype('int64')


fdc['Age'].unique()

array([33, 35, 27, 29, 28, 31, 21, 34, 32, 25, 26, 30, 20, 24, 22, 23, 19,
       38, 42, 36, 37, 18, 17, 39, 40, 41, 16, 43, 53], dtype=int64)


fdc['↓OVA'].dtype

dtype('int64')


fdc['↓OVA'].unique()

array([93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 78, 77,
       76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60,
       59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47], dtype=int64)


fdc['POT'].dtype

dtype('int64')


fdc['POT'].unique()

array([93, 92, 91, 90, 95, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 78,
       77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61,
       60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47],
      dtype=int64)


# Now let´s change the name of the columns
fdc = fdc.rename(columns={'↓OVA':'Overall','POT':'Potential'})
fdc


fdc['Club'].dtype

dtype('O')


fdc['Club'].unique()

array(['\n\n\n\nFC Barcelona', '\n\n\n\nJuventus',
       '\n\n\n\nAtlético Madrid', '\n\n\n\nManchester City',
       '\n\n\n\nParis Saint-Germain', '\n\n\n\nFC Bayern München',
       '\n\n\n\nLiverpool', '\n\n\n\nReal Madrid', '\n\n\n\nChelsea',
       '\n\n\n\nTottenham Hotspur', '\n\n\n\nInter', '\n\n\n\nNapoli',
       '\n\n\n\nBorussia Dortmund', '\n\n\n\nManchester United',
       '\n\n\n\nArsenal', '\n\n\n\nLazio', '\n\n\n\nLeicester City',
       '\n\n\n\nBorussia Mönchengladbach', '\n\n\n\nReal Sociedad',
       '\n\n\n\nAtalanta', '\n\n\n\nOlympique Lyonnais', '\n\n\n\nMilan',
       '\n\n\n\nVillarreal CF', '\n\n\n\nRB Leipzig', '\n\n\n\nCagliari',
       '\n\n\n\nAjax', '\n\n\n\nSL Benfica', '\n\n\n\nAS Monaco',
       '\n\n\n\nWolverhampton Wanderers', '\n\n\n\nEverton',
       '\n\n\n\nFiorentina', '\n\n\n\nFC Porto', '\n\n\n\nRC Celta',
       '\n\n\n\nTorino', '\n\n\n\nSevilla FC', '\n\n\n\nGrêmio',
       '\n\n\n\nReal Betis', '\n\n\n\nRoma', '\n\n\n\nNewcastle United',
       '\n\n\n\nEintracht Frankfurt', '\n\n\n\nValencia CF',
       '\n\n\n\nMedipol Başakşehir FK', '\n\n\n\nInter Miami',
       '\n\n\n\nBayer 04 Leverkusen', '\n\n\n\nLevante UD',
       '\n\n\n\nCrystal Palace', '\n\n\n\nAthletic Club de Bilbao',
       '\n\n\n\nShanghai SIPG FC', '\n\n\n\nVfL Wolfsburg',
       '\n\n\n\nGuangzhou Evergrande Taobao FC', '\n\n\n\nAl Shabab',
       '\n\n\n\nOlympique de Marseille', '\n\n\n\nLos Angeles FC',
       '\n\n\n\nBeijing Sinobo Guoan FC', '\n\n\n\nGetafe CF',
       '\n\n\n\nSV Werder Bremen', '\n\n\n\nTSG 1899 Hoffenheim',
       '\n\n\n\nLOSC Lille', '\n\n\n\nDeportivo Alavés',
       '\n\n\n\nBurnley', '\n\n\n\nLeeds United', '\n\n\n\nFulham',
       '\n\n\n\nPFC CSKA Moscow', '\n\n\n\nGenoa',
       '\n\n\n\nGalatasaray SK', '\n\n\n\nAtlanta United',
       '\n\n\n\nAtlético Mineiro', '\n\n\n\nFlamengo',
       '\n\n\n\nPalmeiras', 'No Club', '\n\n\n\nSão Paulo',
       '\n\n\n\nWest Ham United', '\n\n\n\nSD Eibar',
       '\n\n\n\nRiver Plate', '\n\n\n\nOGC Nice',
       '\n\n\n\nReal Valladolid CF', '\n\n\n\nSporting CP', '\n\n\n\nPSV',
       '\n\n\n\nFeyenoord', '\n\n\n\nClub Brugge KV',
       '\n\n\n\nShakhtar Donetsk', '\n\n\n\nVissel Kobe',
       '\n\n\n\nBoca Juniors', '\n\n\n\n1. FC Union Berlin',
       '\n\n\n\nDalian YiFang FC', '\n\n\n\nAS Saint-Étienne',
       '\n\n\n\nSassuolo', '\n\n\n\nTigres U.A.N.L.',
       '\n\n\n\nDinamo Zagreb', '\n\n\n\nDynamo Kyiv',
       '\n\n\n\nFluminense', '\n\n\n\nUdinese', '\n\n\n\nAston Villa',
       '\n\n\n\nStade Rennais FC', '\n\n\n\nBeşiktaş JK',
       '\n\n\n\nBrighton & Hove Albion', '\n\n\n\nHebei China Fortune FC',
       '\n\n\n\nSouthampton', '\n\n\n\nLokomotiv Moscow',
       '\n\n\n\nJiangsu Suning FC', '\n\n\n\nAl Hilal',
       '\n\n\n\nWest Bromwich Albion', '\n\n\n\nFenerbahçe SK',
       '\n\n\n\nFC Groningen', '\n\n\n\nGuangzhou R&F FC',
       '\n\n\n\nOrlando City SC', '\n\n\n\nSampdoria',
       '\n\n\n\nStade de Reims', '\n\n\n\nSheffield United',
       '\n\n\n\nSantos', '\n\n\n\nInternacional', '\n\n\n\nCoritiba',
       '\n\n\n\nCA Osasuna', '\n\n\n\nLA Galaxy', '\n\n\n\nAl Nassr',
       '\n\n\n\nSC Freiburg', '\n\n\n\nSC Braga',
       '\n\n\n\nOlympiacos CFP', '\n\n\n\nToronto FC',
       '\n\n\n\nGranada CF', '\n\n\n\nFC Schalke 04',
       '\n\n\n\nRC Strasbourg Alsace', '\n\n\n\nHertha BSC',
       '\n\n\n\nAZ Alkmaar', '\n\n\n\nSeattle Sounders FC',
       '\n\n\n\nMontpellier HSC', '\n\n\n\nShanghai Greenland Shenhua FC',
       '\n\n\n\nHamburger SV', '\n\n\n\nGirona FC', '\n\n\n\nRacing Club',
       '\n\n\n\nPortland Timbers', '\n\n\n\nFC Girondins de Bordeaux',
       '\n\n\n\nWatford', '\n\n\n\nEstudiantes de La Plata',
       '\n\n\n\nClub América', '\n\n\n\nVitória Guimarães',
       '\n\n\n\nSK Slavia Praha', '\n\n\n\nTrabzonspor',
       '\n\n\n\nRCD Espanyol', '\n\n\n\nFC Nantes', '\n\n\n\nAngers SCO',
       '\n\n\n\nCruz Azul', '\n\n\n\nBSC Young Boys', '\n\n\n\nClub León',
       '\n\n\n\nSpartak Moscow', '\n\n\n\nSivasspor', '\n\n\n\nElche CF',
       '\n\n\n\nFC Augsburg', '\n\n\n\nWuhan Zall', '\n\n\n\nParma',
       '\n\n\n\nRoyal Antwerp FC', '\n\n\n\nMonterrey',
       '\n\n\n\nSan Lorenzo de Almagro', '\n\n\n\nAC Monza',
       '\n\n\n\nClub Atlético Lanús', '\n\n\n\nSD Huesca',
       '\n\n\n\nDerby County', '\n\n\n\nKayserispor',
       '\n\n\n\nRSC Anderlecht', '\n\n\n\nCD Leganés',
       '\n\n\n\nVélez Sarsfield', '\n\n\n\nStade Brestois 29',
       '\n\n\n\nTianjin TEDA FC', '\n\n\n\nBirmingham City',
       '\n\n\n\nHellas Verona', '\n\n\n\n1. FC Köln', '\n\n\n\nAl Wehda',
       '\n\n\n\nBournemouth', '\n\n\n\nAlanyaspor', '\n\n\n\nBologna',
       '\n\n\n\nAmiens SC', '\n\n\n\nCeltic',
       '\n\n\n\nShijiazhuang Ever Bright F.C.', '\n\n\n\nClub Atlas',
       '\n\n\n\nAl Ahli', '\n\n\n\nNorwich City',
       '\n\n\n\nFatih Karagümrük S.K.', '\n\n\n\nClub Tijuana',
       '\n\n\n\nUniversidad Católica', '\n\n\n\nGoiás',
       '\n\n\n\nBenevento', '\n\n\n\nIndependiente',
       '\n\n\n\nQingdao Huanghai F.C.', '\n\n\n\nBrentford',
       '\n\n\n\nAEK Athens', '\n\n\n\nRacing Club de Lens',
       '\n\n\n\nOlimpia Asunción', '\n\n\n\nShenzhen FC',
       '\n\n\n\nÇaykur Rizespor', '\n\n\n\nKAA Gent',
       '\n\n\n\nNew England Revolution', '\n\n\n\nCádiz CF',
       '\n\n\n\n1. FSV Mainz 05', '\n\n\n\nVasco da Gama',
       '\n\n\n\nFC Midtjylland', '\n\n\n\nAl Ain FC',
       '\n\n\n\nSantos Laguna', '\n\n\n\nSPAL', '\n\n\n\nPerth Glory',
       '\n\n\n\nFC Utrecht', '\n\n\n\nMontreal Impact',
       '\n\n\n\nUD Almería', '\n\n\n\nFortaleza', '\n\n\n\nSpezia',
       '\n\n\n\nFC Basel 1893', '\n\n\n\nFC København',
       '\n\n\n\nGodoy Cruz', '\n\n\n\nDeportivo Toluca',
       '\n\n\n\nNacional de Montevideo', '\n\n\n\nAalborg BK',
       '\n\n\n\nRio Ave FC', '\n\n\n\nFC Metz',
       '\n\n\n\nChongqing Dangdai Lifan FC SWM Team',
       '\n\n\n\nRosenborg BK', '\n\n\n\nFC Red Bull Salzburg',
       '\n\n\n\nBotafogo', '\n\n\n\nDSC Arminia Bielefeld',
       '\n\n\n\nClub Athletico Paranaense', '\n\n\n\nRCD Mallorca',
       '\n\n\n\nPachuca', '\n\n\n\nSwansea City', '\n\n\n\nKRC Genk',
       '\n\n\n\nEn Avant de Guingamp', '\n\n\n\nLecce',
       '\n\n\n\nNîmes Olympique', '\n\n\n\nColo-Colo',
       '\n\n\n\nStade Malherbe Caen', '\n\n\n\nColumbus Crew SC',
       '\n\n\n\nJunior FC', '\n\n\n\nVfB Stuttgart',
       '\n\n\n\nMoreirense FC', '\n\n\n\nRangers FC', '\n\n\n\nMalmö FF',
       '\n\n\n\nShandong Luneng TaiShan FC', '\n\n\n\nSparta Praha',
       '\n\n\n\nHatayspor', '\n\n\n\nChicago Fire',
       '\n\n\n\nToulouse Football Club', '\n\n\n\nFC Seoul',
       '\n\n\n\nAntalyaspor', '\n\n\n\nDeportivo Cali',
       '\n\n\n\nKaizer Chiefs', '\n\n\n\nReal Zaragoza',
       '\n\n\n\nAtlético Clube Goianiense', '\n\n\n\nPuebla FC',
       '\n\n\n\nFortuna Düsseldorf', '\n\n\n\nWestern United FC',
       '\n\n\n\nAstra Giurgiu', '\n\n\n\nHenan Jianye FC',
       '\n\n\n\nBahia', '\n\n\n\nFamalicão', '\n\n\n\nAtiker Konyaspor',
       '\n\n\n\nPeñarol', '\n\n\n\nDC United', '\n\n\n\nNew York City FC',
       '\n\n\n\nArgentinos Juniors', '\n\n\n\nU.N.A.M.',
       '\n\n\n\nMinnesota United FC', '\n\n\n\nNottingham Forest',
       '\n\n\n\nIndependiente del Valle', '\n\n\n\nDefensa y Justicia',
       '\n\n\n\nPhiladelphia Union', "\n\n\n\nNewell's Old Boys",
       '\n\n\n\nRosario Central', '\n\n\n\nAtlético de San Luis',
       '\n\n\n\nFC Dallas', '\n\n\n\nDenizlispor',
       '\n\n\n\nYeni Malatyaspor', '\n\n\n\nClub Atlético Colón',
       '\n\n\n\nHouston Dynamo', '\n\n\n\nMillwall',
       '\n\n\n\nClube Sport Marítimo', '\n\n\n\nMKE Ankaragücü',
       '\n\n\n\nGuadalajara', '\n\n\n\nBoavista FC',
       '\n\n\n\nSporting Kansas City', '\n\n\n\nAtlético Nacional',
       '\n\n\n\nCD Lugo', '\n\n\n\nNew York Red Bulls', '\n\n\n\nPAOK',
       '\n\n\n\nSC Heerenveen', '\n\n\n\nDaegu FC',
       '\n\n\n\nPreston North End', '\n\n\n\nDamac FC', '\n\n\n\nBrescia',
       '\n\n\n\nVfL Bochum 1848', '\n\n\n\nVancouver Whitecaps FC',
       '\n\n\n\nSV Sandhausen', '\n\n\n\nRoyal Excel Mouscron',
       '\n\n\n\nDijon FCO', '\n\n\n\nMolde FK',
       '\n\n\n\nPanathinaikos FC', '\n\n\n\nLASK Linz',
       '\n\n\n\nUlsan Hyundai FC', '\n\n\n\nReal Salt Lake',
       '\n\n\n\nFC Luzern', '\n\n\n\nViktoria Plzeň',
       '\n\n\n\nOrlando Pirates', '\n\n\n\nAl Ittihad',
       '\n\n\n\n1. FC Nürnberg', '\n\n\n\nStandard de Liège',
       '\n\n\n\nRayo Vallecano', '\n\n\n\nClub Libertad',
       '\n\n\n\nJeonbuk Hyundai Motors',
       '\n\n\n\nWestern Sydney Wanderers', '\n\n\n\nUnión de Santa Fe',
       '\n\n\n\nFC Sion', '\n\n\n\nSydney FC', '\n\n\n\nHannover 96',
       '\n\n\n\nReal Sporting de Gijón', '\n\n\n\nFC Cincinnati',
       '\n\n\n\nESTAC Troyes', '\n\n\n\nSan Jose Earthquakes',
       '\n\n\n\nClub Guaraní', '\n\n\n\nFC Lorient',
       '\n\n\n\nGençlerbirliği SK', '\n\n\n\nSporting de Charleroi',
       '\n\n\n\nWisła Kraków', '\n\n\n\nAl Taawoun', '\n\n\n\nReading',
       '\n\n\n\nGazişehir Gaziantep F.K.', '\n\n\n\nBlackburn Rovers',
       '\n\n\n\nKashiwa Reysol', '\n\n\n\nFK Bodø/Glimt',
       '\n\n\n\nWillem II', '\n\n\n\nVitesse', '\n\n\n\nGil Vicente FC',
       '\n\n\n\nSønderjyskE', '\n\n\n\nYokohama F. Marinos',
       '\n\n\n\nFC St. Gallen', '\n\n\n\nClub Atlético Aldosivi',
       '\n\n\n\nGöztepe SK', '\n\n\n\nHolstein Kiel', '\n\n\n\nFC Juárez',
       '\n\n\n\nFCSB (Steaua)', '\n\n\n\nEttifaq FC', '\n\n\n\nKAS Eupen',
       '\n\n\n\nFC Tokyo', '\n\n\n\nMelbourne City FC',
       '\n\n\n\nUniversitatea Craiova', '\n\n\n\nCD Tondela',
       '\n\n\n\nWolfsberger AC', '\n\n\n\nCeará Sporting Club',
       '\n\n\n\nRiver Plate Asunción', '\n\n\n\nStoke City',
       '\n\n\n\nParis FC', '\n\n\n\nIFK Norrköping', '\n\n\n\nCrotone',
       '\n\n\n\nCardiff City', '\n\n\n\nAmérica de Cali',
       '\n\n\n\nAl Faisaly', '\n\n\n\nIndependiente Medellín',
       '\n\n\n\nPortimonense SC', '\n\n\n\nCD Tenerife',
       '\n\n\n\nHeracles Almelo', '\n\n\n\nNashville SC',
       '\n\n\n\nSK Rapid Wien', '\n\n\n\nKawasaki Frontale',
       '\n\n\n\nClub Necaxa', '\n\n\n\nChievo Verona',
       '\n\n\n\nLech Poznań', '\n\n\n\nAIK', '\n\n\n\nFK Austria Wien',
       '\n\n\n\nQuerétaro', '\n\n\n\n1. FC Heidenheim 1846',
       '\n\n\n\nLegia Warszawa', '\n\n\n\nClub Atlético Banfield',
       '\n\n\n\nOs Belenenses', '\n\n\n\nHuddersfield Town',
       '\n\n\n\nKSV Cercle Brugge', '\n\n\n\nLDU Quito',
       '\n\n\n\nGimnasia y Esgrima La Plata', '\n\n\n\nGamba Osaka',
       '\n\n\n\nMiddlesbrough', '\n\n\n\nServette FC', '\n\n\n\nEmpoli',
       '\n\n\n\nReal Oviedo', '\n\n\n\nKasimpaşa SK', '\n\n\n\nCFR Cluj',
       '\n\n\n\nMillonarios FC', '\n\n\n\nFC Paços de Ferreira',
       '\n\n\n\nSint-Truidense VV', '\n\n\n\nAD Alcorcón',
       '\n\n\n\nColorado Rapids', '\n\n\n\nDinamo Bucureşti',
       '\n\n\n\nCerezo Osaka', '\n\n\n\nFC Emmen',
       '\n\n\n\nSheffield Wednesday', '\n\n\n\nFC Erzgebirge Aue',
       '\n\n\n\nRaków Częstochowa', '\n\n\n\nAtlético Tucumán',
       '\n\n\n\nSV Darmstadt 98', '\n\n\n\nBristol City',
       '\n\n\n\nOud-Heverlee Leuven', '\n\n\n\nBarnsley',
       '\n\n\n\nQueens Park Rangers', '\n\n\n\nSangju Sangmu FC',
       '\n\n\n\nHelsingborgs IF', '\n\n\n\nAC Ajaccio',
       '\n\n\n\nOceânico FC', '\n\n\n\nClub Atlético Talleres',
       '\n\n\n\nAl Hazem', '\n\n\n\nSC Paderborn 07',
       '\n\n\n\nClub Atlético Huracán', '\n\n\n\nKV Kortrijk',
       '\n\n\n\nEl Nacional', '\n\n\n\nMazatlán FC', '\n\n\n\nAberdeen',
       '\n\n\n\nCD Mirandés', '\n\n\n\nSV Zulte-Waregem',
       '\n\n\n\nPiast Gliwice', '\n\n\n\nDjurgårdens IF',
       '\n\n\n\nCentral Córdoba', '\n\n\n\nWellington Phoenix',
       '\n\n\n\nDeportivo Binacional', '\n\n\n\nPatronato',
       '\n\n\n\nSpVgg Greuther Fürth', '\n\n\n\nIFK Göteborg',
       '\n\n\n\nFC Cartagena', '\n\n\n\nSanta Clara', '\n\n\n\nFC Twente',
       '\n\n\n\nFarense', '\n\n\n\nADO Den Haag', '\n\n\n\nAJ Auxerre',
       '\n\n\n\nAl Fateh', '\n\n\n\nValenciennes FC',
       '\n\n\n\nHokkaido Consadole Sapporo', '\n\n\n\nFC St. Pauli',
       '\n\n\n\nAlbacete BP', '\n\n\n\n1. FC Kaiserslautern',
       '\n\n\n\nAl Fayha', '\n\n\n\nIncheon United FC',
       '\n\n\n\nKV Mechelen', '\n\n\n\nPohang Steelers',
       '\n\n\n\nCD Nacional', '\n\n\n\nUnión La Calera',
       '\n\n\n\nBB Erzurumspor', '\n\n\n\nKashima Antlers',
       '\n\n\n\nArsenal de Sarandí', '\n\n\n\nHJK Helsinki',
       '\n\n\n\nKarlsruher SC', '\n\n\n\nAarhus GF', '\n\n\n\nFC Zürich',
       '\n\n\n\nBarcelona Sporting Club', '\n\n\n\nSG Dynamo Dresden',
       '\n\n\n\nSanfrecce Hiroshima', '\n\n\n\nAl Raed',
       '\n\n\n\nSD Ponferradina', '\n\n\n\nBrøndby IF',
       '\n\n\n\nLechia Gdańsk', '\n\n\n\nFC Viitorul',
       '\n\n\n\nSK Sturm Graz', '\n\n\n\nIF Elfsborg',
       '\n\n\n\nGangwon FC', '\n\n\n\nMelgar FBC',
       '\n\n\n\nClub Atlético Grau', '\n\n\n\nJagiellonia Białystok',
       '\n\n\n\nWaasland-Beveren', '\n\n\n\nVfL Osnabrück',
       '\n\n\n\nNagoya Grampus', '\n\n\n\nCF Fuenlabrada',
       '\n\n\n\nShamrock Rovers', '\n\n\n\nShimizu S-Pulse',
       '\n\n\n\nRKC Waalwijk', '\n\n\n\nVålerenga Fotball',
       '\n\n\n\nClermont Foot 63', '\n\n\n\nFC Ingolstadt 04',
       '\n\n\n\nFC Würzburger Kickers', '\n\n\n\nGórnik Zabrze',
       '\n\n\n\nCharlton Athletic', '\n\n\n\nBeerschot AC',
       '\n\n\n\nCoquimbo Unido', '\n\n\n\nViktoria Köln',
       '\n\n\n\nHull City', '\n\n\n\nMálaga CF',
       '\n\n\n\nFC Nordsjælland', '\n\n\n\nUD Las Palmas',
       '\n\n\n\nVVV-Venlo', '\n\n\n\nGwangJu FC',
       '\n\n\n\nChamois Niortais Football Club', '\n\n\n\nFC Lugano',
       '\n\n\n\nCusco FC', '\n\n\n\nSparta Rotterdam',
       '\n\n\n\nSol de América', '\n\n\n\nPEC Zwolle',
       '\n\n\n\nPogoń Szczecin', '\n\n\n\nDoncaster Rovers',
       '\n\n\n\nKristiansund BK', '\n\n\n\nLiverpool Fútbol Club',
       '\n\n\n\nClub Bolívar', '\n\n\n\nUrawa Red Diamonds',
       '\n\n\n\nSK Brann', '\n\n\n\nEintracht Braunschweig',
       '\n\n\n\nNacional Asunción', '\n\n\n\nMelbourne Victory',
       '\n\n\n\nKilmarnock', '\n\n\n\nDundalk',
       '\n\n\n\nSSV Jahn Regensburg', '\n\n\n\nAbha Club',
       '\n\n\n\nClub Atlético Tigre', '\n\n\n\nSuwon Samsung Bluewings',
       '\n\n\n\nShonan Bellmare', '\n\n\n\nSD Aucas', '\n\n\n\nEmelec',
       '\n\n\n\nFortuna Sittard', '\n\n\n\nLa Berrichonne de Châteauroux',
       '\n\n\n\nŚląsk Wrocław', '\n\n\n\nCoventry City',
       '\n\n\n\nSunderland', '\n\n\n\nAlianza Lima',
       '\n\n\n\nHammarby IF', '\n\n\n\nCracovia',
       '\n\n\n\nCE Sabadell FC', '\n\n\n\nOita Trinita',
       '\n\n\n\nBK Häcken', '\n\n\n\nLuton Town', '\n\n\n\nLe Havre AC',
       '\n\n\n\nIK Sirius', '\n\n\n\nZagłębie Lubin',
       '\n\n\n\nGrenoble Foot 38', '\n\n\n\nMacarthur FC',
       '\n\n\n\nRodez Aveyron Football', '\n\n\n\nVegalta Sendai',
       '\n\n\n\nMotherwell', '\n\n\n\nSV Wehen Wiesbaden',
       '\n\n\n\nAudax Italiano', '\n\n\n\nGaz Metan Mediaş',
       '\n\n\n\nPortsmouth', '\n\n\n\nHibernian', '\n\n\n\nAlways Ready',
       '\n\n\n\nUD Logroñés', '\n\n\n\nFC Voluntari',
       '\n\n\n\nFC Hermannstadt', '\n\n\n\nAS Nancy Lorraine',
       '\n\n\n\nRotherham United', '\n\n\n\nFleetwood Town',
       '\n\n\n\nJorge Wilstermann', '\n\n\n\nOdense Boldklub',
       '\n\n\n\nMSV Duisburg', '\n\n\n\nFC Sochaux-Montbéliard',
       '\n\n\n\nMilton Keynes Dons', '\n\n\n\nRiver Plate Montevideo',
       '\n\n\n\nDelfín SC', '\n\n\n\nUniversidad Católica del Ecuador',
       '\n\n\n\nDeportivo Pasto', '\n\n\n\nKV Oostende',
       '\n\n\n\nClub Plaza Colonia', '\n\n\n\nWigan Athletic',
       '\n\n\n\nFC Botoşani', '\n\n\n\nC.D. Castellón',
       '\n\n\n\nSCR Altach', '\n\n\n\nÖrebro SK', '\n\n\n\nTSV Hartberg',
       '\n\n\n\nStabæk Fotball', '\n\n\n\nWycombe Wanderers',
       '\n\n\n\nSt. Johnstone FC', '\n\n\n\nOxford United',
       '\n\n\n\nLincoln City', '\n\n\n\nRanders FC', '\n\n\n\nViking FK',
       '\n\n\n\nFC Chambly Oise', '\n\n\n\nDundee United',
       '\n\n\n\nOriente Petrolero', '\n\n\n\nOdds BK',
       '\n\n\n\nNacional Potosí', '\n\n\n\nSepsi OSK',
       '\n\n\n\nFC Hansa Rostock', '\n\n\n\nLyngby BK',
       '\n\n\n\nSpVgg Unterhaching', '\n\n\n\nBrisbane Roar',
       '\n\n\n\nSeongnam FC', '\n\n\n\nSV Ried', '\n\n\n\nCD Huachipato',
       '\n\n\n\nWSG Tirol', '\n\n\n\nPodbeskidzie Bielsko-Biała',
       '\n\n\n\nPeterborough United', '\n\n\n\nClub Blooming',
       '\n\n\n\nWisła Płock', '\n\n\n\nBusan IPark',
       '\n\n\n\nFSV Zwickau', '\n\n\n\nMjøndalen IF',
       '\n\n\n\nVejle Boldklub', '\n\n\n\nPau FC',
       '\n\n\n\nFC Lausanne-Sport', '\n\n\n\nEstudiantes de Mérida',
       '\n\n\n\nSagan Tosu', '\n\n\n\nSV Waldhof Mannheim',
       '\n\n\n\nSport Huancayo', '\n\n\n\nHallescher FC',
       '\n\n\n\nKalmar FF', '\n\n\n\nSt. Mirren',
       '\n\n\n\nSKN St. Pölten', '\n\n\n\nSarpsborg 08 FF',
       '\n\n\n\nShrewsbury', '\n\n\n\nTSV 1860 München',
       '\n\n\n\nSV Meppen', '\n\n\n\nIpswich Town', '\n\n\n\nIK Start',
       '\n\n\n\nAdelaide United', '\n\n\n\nStal Mielec',
       '\n\n\n\nCentro Atlético Fénix', '\n\n\n\nNewcastle Jets',
       '\n\n\n\nLivingston FC', '\n\n\n\nAC Mineros de Guayana',
       '\n\n\n\nAFC Wimbledon', '\n\n\n\nBurton Albion',
       '\n\n\n\nAl Adalah', '\n\n\n\nWarta Poznań',
       '\n\n\n\nUSL Dunkerque', '\n\n\n\nBristol Rovers',
       '\n\n\n\nUTA Arad', '\n\n\n\nÖstersunds FK',
       '\n\n\n\nSportivo Luqueño', '\n\n\n\nPolitehnica Iaşi',
       '\n\n\n\nSalford City', '\n\n\n\nCentral Coast Mariners',
       '\n\n\n\nCaracas FC', '\n\n\n\nAC Horsens',
       '\n\n\n\nPlymouth Argyle', '\n\n\n\nAalesunds FK',
       '\n\n\n\nCrewe Alexandra', '\n\n\n\nChindia Târgovişte',
       '\n\n\n\nOldham Athletic', '\n\n\n\nYokohama FC',
       '\n\n\n\nBlackpool', '\n\n\n\nNorthampton Town',
       '\n\n\n\nBayern München II', '\n\n\n\nFK Haugesund',
       '\n\n\n\nKFC Uerdingen 05', '\n\n\n\nForest Green Rovers',
       '\n\n\n\nCheltenham Town', '\n\n\n\nStrømsgodset IF',
       '\n\n\n\nMjällby AIF', '\n\n\n\nExeter City',
       '\n\n\n\nCambridge United', '\n\n\n\nGrimsby Town',
       '\n\n\n\nFC Vaduz', '\n\n\n\nTürkgücü München',
       '\n\n\n\nVfB Lübeck', '\n\n\n\nBolton Wanderers',
       '\n\n\n\nBradford City', '\n\n\n\n1. FC Magdeburg',
       '\n\n\n\nFC Admira Wacker Mödling',
       '\n\n\n\nHamilton Academical FC', '\n\n\n\nAragua FC',
       '\n\n\n\nAccrington Stanley', '\n\n\n\nSwindon Town',
       '\n\n\n\nCarlisle United', '\n\n\n\nFC Argeș', '\n\n\n\nPort Vale',
       '\n\n\n\nAcademica Clinceni', '\n\n\n\nRochdale',
       '\n\n\n\nGillingham', '\n\n\n\nTranmere Rovers',
       '\n\n\n\nSouthend United', '\n\n\n\n1. FC Saarbrücken',
       '\n\n\n\nColchester United', '\n\n\n\nMansfield Town',
       '\n\n\n\nFalkenbergs FF', '\n\n\n\nSC Verl',
       '\n\n\n\nVarbergs BoIS', '\n\n\n\nCrawley Town',
       '\n\n\n\nSandefjord Fotball', '\n\n\n\nNewport County',
       '\n\n\n\nBohemian FC', '\n\n\n\nRoss County FC',
       '\n\n\n\nScunthorpe United', '\n\n\n\nMorecambe',
       '\n\n\n\nWalsall', '\n\n\n\nLeyton Orient', '\n\n\n\nStevenage',
       '\n\n\n\nBarrow', '\n\n\n\nDerry City',
       '\n\n\n\nLlaneros de Guanare', '\n\n\n\nSligo Rovers',
       '\n\n\n\nZamora FC', "\n\n\n\nSt. Patrick's Athletic",
       '\n\n\n\nCork City', '\n\n\n\nShelbourne FC',
       '\n\n\n\nHarrogate Town', '\n\n\n\nWaterford FC',
       '\n\n\n\nFinn Harps'], dtype=object)


fdc['Club'] = fdc['Club'].str.strip()
fdc['Club'].unique()

array(['FC Barcelona', 'Juventus', 'Atlético Madrid', 'Manchester City',
       'Paris Saint-Germain', 'FC Bayern München', 'Liverpool',
       'Real Madrid', 'Chelsea', 'Tottenham Hotspur', 'Inter', 'Napoli',
       'Borussia Dortmund', 'Manchester United', 'Arsenal', 'Lazio',
       'Leicester City', 'Borussia Mönchengladbach', 'Real Sociedad',
       'Atalanta', 'Olympique Lyonnais', 'Milan', 'Villarreal CF',
       'RB Leipzig', 'Cagliari', 'Ajax', 'SL Benfica', 'AS Monaco',
       'Wolverhampton Wanderers', 'Everton', 'Fiorentina', 'FC Porto',
       'RC Celta', 'Torino', 'Sevilla FC', 'Grêmio', 'Real Betis', 'Roma',
       'Newcastle United', 'Eintracht Frankfurt', 'Valencia CF',
       'Medipol Başakşehir FK', 'Inter Miami', 'Bayer 04 Leverkusen',
       'Levante UD', 'Crystal Palace', 'Athletic Club de Bilbao',
       'Shanghai SIPG FC', 'VfL Wolfsburg',
       'Guangzhou Evergrande Taobao FC', 'Al Shabab',
       'Olympique de Marseille', 'Los Angeles FC',
       'Beijing Sinobo Guoan FC', 'Getafe CF', 'SV Werder Bremen',
       'TSG 1899 Hoffenheim', 'LOSC Lille', 'Deportivo Alavés', 'Burnley',
       'Leeds United', 'Fulham', 'PFC CSKA Moscow', 'Genoa',
       'Galatasaray SK', 'Atlanta United', 'Atlético Mineiro', 'Flamengo',
       'Palmeiras', 'No Club', 'São Paulo', 'West Ham United', 'SD Eibar',
       'River Plate', 'OGC Nice', 'Real Valladolid CF', 'Sporting CP',
       'PSV', 'Feyenoord', 'Club Brugge KV', 'Shakhtar Donetsk',
       'Vissel Kobe', 'Boca Juniors', '1. FC Union Berlin',
       'Dalian YiFang FC', 'AS Saint-Étienne', 'Sassuolo',
       'Tigres U.A.N.L.', 'Dinamo Zagreb', 'Dynamo Kyiv', 'Fluminense',
       'Udinese', 'Aston Villa', 'Stade Rennais FC', 'Beşiktaş JK',
       'Brighton & Hove Albion', 'Hebei China Fortune FC', 'Southampton',
       'Lokomotiv Moscow', 'Jiangsu Suning FC', 'Al Hilal',
       'West Bromwich Albion', 'Fenerbahçe SK', 'FC Groningen',
       'Guangzhou R&F FC', 'Orlando City SC', 'Sampdoria',
       'Stade de Reims', 'Sheffield United', 'Santos', 'Internacional',
       'Coritiba', 'CA Osasuna', 'LA Galaxy', 'Al Nassr', 'SC Freiburg',
       'SC Braga', 'Olympiacos CFP', 'Toronto FC', 'Granada CF',
       'FC Schalke 04', 'RC Strasbourg Alsace', 'Hertha BSC',
       'AZ Alkmaar', 'Seattle Sounders FC', 'Montpellier HSC',
       'Shanghai Greenland Shenhua FC', 'Hamburger SV', 'Girona FC',
       'Racing Club', 'Portland Timbers', 'FC Girondins de Bordeaux',
       'Watford', 'Estudiantes de La Plata', 'Club América',
       'Vitória Guimarães', 'SK Slavia Praha', 'Trabzonspor',
       'RCD Espanyol', 'FC Nantes', 'Angers SCO', 'Cruz Azul',
       'BSC Young Boys', 'Club León', 'Spartak Moscow', 'Sivasspor',
       'Elche CF', 'FC Augsburg', 'Wuhan Zall', 'Parma',
       'Royal Antwerp FC', 'Monterrey', 'San Lorenzo de Almagro',
       'AC Monza', 'Club Atlético Lanús', 'SD Huesca', 'Derby County',
       'Kayserispor', 'RSC Anderlecht', 'CD Leganés', 'Vélez Sarsfield',
       'Stade Brestois 29', 'Tianjin TEDA FC', 'Birmingham City',
       'Hellas Verona', '1. FC Köln', 'Al Wehda', 'Bournemouth',
       'Alanyaspor', 'Bologna', 'Amiens SC', 'Celtic',
       'Shijiazhuang Ever Bright F.C.', 'Club Atlas', 'Al Ahli',
       'Norwich City', 'Fatih Karagümrük S.K.', 'Club Tijuana',
       'Universidad Católica', 'Goiás', 'Benevento', 'Independiente',
       'Qingdao Huanghai F.C.', 'Brentford', 'AEK Athens',
       'Racing Club de Lens', 'Olimpia Asunción', 'Shenzhen FC',
       'Çaykur Rizespor', 'KAA Gent', 'New England Revolution',
       'Cádiz CF', '1. FSV Mainz 05', 'Vasco da Gama', 'FC Midtjylland',
       'Al Ain FC', 'Santos Laguna', 'SPAL', 'Perth Glory', 'FC Utrecht',
       'Montreal Impact', 'UD Almería', 'Fortaleza', 'Spezia',
       'FC Basel 1893', 'FC København', 'Godoy Cruz', 'Deportivo Toluca',
       'Nacional de Montevideo', 'Aalborg BK', 'Rio Ave FC', 'FC Metz',
       'Chongqing Dangdai Lifan FC SWM Team', 'Rosenborg BK',
       'FC Red Bull Salzburg', 'Botafogo', 'DSC Arminia Bielefeld',
       'Club Athletico Paranaense', 'RCD Mallorca', 'Pachuca',
       'Swansea City', 'KRC Genk', 'En Avant de Guingamp', 'Lecce',
       'Nîmes Olympique', 'Colo-Colo', 'Stade Malherbe Caen',
       'Columbus Crew SC', 'Junior FC', 'VfB Stuttgart', 'Moreirense FC',
       'Rangers FC', 'Malmö FF', 'Shandong Luneng TaiShan FC',
       'Sparta Praha', 'Hatayspor', 'Chicago Fire',
       'Toulouse Football Club', 'FC Seoul', 'Antalyaspor',
       'Deportivo Cali', 'Kaizer Chiefs', 'Real Zaragoza',
       'Atlético Clube Goianiense', 'Puebla FC', 'Fortuna Düsseldorf',
       'Western United FC', 'Astra Giurgiu', 'Henan Jianye FC', 'Bahia',
       'Famalicão', 'Atiker Konyaspor', 'Peñarol', 'DC United',
       'New York City FC', 'Argentinos Juniors', 'U.N.A.M.',
       'Minnesota United FC', 'Nottingham Forest',
       'Independiente del Valle', 'Defensa y Justicia',
       'Philadelphia Union', "Newell's Old Boys", 'Rosario Central',
       'Atlético de San Luis', 'FC Dallas', 'Denizlispor',
       'Yeni Malatyaspor', 'Club Atlético Colón', 'Houston Dynamo',
       'Millwall', 'Clube Sport Marítimo', 'MKE Ankaragücü',
       'Guadalajara', 'Boavista FC', 'Sporting Kansas City',
       'Atlético Nacional', 'CD Lugo', 'New York Red Bulls', 'PAOK',
       'SC Heerenveen', 'Daegu FC', 'Preston North End', 'Damac FC',
       'Brescia', 'VfL Bochum 1848', 'Vancouver Whitecaps FC',
       'SV Sandhausen', 'Royal Excel Mouscron', 'Dijon FCO', 'Molde FK',
       'Panathinaikos FC', 'LASK Linz', 'Ulsan Hyundai FC',
       'Real Salt Lake', 'FC Luzern', 'Viktoria Plzeň', 'Orlando Pirates',
       'Al Ittihad', '1. FC Nürnberg', 'Standard de Liège',
       'Rayo Vallecano', 'Club Libertad', 'Jeonbuk Hyundai Motors',
       'Western Sydney Wanderers', 'Unión de Santa Fe', 'FC Sion',
       'Sydney FC', 'Hannover 96', 'Real Sporting de Gijón',
       'FC Cincinnati', 'ESTAC Troyes', 'San Jose Earthquakes',
       'Club Guaraní', 'FC Lorient', 'Gençlerbirliği SK',
       'Sporting de Charleroi', 'Wisła Kraków', 'Al Taawoun', 'Reading',
       'Gazişehir Gaziantep F.K.', 'Blackburn Rovers', 'Kashiwa Reysol',
       'FK Bodø/Glimt', 'Willem II', 'Vitesse', 'Gil Vicente FC',
       'SønderjyskE', 'Yokohama F. Marinos', 'FC St. Gallen',
       'Club Atlético Aldosivi', 'Göztepe SK', 'Holstein Kiel',
       'FC Juárez', 'FCSB (Steaua)', 'Ettifaq FC', 'KAS Eupen',
       'FC Tokyo', 'Melbourne City FC', 'Universitatea Craiova',
       'CD Tondela', 'Wolfsberger AC', 'Ceará Sporting Club',
       'River Plate Asunción', 'Stoke City', 'Paris FC', 'IFK Norrköping',
       'Crotone', 'Cardiff City', 'América de Cali', 'Al Faisaly',
       'Independiente Medellín', 'Portimonense SC', 'CD Tenerife',
       'Heracles Almelo', 'Nashville SC', 'SK Rapid Wien',
       'Kawasaki Frontale', 'Club Necaxa', 'Chievo Verona', 'Lech Poznań',
       'AIK', 'FK Austria Wien', 'Querétaro', '1. FC Heidenheim 1846',
       'Legia Warszawa', 'Club Atlético Banfield', 'Os Belenenses',
       'Huddersfield Town', 'KSV Cercle Brugge', 'LDU Quito',
       'Gimnasia y Esgrima La Plata', 'Gamba Osaka', 'Middlesbrough',
       'Servette FC', 'Empoli', 'Real Oviedo', 'Kasimpaşa SK', 'CFR Cluj',
       'Millonarios FC', 'FC Paços de Ferreira', 'Sint-Truidense VV',
       'AD Alcorcón', 'Colorado Rapids', 'Dinamo Bucureşti',
       'Cerezo Osaka', 'FC Emmen', 'Sheffield Wednesday',
       'FC Erzgebirge Aue', 'Raków Częstochowa', 'Atlético Tucumán',
       'SV Darmstadt 98', 'Bristol City', 'Oud-Heverlee Leuven',
       'Barnsley', 'Queens Park Rangers', 'Sangju Sangmu FC',
       'Helsingborgs IF', 'AC Ajaccio', 'Oceânico FC',
       'Club Atlético Talleres', 'Al Hazem', 'SC Paderborn 07',
       'Club Atlético Huracán', 'KV Kortrijk', 'El Nacional',
       'Mazatlán FC', 'Aberdeen', 'CD Mirandés', 'SV Zulte-Waregem',
       'Piast Gliwice', 'Djurgårdens IF', 'Central Córdoba',
       'Wellington Phoenix', 'Deportivo Binacional', 'Patronato',
       'SpVgg Greuther Fürth', 'IFK Göteborg', 'FC Cartagena',
       'Santa Clara', 'FC Twente', 'Farense', 'ADO Den Haag',
       'AJ Auxerre', 'Al Fateh', 'Valenciennes FC',
       'Hokkaido Consadole Sapporo', 'FC St. Pauli', 'Albacete BP',
       '1. FC Kaiserslautern', 'Al Fayha', 'Incheon United FC',
       'KV Mechelen', 'Pohang Steelers', 'CD Nacional', 'Unión La Calera',
       'BB Erzurumspor', 'Kashima Antlers', 'Arsenal de Sarandí',
       'HJK Helsinki', 'Karlsruher SC', 'Aarhus GF', 'FC Zürich',
       'Barcelona Sporting Club', 'SG Dynamo Dresden',
       'Sanfrecce Hiroshima', 'Al Raed', 'SD Ponferradina', 'Brøndby IF',
       'Lechia Gdańsk', 'FC Viitorul', 'SK Sturm Graz', 'IF Elfsborg',
       'Gangwon FC', 'Melgar FBC', 'Club Atlético Grau',
       'Jagiellonia Białystok', 'Waasland-Beveren', 'VfL Osnabrück',
       'Nagoya Grampus', 'CF Fuenlabrada', 'Shamrock Rovers',
       'Shimizu S-Pulse', 'RKC Waalwijk', 'Vålerenga Fotball',
       'Clermont Foot 63', 'FC Ingolstadt 04', 'FC Würzburger Kickers',
       'Górnik Zabrze', 'Charlton Athletic', 'Beerschot AC',
       'Coquimbo Unido', 'Viktoria Köln', 'Hull City', 'Málaga CF',
       'FC Nordsjælland', 'UD Las Palmas', 'VVV-Venlo', 'GwangJu FC',
       'Chamois Niortais Football Club', 'FC Lugano', 'Cusco FC',
       'Sparta Rotterdam', 'Sol de América', 'PEC Zwolle',
       'Pogoń Szczecin', 'Doncaster Rovers', 'Kristiansund BK',
       'Liverpool Fútbol Club', 'Club Bolívar', 'Urawa Red Diamonds',
       'SK Brann', 'Eintracht Braunschweig', 'Nacional Asunción',
       'Melbourne Victory', 'Kilmarnock', 'Dundalk',
       'SSV Jahn Regensburg', 'Abha Club', 'Club Atlético Tigre',
       'Suwon Samsung Bluewings', 'Shonan Bellmare', 'SD Aucas', 'Emelec',
       'Fortuna Sittard', 'La Berrichonne de Châteauroux',
       'Śląsk Wrocław', 'Coventry City', 'Sunderland', 'Alianza Lima',
       'Hammarby IF', 'Cracovia', 'CE Sabadell FC', 'Oita Trinita',
       'BK Häcken', 'Luton Town', 'Le Havre AC', 'IK Sirius',
       'Zagłębie Lubin', 'Grenoble Foot 38', 'Macarthur FC',
       'Rodez Aveyron Football', 'Vegalta Sendai', 'Motherwell',
       'SV Wehen Wiesbaden', 'Audax Italiano', 'Gaz Metan Mediaş',
       'Portsmouth', 'Hibernian', 'Always Ready', 'UD Logroñés',
       'FC Voluntari', 'FC Hermannstadt', 'AS Nancy Lorraine',
       'Rotherham United', 'Fleetwood Town', 'Jorge Wilstermann',
       'Odense Boldklub', 'MSV Duisburg', 'FC Sochaux-Montbéliard',
       'Milton Keynes Dons', 'River Plate Montevideo', 'Delfín SC',
       'Universidad Católica del Ecuador', 'Deportivo Pasto',
       'KV Oostende', 'Club Plaza Colonia', 'Wigan Athletic',
       'FC Botoşani', 'C.D. Castellón', 'SCR Altach', 'Örebro SK',
       'TSV Hartberg', 'Stabæk Fotball', 'Wycombe Wanderers',
       'St. Johnstone FC', 'Oxford United', 'Lincoln City', 'Randers FC',
       'Viking FK', 'FC Chambly Oise', 'Dundee United',
       'Oriente Petrolero', 'Odds BK', 'Nacional Potosí', 'Sepsi OSK',
       'FC Hansa Rostock', 'Lyngby BK', 'SpVgg Unterhaching',
       'Brisbane Roar', 'Seongnam FC', 'SV Ried', 'CD Huachipato',
       'WSG Tirol', 'Podbeskidzie Bielsko-Biała', 'Peterborough United',
       'Club Blooming', 'Wisła Płock', 'Busan IPark', 'FSV Zwickau',
       'Mjøndalen IF', 'Vejle Boldklub', 'Pau FC', 'FC Lausanne-Sport',
       'Estudiantes de Mérida', 'Sagan Tosu', 'SV Waldhof Mannheim',
       'Sport Huancayo', 'Hallescher FC', 'Kalmar FF', 'St. Mirren',
       'SKN St. Pölten', 'Sarpsborg 08 FF', 'Shrewsbury',
       'TSV 1860 München', 'SV Meppen', 'Ipswich Town', 'IK Start',
       'Adelaide United', 'Stal Mielec', 'Centro Atlético Fénix',
       'Newcastle Jets', 'Livingston FC', 'AC Mineros de Guayana',
       'AFC Wimbledon', 'Burton Albion', 'Al Adalah', 'Warta Poznań',
       'USL Dunkerque', 'Bristol Rovers', 'UTA Arad', 'Östersunds FK',
       'Sportivo Luqueño', 'Politehnica Iaşi', 'Salford City',
       'Central Coast Mariners', 'Caracas FC', 'AC Horsens',
       'Plymouth Argyle', 'Aalesunds FK', 'Crewe Alexandra',
       'Chindia Târgovişte', 'Oldham Athletic', 'Yokohama FC',
       'Blackpool', 'Northampton Town', 'Bayern München II',
       'FK Haugesund', 'KFC Uerdingen 05', 'Forest Green Rovers',
       'Cheltenham Town', 'Strømsgodset IF', 'Mjällby AIF', 'Exeter City',
       'Cambridge United', 'Grimsby Town', 'FC Vaduz', 'Türkgücü München',
       'VfB Lübeck', 'Bolton Wanderers', 'Bradford City',
       '1. FC Magdeburg', 'FC Admira Wacker Mödling',
       'Hamilton Academical FC', 'Aragua FC', 'Accrington Stanley',
       'Swindon Town', 'Carlisle United', 'FC Argeș', 'Port Vale',
       'Academica Clinceni', 'Rochdale', 'Gillingham', 'Tranmere Rovers',
       'Southend United', '1. FC Saarbrücken', 'Colchester United',
       'Mansfield Town', 'Falkenbergs FF', 'SC Verl', 'Varbergs BoIS',
       'Crawley Town', 'Sandefjord Fotball', 'Newport County',
       'Bohemian FC', 'Ross County FC', 'Scunthorpe United', 'Morecambe',
       'Walsall', 'Leyton Orient', 'Stevenage', 'Barrow', 'Derry City',
       'Llaneros de Guanare', 'Sligo Rovers', 'Zamora FC',
       "St. Patrick's Athletic", 'Cork City', 'Shelbourne FC',
       'Harrogate Town', 'Waterford FC', 'Finn Harps'], dtype=object)


fdc['Contract'].dtype

dtype('O')


fdc['Contract'].unique()

array(['2004 ~ 2021', '2018 ~ 2022', '2014 ~ 2023', '2015 ~ 2023',
       '2017 ~ 2022', '2017 ~ 2023', '2018 ~ 2024', '2014 ~ 2022',
       '2018 ~ 2023', '2016 ~ 2023', '2013 ~ 2023', '2011 ~ 2023',
       '2009 ~ 2022', '2005 ~ 2021', '2011 ~ 2021', '2015 ~ 2022',
       '2017 ~ 2024', '2010 ~ 2024', '2012 ~ 2021', '2019 ~ 2024',
       '2015 ~ 2024', '2017 ~ 2025', '2020 ~ 2025', '2019 ~ 2023',
       '2008 ~ 2023', '2015 ~ 2021', '2020 ~ 2022', '2012 ~ 2022',
       '2016 ~ 2025', '2013 ~ 2022', '2011 ~ 2022', '2012 ~ 2024',
       '2016 ~ 2021', '2012 ~ 2023', '2008 ~ 2022', '2019 ~ 2022',
       '2017 ~ 2021', '2013 ~ 2024', '2020 ~ 2024', '2010 ~ 2022',
       '2020 ~ 2021', '2011 ~ 2024', '2020 ~ 2023', '2014 ~ 2024',
       '2013 ~ 2026', '2016 ~ 2022', '2010 ~ 2021', '2013 ~ 2021',
       '2019 ~ 2025', '2018 ~ 2025', '2016 ~ 2024', '2018 ~ 2021',
       '2009 ~ 2024', '2007 ~ 2022', 'Jun 30, 2021 On Loan',
       '2009 ~ 2021', '2019 ~ 2021', '2019 ~ 2026', 'Free', '2012 ~ 2028',
       '2010 ~ 2023', '2014 ~ 2021', '2015 ~ 2025', '2014 ~ 2026',
       '2012 ~ 2025', '2017 ~ 2020', '2002 ~ 2022', '2020 ~ 2027',
       '2013 ~ 2025', 'Dec 31, 2020 On Loan', '2019 ~ 2020',
       '2011 ~ 2025', '2016 ~ 2020', '2007 ~ 2021', '2020 ~ 2026',
       '2010 ~ 2025', '2009 ~ 2023', '2008 ~ 2021', '2020 ~ 2020',
       '2016 ~ 2026', 'Jan 30, 2021 On Loan', '2012 ~ 2020',
       '2014 ~ 2025', 'Jun 30, 2022 On Loan', '2015 ~ 2020',
       'May 31, 2021 On Loan', '2018 ~ 2020', '2014 ~ 2020',
       '2013 ~ 2020', '2006 ~ 2024', 'Jul 5, 2021 On Loan',
       'Dec 31, 2021 On Loan', '2004 ~ 2025', '2011 ~ 2020',
       'Jul 1, 2021 On Loan', 'Jan 1, 2021 On Loan', '2006 ~ 2023',
       'Aug 31, 2021 On Loan', '2006 ~ 2021', '2005 ~ 2023',
       '2003 ~ 2020', '2009 ~ 2020', '2002 ~ 2020', '2005 ~ 2020',
       '2005 ~ 2022', 'Jan 31, 2021 On Loan', '2010 ~ 2020',
       'Dec 30, 2021 On Loan', '2008 ~ 2020', '2007 ~ 2020',
       '2003 ~ 2021', 'Jun 23, 2021 On Loan', 'Jan 3, 2021 On Loan',
       'Nov 27, 2021 On Loan', '2002 ~ 2021', 'Jan 17, 2021 On Loan',
       'Jun 30, 2023 On Loan', '1998 ~ 2021', '2003 ~ 2022',
       '2007 ~ 2023', 'Jul 31, 2021 On Loan', 'Nov 22, 2020 On Loan',
       'May 31, 2022 On Loan', '2006 ~ 2020', 'Dec 30, 2020 On Loan',
       '2007 ~ 2025', 'Jan 4, 2021 On Loan', 'Nov 30, 2020 On Loan',
       '2004 ~ 2020', '2009 ~ 2025', 'Aug 1, 2021 On Loan'], dtype=object)


# let´s search for the values 'On Loan' and 'Free' in 'Contract'
for index, row in fdc.iterrows():
    if 'On Loan' in row['Contract'] or 'Free' in row['Contract']:
        print(row['Contract'])

Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Free
Free
Free
Free
Free
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Free
Free
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Free
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jan 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Free
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2022 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2022 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2022 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jul 5, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2022 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Dec 31, 2021 On Loan
Free
Dec 31, 2020 On Loan
Dec 31, 2020 On Loan
May 31, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Dec 31, 2020 On Loan
Free
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jul 1, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Dec 31, 2021 On Loan
Free
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Jan 1, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Aug 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Free
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Dec 31, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2022 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Free
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Free
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2022 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Free
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Free
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jan 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Free
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Free
Jun 30, 2021 On Loan
Free
Dec 31, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 31, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Dec 31, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2022 On Loan
Dec 31, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Dec 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 1, 2021 On Loan
Dec 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Dec 31, 2020 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Dec 31, 2020 On Loan
Free
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jan 1, 2021 On Loan
Jun 30, 2021 On Loan
Jan 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 23, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jan 31, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Dec 31, 2020 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 1, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2022 On Loan
Free
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Free
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Dec 31, 2021 On Loan
Dec 31, 2020 On Loan
Free
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 31, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jan 31, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 3, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Dec 31, 2020 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Nov 27, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Dec 31, 2020 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2022 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Nov 27, 2021 On Loan
Free
Jan 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Dec 31, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 23, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 17, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jan 31, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2023 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 1, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Dec 31, 2021 On Loan
Free
Jun 30, 2021 On Loan
May 31, 2021 On Loan
Free
Jun 23, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 23, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2022 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 23, 2021 On Loan
Jun 30, 2021 On Loan
Free
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 3, 2021 On Loan
Jan 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jul 31, 2021 On Loan
Dec 31, 2020 On Loan
Nov 27, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Nov 22, 2020 On Loan
May 31, 2021 On Loan
Jun 30, 2021 On Loan
May 31, 2022 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Dec 31, 2020 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Nov 27, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Nov 22, 2020 On Loan
Jun 30, 2021 On Loan
Free
Nov 22, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 3, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Nov 22, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 23, 2021 On Loan
Jun 30, 2021 On Loan
Jan 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jan 31, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Free
Free
Jun 30, 2021 On Loan
Dec 30, 2020 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Free
Dec 31, 2020 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Dec 31, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Nov 27, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jan 1, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 4, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2022 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 1, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Free
Free
Jun 30, 2021 On Loan
Jan 1, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Nov 30, 2020 On Loan
May 31, 2022 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 1, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2022 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Dec 31, 2020 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Free
May 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2022 On Loan
Dec 31, 2021 On Loan
Jan 31, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jan 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 1, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Dec 31, 2020 On Loan
Jan 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 1, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Nov 22, 2020 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jan 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2022 On Loan
Jun 30, 2021 On Loan
Jun 30, 2022 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 23, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jan 31, 2021 On Loan
Jun 30, 2021 On Loan
Nov 27, 2021 On Loan
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Free
Dec 31, 2020 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jan 1, 2021 On Loan
Nov 30, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jan 1, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Nov 30, 2020 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Nov 30, 2020 On Loan
Jun 30, 2021 On Loan
Aug 1, 2021 On Loan
Jun 30, 2021 On Loan
Free
Jun 30, 2021 On Loan
Nov 30, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Free
Dec 31, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jan 1, 2021 On Loan
Jun 30, 2021 On Loan
Free
Free
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Nov 30, 2020 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jan 4, 2021 On Loan
Jun 30, 2021 On Loan
Nov 30, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2021 On Loan
Free
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jan 1, 2021 On Loan
Dec 31, 2020 On Loan
Jun 30, 2021 On Loan
Jun 30, 2021 On Loan
Aug 31, 2021 On Loan
Jun 30, 2021 On Loan
Dec 31, 2020 On Loan
Dec 31, 2020 On Loan
Dec 31, 2020 On Loan


# Let´s use a function to extract the values into new columns
def extract_contract_info(contract):
    if contract == 'Free' or 'On Loan' in contract:
        start_date = np.nan
        end_date = np.nan
        contract_length = 0
    else:
        start_date, end_date = contract.split(' ~ ')
        start_year = int(start_date[:4])
        end_year = int(end_date[:4])
        contract_length = end_year - start_year
    return start_date, end_date, contract_length
    
# We apply the function to the 'Contract' column. This will create 3 new columns
new_cols = ['Contract Start', 'Contract End', 'Contract Length(years)']
new_data = fdc['Contract'].apply(lambda x: pd.Series(extract_contract_info(x)))

# Now we need a loop to go through all the values of the column
for i in range(len(new_cols)):
    fdc.insert(loc=fdc.columns.get_loc('Contract')+1+i, column=new_cols[i], value=new_data[i])


# Run to see the changes
fdc_new_col = fdc[['Contract', 'Contract Start', 'Contract End', 'Contract Length(years)']]
fdc_new_col.head()


# Let´s define the contract categories
def categorize_contract_status(contract):
    if contract == 'Free':
        return 'Free'
    elif 'On Loan' in contract:
        return 'On Loan'
    else:
        return 'Contract'

# Add the new column 'Contract Status'
fdc.insert(fdc.columns.get_loc('Contract Length(years)')+1, 'Contract Status', fdc['Contract'].apply(categorize_contract_status))
fdc_new_col = fdc[['Contract', 'Contract Start', 'Contract End', 'Contract Length(years)', 'Contract Status']]
fdc_new_col.sample(5)


fdc['Positions'].dtype

dtype('O')


fdc['Positions'].unique()

array(['RW, ST, CF', 'ST, LW', 'GK', 'CAM, CM', 'LW, CAM', 'ST', 'RW',
       'ST, LW, RW', 'CB', 'LW', 'CDM', 'CF, ST', 'LW, RW', 'CDM, CM',
       'CDM, RB', 'CF, CAM', 'LW, ST', 'CM', 'ST, CF, LW', 'RM, LM, CAM',
       'RB', 'RW, CAM, CM', 'LB', 'LM, CF', 'CF', 'RW, LW', 'CAM, RM, RW',
       'CM, CDM', 'CAM, CF, ST', 'CM, CDM, CAM', 'CF, LW, CAM',
       'CAM, RM, CF', 'LM, ST', 'RM, LM, RW', 'LM', 'CAM, RW', 'CB, CDM',
       'RW, RM', 'LW, CF', 'CM, RM, LM', 'LB, LM', 'CAM, CM, RM',
       'CAM, CM, CF', 'CAM, CF', 'LM, RM, LW', 'LM, LB, CM', 'CM, LM, LB',
       'RM, RW', 'RM, CM', 'CAM, CM, LW', 'CB, LB', 'RM, RB', 'ST, RW',
       'LM, RW, LW', 'RB, LB', 'RB, RM', 'RM', 'LM, RM, CF', 'CAM, RM',
       'RB, RWB', 'CDM, CB, CM', 'CAM, RM, ST', 'LM, LW, RM', 'CM, CAM',
       'ST, RM, CF', 'LM, RM', 'RM, CF', 'LM, LWB', 'RW, RM, CF',
       'RB, CM', 'LW, CAM, RW', 'CAM, LW, CM', 'CM, CAM, CDM',
       'RW, LW, CAM', 'CM, CAM, LM', 'CM, RM, ST', 'CDM, CM, RB',
       'ST, CAM', 'CAM, LW, ST', 'LB, CB, LWB', 'RM, ST', 'CB, CDM, LB',
       'RWB, RM', 'CM, LM, RM', 'RB, CDM, CM', 'RW, LW, RM', 'LM, LW',
       'CM, LM', 'LM, LB', 'RM, LM, CF', 'LB, LM, RM', 'CDM, CM, CAM',
       'ST, LW, RM', 'CAM, CM, ST', 'ST, CF', 'LWB, LB', 'LW, RW, LM',
       'RM, RW, ST', 'LWB', 'CF, ST, CAM', 'LM, CAM, RM', 'RB, CB',
       'ST, LM', 'RW, CAM', 'LM, CAM', 'RWB, RB', 'ST, RW, LW', 'CAM',
       'RB, RM, RW', 'LB, LWB', 'RM, CAM', 'CAM, ST', 'CDM, CB',
       'CF, LM, LW', 'CAM, LM, LW', 'LW, RW, CAM', 'CB, RB',
       'RB, CB, RWB', 'LM, LW, ST', 'LW, ST, CM', 'RM, CAM, CM',
       'CB, RB, RWB', 'RW, ST, LW', 'LW, CAM, CM', 'CM, LM, CDM',
       'LB, LM, LWB', 'CB, LB, RB', 'CAM, LW', 'RWB, RB, RM',
       'CF, CAM, ST', 'RM, CAM, RW', 'RW, ST', 'LW, LM', 'RB, RM, CM',
       'ST, CAM, RW', 'CM, RB, LB', 'CAM, LM, RM', 'RB, RW', 'LM, CM',
       'RM, LM, CM', 'CDM, CM, CB', 'CM, CF', 'CF, LW, RW', 'ST, RM',
       'CAM, CM, CDM', 'LB, CB', 'RW, RWB', 'ST, LM, RM', 'RM, RWB, RB',
       'LM, ST, CAM', 'CAM, ST, CF', 'LW, CM', 'RB, RWB, LB', 'RM, LM',
       'CM, CAM, RM', 'ST, LW, CAM', 'RM, RW, CAM', 'CM, CDM, RM',
       'RB, RM, RWB', 'CDM, CB, LB', 'CAM, ST, LM', 'LM, CM, RM',
       'CF, RW', 'CAM, RM, LW', 'CM, RM, CDM', 'LB, LWB, LM',
       'LW, RW, CF', 'LW, LM, CAM', 'LWB, LW, LB', 'CDM, CM, LM',
       'CB, CM', 'RWB, RW, RB', 'ST, RW, RM', 'LW, RW, CM', 'LM, RM, CAM',
       'LB, RB', 'RWB, RM, RB', 'LM, RB, LB', 'LW, LM, ST', 'ST, RM, LM',
       'CAM, CM, LM', 'CF, CM', 'LW, ST, RW', 'ST, CAM, CF', 'LB, CB, LM',
       'CM, CDM, LM', 'RM, RWB', 'LB, LM, RB', 'RB, LB, CB',
       'CM, CDM, RB', 'LB, CM', 'CF, CAM, LW', 'RM, LWB', 'CF, ST, LM',
       'LB, RB, CB', 'LW, CF, ST', 'RM, ST, CAM', 'LW, RW, ST',
       'ST, LW, LM', 'CM, LW', 'CDM, CM, LB', 'CM, RM, CAM',
       'ST, CF, CAM', 'CM, LM, CAM', 'RWB', 'LM, ST, RM', 'CAM, RM, CM',
       'CAM, LM, CM', 'RW, LW, ST', 'CAM, RM, LM', 'CF, ST, LW',
       'LWB, LB, LM', 'RM, CM, CAM', 'LB, LM, CAM', 'CAM, CDM',
       'RW, RB, RM', 'RM, ST, RW', 'CM, CAM, LW', 'CF, ST, RW',
       'LM, RM, RB', 'LM, RM, CM', 'CB, RB, RM', 'RB, CDM', 'RM, ST, LM',
       'LB, LWB, CB', 'CDM, LB', 'LM, RM, ST', 'RWB, RB, LWB',
       'ST, LM, CAM', 'ST, RM, CAM', 'RB, RWB, RM', 'CF, LM, CAM',
       'CAM, CF, RW', 'RB, RM, LB', 'CDM, RWB', 'CM, RW, CAM',
       'ST, CF, LM', 'RM, LM, RWB', 'RB, LB, RM', 'LM, LW, LB',
       'RM, LM, ST', 'CAM, LM', 'ST, LM, LW', 'LW, RW, RM', 'CF, LW',
       'LB, CM, RB', 'RB, CB, CDM', 'CB, RB, LB', 'CAM, RW, RM',
       'LWB, LM', 'LW, CF, LM', 'CAM, CF, CM', 'LWB, CB', 'CM, CDM, LW',
       'LM, CM, LB', 'CM, RM', 'RW, LW, CF', 'CM, CDM, CB', 'LM, RM, RW',
       'RM, RW, LM', 'RM, CAM, LM', 'LWB, RM, LB', 'RM, LB, RB',
       'CM, CF, RM', 'RM, RB, LB', 'ST, LM, RW', 'CAM, ST, LW',
       'CF, LW, ST', 'LM, LW, CAM', 'RM, RW, CF', 'LW, LM, CF',
       'CM, RW, LW', 'LM, CM, CAM', 'CAM, RW, ST', 'CM, CAM, CF',
       'RW, LWB, LW', 'CB, RB, CDM', 'RW, RM, LW', 'LW, ST, LM',
       'RWB, RW', 'ST, CAM, LM', 'CM, CB', 'RM, RW, CM', 'LWB, CM',
       'RM, LM, LB', 'RM, CAM, ST', 'CDM, RM, RB', 'LM, LB, RB',
       'LB, RB, RWB', 'RM, RWB, CAM', 'CAM, LM, CF', 'LM, CAM, CM',
       'CF, CAM, RW', 'CDM, CM, RM', 'CF, CAM, CM', 'ST, RM, LW',
       'CB, CDM, CM', 'RB, RW, LB', 'ST, RW, CAM', 'CM, LB', 'LW, RM, RW',
       'CM, RM, RW', 'RM, CF, LM', 'CF, LM, RM', 'CAM, ST, RM', 'RW, CF',
       'CM, CAM, ST', 'CAM, CDM, CM', 'RM, RW, LW', 'CAM, LM, LB',
       'CAM, RW, LW', 'CDM, CAM', 'LWB, LM, LB', 'RW, LB', 'LW, CAM, CF',
       'RB, RWB, CB', 'LM, CF, RM', 'RB, LB, RWB', 'RM, LW',
       'CAM, LM, ST', 'LW, LM, LB', 'LB, RB, RM', 'CM, LWB',
       'CDM, RB, RM', 'RM, CM, RB', 'ST, RM, RW', 'LM, RM, LWB',
       'RW, LW, LM', 'LW, CF, RW', 'CM, CDM, LB', 'ST, LM, RB', 'LB, CDM',
       'CM, CB, CDM', 'CB, LB, CDM', 'RW, ST, RM', 'RM, ST, CF',
       'CAM, LW, LM', 'LM, ST, LW', 'CAM, RM, RB', 'RB, CM, RM',
       'CM, LW, LM', 'ST, CAM, RM', 'RW, ST, CAM', 'CM, LB, LM',
       'RB, CDM, RM', 'LM, RWB, RM', 'LM, CAM, LB', 'CAM, LW, RW',
       'CM, RM, RWB', 'RW, CAM, LW', 'RB, CB, LB', 'LB, LM, LW', 'RW, RB',
       'LM, LWB, CM', 'RM, CAM, RB', 'RM, CM, ST', 'RB, RM, LM',
       'LM, LB, LW', 'LM, ST, CF', 'RW, LWB, RM', 'LM, CAM, LW',
       'CB, RWB', 'RM, LW, CAM', 'LB, LW', 'CDM, CM, ST', 'LB, CM, CDM',
       'CF, CAM, LM', 'RW, CAM, RM', 'RW, LM, LW', 'RW, CM, CAM',
       'LM, LW, RW', 'RM, LW, RW', 'LW, LWB, RWB', 'RM, LM, RB',
       'CB, CDM, RB', 'CAM, RW, CM', 'ST, CAM, CM', 'LWB, LM, CM',
       'RW, LW, RWB', 'CB, LB, LWB', 'CM, LM, ST', 'CM, LWB, LM',
       'LB, LW, LM', 'RB, CM, CAM', 'RM, CAM, CF', 'CM, RW',
       'RW, RM, CAM', 'CB, LWB', 'CAM, RB', 'LM, CM, CDM', 'RM, LB, LM',
       'CB, RB, CM', 'RB, CM, RWB', 'LB, RB, LM', 'CAM, ST, RW',
       'LB, LW, RW', 'CDM, CAM, CM', 'CAM, ST, CM', 'LM, CAM, ST',
       'LM, ST, CM', 'CAM, CF, RM', 'CF, ST, RM', 'LM, ST, LB',
       'ST, CAM, LW', 'LWB, CM, LB', 'RWB, RM, LWB', 'LWB, LM, RB',
       'CM, LW, ST', 'LB, LM, CB', 'CM, RM, RB', 'RWB, CB', 'RM, RB, RWB',
       'LB, CM, LM', 'ST, LW, CF', 'RM, RWB, ST', 'CF, ST, CM',
       'LM, LB, LWB', 'LM, RW', 'LM, LWB, LB', 'LB, LWB, RB',
       'RM, RB, LM', 'RM, CM, LM', 'LW, ST, CAM', 'RB, RM, CB',
       'LWB, RWB, LB', 'CF, RM, RW', 'RM, CF, CAM', 'LM, LW, CF',
       'RB, LM, LB', 'CAM, CM, RW', 'LB, CM, LW', 'CM, CF, CAM',
       'RW, RM, ST', 'CDM, RB, CM', 'CM, RB', 'RB, RWB, RW', 'LM, RW, RM',
       'CM, RB, CDM', 'LM, RM, LB', 'CDM, RB, CB', 'RB, CB, RM',
       'RWB, LW, LM', 'ST, CF, RW', 'RM, CM, CDM', 'LM, CF, ST',
       'LW, CAM, LM', 'CDM, LM, CM', 'ST, CM, CAM', 'LM, RM, RWB',
       'CM, CAM, RW', 'CAM, LM, RW', 'LW, LM, RM', 'CAM, RW, CF',
       'LM, ST, RW', 'CB, LWB, LB', 'RW, CF, LW', 'CB, CM, CDM',
       'CB, CAM', 'LW, LB', 'CDM, RB, LB', 'ST, CM, RB', 'RWB, LWB, RB',
       'CM, CB, RB', 'LB, LW, RB', 'CF, RM, RWB', 'LB, RM', 'RM, RW, RB',
       'LB, RM, RB', 'CDM, RM, CM', 'CDM, LB, CM', 'LM, CDM, CM',
       'RW, LM, CAM', 'LM, LB, RM', 'RM, CF, ST', 'RW, CM', 'RM, RWB, LM',
       'CF, CM, ST', 'RW, RM, LM', 'LM, CM, RW', 'RWB, CM', 'RB, RM, CDM',
       'CM, CB, CAM', 'CF, CM, LM', 'RWB, LWB, RM', 'CF, RM',
       'CM, CF, ST', 'RWB, RB, CB', 'LWB, RWB', 'RM, LM, LW',
       'LW, RM, CM', 'LW, CAM, ST', 'ST, RM, RWB', 'ST, CM',
       'CDM, CM, RWB', 'LB, CB, RB', 'CB, ST, CAM', 'CF, RM, CM',
       'CDM, CB, RB', 'CM, LM, RB', 'RB, CB, LWB', 'LM, LB, CDM',
       'LW, LWB', 'RW, CF, CAM', 'RM, RB, CAM', 'LB, RB, CM',
       'RM, CDM, LM', 'LWB, LB, CB', 'LB, RB, CDM', 'RB, CM, LB',
       'CDM, RM', 'LM, RB, CB', 'RW, RWB, RM', 'RM, LW, ST',
       'LB, RB, LWB', 'RM, LM, CDM', 'RB, RM, CAM', 'RM, ST, LW',
       'CF, LM', 'RB, LB, CDM', 'RW, LW, RB', 'RB, RWB, CDM',
       'CM, CAM, LB', 'LW, RW, LWB', 'CB, LWB, RWB', 'LB, CB, CDM',
       'ST, RB, RM', 'CM, RB, RM', 'RW, LM', 'RM, CF, RB', 'CB, RM, RB',
       'RWB, LWB', 'CM, ST, CDM', 'LWB, LW', 'CB, RB, ST', 'LB, LWB, CDM',
       'CB, RWB, RM', 'CB, ST', 'CM, ST', 'LM, LWB, CB', 'ST, LW, LWB',
       'LM, CDM, RM', 'LWB, LB, RW', 'RB, CM, CB', 'LM, RW, ST',
       'CM, RM, LW', 'ST, RW, LM', 'RWB, CM, RM', 'CAM, RWB, CM',
       'CM, RB, LM', 'RM, RB, RW', 'LW, LM, CM', 'RM, LM, LWB',
       'ST, LM, CF', 'LW, RM, CAM', 'RB, RWB, LWB', 'LB, CM, ST',
       'ST, LWB', 'RB, ST, LM', 'CDM, CAM, LM', 'LM, ST, LWB',
       'LB, RB, RW', 'RM, CDM', 'RB, LB, LWB', 'RW, CAM, LM',
       'LB, CDM, LM', 'RW, RB, LM', 'CB, LM', 'RW, LM, RM', 'RM, LB, CM',
       'LW, CAM, RM', 'RW, RB, LB', 'LW, LB, LM', 'CDM, RB, RWB',
       'CB, RW', 'CM, LM, LW', 'LM, RM, CB', 'LM, LWB, ST', 'ST, CB, CDM',
       'LB, CDM, CB', 'CDM, CB, RM', 'RB, RWB, CM', 'ST, RW, CF',
       'ST, CB', 'ST, LW, CM', 'LM, LB, ST', 'ST, CB, RB', 'RB, LM',
       'GK, RB', 'LM, LW, CM', 'LM, CM, LW', 'CB, CAM, CM', 'LM, RB',
       'RWB, CAM', 'RB, CAM', 'CB, CDM, RM', 'ST, RB', 'ST, RWB',
       'CAM, CDM, LM', 'CB, RWB, RB', 'LM, LWB, RM', 'LB, LM, CM',
       'CM, CF, LM', 'CM, RWB', 'CDM, LM', 'ST, CF, RM', 'CAM, LB',
       'RB, RM, ST', 'LM, CDM', 'CDM, RW, RB', 'LM, CF, CAM',
       'LWB, LM, RWB', 'CF, RM, ST', 'CAM, CF, LM', 'RB, ST, CB',
       'RW, RB, LW', 'LB, LM, CDM', 'RB, CB, ST', 'RWB, CB, RB',
       'CDM, ST', 'LW, RM, LM', 'RB, LWB', 'CDM, LB, CB', 'LM, LB, CF',
       'RB, CDM, LB', 'LB, LWB, CM', 'RM, LM, CB', 'CAM, LW, CF',
       'LB, LW, CM', 'RB, CM, CDM', 'LWB, LM, ST', 'CM, RW, RM', 'CB, RM',
       'CM, LM, CB', 'LM, LB, CB', 'ST, RW, CM', 'RM, ST, CM',
       'RWB, RM, LM', 'CM, CAM, RB', 'CM, RWB, CDM', 'LB, LWB, LW'],
      dtype=object)


missing_values = fdc['Positions'].isnull().sum()
print("Number of missing values: ", missing_values)

Number of missing values:  0


# Let´s check the data type of the values
fdc['Height'].dtype

dtype('O')


fdc['Height'].unique()

array(['170cm', '187cm', '188cm', '181cm', '175cm', '184cm', '191cm',
       '178cm', '193cm', '185cm', '199cm', '173cm', '168cm', '176cm',
       '177cm', '183cm', '180cm', '189cm', '179cm', '195cm', '172cm',
       '182cm', '186cm', '192cm', '165cm', '194cm', '167cm', '196cm',
       '163cm', '190cm', '174cm', '169cm', '171cm', '197cm', '200cm',
       '166cm', '6\'2"', '164cm', '198cm', '6\'3"', '6\'5"', '5\'11"',
       '6\'4"', '6\'1"', '6\'0"', '5\'10"', '5\'9"', '5\'6"', '5\'7"',
       '5\'4"', '201cm', '158cm', '162cm', '161cm', '160cm', '203cm',
       '157cm', '156cm', '202cm', '159cm', '206cm', '155cm'], dtype=object)


# Let´s create a function to clean all the numb and transform the others to cm
def convert_height(height):
    if 'cm' in height:
        return int(height.strip('cm'))
    else:
        feet, inches = height.split("'")
        total_inches = int(feet) * 12 + int(inches.strip('"'))
        return round(total_inches * 2.54)
    
# Apply the function to the 'Height' column
fdc['Height'] = fdc['Height'].apply(convert_height)
fdc['Height'].unique()

array([170, 187, 188, 181, 175, 184, 191, 178, 193, 185, 199, 173, 168,
       176, 177, 183, 180, 189, 179, 195, 172, 182, 186, 192, 165, 194,
       167, 196, 163, 190, 174, 169, 171, 197, 200, 166, 164, 198, 201,
       158, 162, 161, 160, 203, 157, 156, 202, 159, 206, 155], dtype=int64)


# Rename the 'Height' column to express that the values are in cm
fdc = fdc.rename(columns={'Height':'Height(cm)'})
fdc['Height(cm)'].sample(3)

14801    174
9579     180
17348    180
Name: Height(cm), dtype: int64


# Let´s check the data type
fdc['Weight'].dtype

dtype('O')


# How the data is composed
fdc['Weight'].unique()

array(['72kg', '83kg', '87kg', '70kg', '68kg', '80kg', '71kg', '91kg',
       '73kg', '85kg', '92kg', '69kg', '84kg', '96kg', '81kg', '82kg',
       '75kg', '86kg', '89kg', '74kg', '76kg', '64kg', '78kg', '90kg',
       '66kg', '60kg', '94kg', '79kg', '67kg', '65kg', '59kg', '61kg',
       '93kg', '88kg', '97kg', '77kg', '62kg', '63kg', '95kg', '100kg',
       '58kg', '183lbs', '179lbs', '172lbs', '196lbs', '176lbs', '185lbs',
       '170lbs', '203lbs', '168lbs', '161lbs', '146lbs', '130lbs',
       '190lbs', '174lbs', '148lbs', '165lbs', '159lbs', '192lbs',
       '181lbs', '139lbs', '154lbs', '157lbs', '163lbs', '98kg', '103kg',
       '99kg', '102kg', '56kg', '101kg', '57kg', '55kg', '104kg', '107kg',
       '110kg', '53kg', '50kg', '54kg', '52kg'], dtype=object)


# Let´s create a function to create our data in kg and int
def convert_weight(weight):
    if "kg" in weight:
        return int(weight.strip('kg'))
    else:
        pounds = int(weight.strip('lbs'))
        return round(pounds/2.205)
    
# Apply the function to the weight column
fdc['Weight'] = fdc['Weight'].apply(convert_weight)
fdc['Weight'].unique()

array([ 72,  83,  87,  70,  68,  80,  71,  91,  73,  85,  92,  69,  84,
        96,  81,  82,  75,  86,  89,  74,  76,  64,  78,  90,  66,  60,
        94,  79,  67,  65,  59,  61,  93,  88,  97,  77,  62,  63,  95,
       100,  58,  98, 103,  99, 102,  56, 101,  57,  55, 104, 107, 110,
        53,  50,  54,  52], dtype=int64)


# Rename the 'Weight' column
fdc = fdc.rename(columns={'Weight':'Weight(kg)'})
fdc['Weight(kg)'].sample(3)

5504     68
18272    70
7078     81
Name: Weight(kg), dtype: int64


fdc['Preferred Foot'].dtype

dtype('O')


fdc['Preferred Foot'].unique()

array(['Left', 'Right'], dtype=object)


fdc[['Preferred Foot']].head(10)


fdc['BOV'].dtype

dtype('int64')


fdc['BOV'].unique()

array([93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83, 82, 81, 80, 79, 78, 77,
       76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60,
       59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48], dtype=int64)


missing_values = fdc['BOV'].isnull().sum()
print("Number of missing values: ", missing_values)

Number of missing values:  0


fdc['Best Position'].unique()

array(['RW', 'ST', 'GK', 'CAM', 'LW', 'CB', 'CDM', 'CF', 'CM', 'RB', 'LB',
       'LM', 'RM', 'LWB', 'RWB'], dtype=object)


missing_values = fdc['Best Position'].isnull().sum()
print("Number of missing values: ", missing_values)

Number of missing values:  0


fdc['Joined'].dtype

dtype('O')


fdc['Joined'].unique()

array(['01-Jul-04', '10-Jul-18', '16-Jul-14', ..., '22-Sep-18',
       '28-Feb-15', '06-Mar-18'], dtype=object)


missing_values = fdc['Joined'].isnull().sum()
print("Number of missing values: ", missing_values)

Number of missing values:  0


fdc['Joined'] = pd.to_datetime(fdc['Joined'], dayfirst=True, format='%d-%b-%y')
fdc['Joined'].sample(15)

7652    2019-07-01
5197    2019-07-07
6777    2020-09-14
11794   2017-12-08
6256    2020-08-25
16396   2017-09-01
12845   2018-01-07
15884   2019-07-03
2631    2017-07-01
6790    2019-01-12
12664   2016-07-01
9871    2016-08-21
11394   2020-09-04
4683    2017-07-04
3250    2020-08-11
Name: Joined, dtype: datetime64[ns]


print(fdc['Joined'].dt.strftime('%d-%b-%y'))

0        01-Jul-04
1        10-Jul-18
2        16-Jul-14
3        30-Aug-15
4        03-Aug-17
           ...    
18974    13-Jul-18
18975    01-Aug-20
18976    08-Mar-19
18977    22-Sep-20
18978    29-Jul-19
Name: Joined, Length: 18979, dtype: object


# Check the data type
fdc['Loan Date End'].dtype

dtype('O')


fdc['Loan Date End'].unique()

array([nan, '30-Jun-21', '31-Dec-20', '30-Jan-21', '30-Jun-22',
       '31-May-21', '05-Jul-21', '31-Dec-21', '01-Jul-21', '01-Jan-21',
       '31-Aug-21', '31-Jan-21', '30-Dec-21', '23-Jun-21', '03-Jan-21',
       '27-Nov-21', '17-Jan-21', '30-Jun-23', '31-Jul-21', '22-Nov-20',
       '31-May-22', '30-Dec-20', '04-Jan-21', '30-Nov-20', '01-Aug-21'],
      dtype=object)


# Let´s compare the data with the one stored in column 'Contract Status'
on_loan = fdc[fdc['Contract Status'] == 'On Loan']
on_loan[['Contract', 'Contract Status', 'Loan Date End']]


fdc['Value'].dtype

dtype('O')


fdc['Value'].unique()

array(['€103.5M', '€63M', '€120M', '€129M', '€132M', '€111M', '€120.5M',
       '€102M', '€185.5M', '€110M', '€113M', '€90.5M', '€82M', '€17.5M',
       '€83.5M', '€33.5M', '€114.5M', '€78M', '€103M', '€109M', '€92M',
       '€10M', '€76.5M', '€89.5M', '€87.5M', '€79.5M', '€124M', '€114M',
       '€95M', '€92.5M', '€105.5M', '€88.5M', '€85M', '€81.5M', '€26M',
       '€21M', '€56M', '€67.5M', '€53M', '€36.5M', '€51M', '€65.5M',
       '€46.5M', '€61.5M', '€72.5M', '€77.5M', '€43.5M', '€32.5M', '€36M',
       '€32M', '€54M', '€49.5M', '€57M', '€66.5M', '€74.5M', '€71.5M',
       '€121M', '€99M', '€67M', '€86.5M', '€93.5M', '€70M', '€62M',
       '€66M', '€58M', '€44M', '€81M', '€37M', '€14.5M', '€46M', '€47.5M',
       '€52.5M', '€54.5M', '€34.5M', '€57.5M', '€51.5M', '€44.5M', '€55M',
       '€48M', '€60.5M', '€63.5M', '€61M', '€29M', '€58.5M', '€55.5M',
       '€42M', '€40.5M', '€43M', '€45.5M', '€34M', '€26.5M', '€42.5M',
       '€35.5M', '€45M', '€41.5M', '€40M', '€11M', '€13.5M', '€29.5M',
       '€27M', '€15.5M', '€38.5M', '€52M', '€33M', '€19M', '€73.5M',
       '€38M', '€35M', '€47M', '€24M', '€30.5M', '€18M', '€28M', '€25.5M',
       '€25M', '€31M', '€23.5M', '€30M', '€31.5M', '€22.5M', '€28.5M',
       '€4M', '€12.5M', '€37.5M', '€27.5M', '€16M', '€15M', '€20.5M',
       '€22M', '€3.4M', '€5M', '€56.5M', '€62.5M', '€0', '€39M', '€24.5M',
       '€21.5M', '€13M', '€8M', '€20M', '€8.5M', '€2.9M', '€9M', '€4.6M',
       '€50M', '€23M', '€18.5M', '€7M', '€19.5M', '€5.5M', '€7.5M',
       '€3.8M', '€14M', '€10.5M', '€16.5M', '€3.6M', '€9.5M', '€39.5M',
       '€17M', '€12M', '€11.5M', '€4.9M', '€3M', '€1.9M', '€6.5M',
       '€1.7M', '€2.4M', '€3.1M', '€6M', '€3.7M', '€4.7M', '€4.3M',
       '€2.1M', '€1.2M', '€1.8M', '€4.8M', '€3.2M', '€1.3M', '€825K',
       '€2.3M', '€1.5M', '€3.9M', '€2.6M', '€3.5M', '€2.8M', '€2.7M',
       '€4.4M', '€4.1M', '€950K', '€1.6M', '€625K', '€1.1M', '€4.5M',
       '€4.2M', '€2.2M', '€3.3M', '€1.4M', '€2M', '€475K', '€925K',
       '€750K', '€725K', '€2.5M', '€1M', '€350K', '€525K', '€600K',
       '€850K', '€800K', '€550K', '€250K', '€400K', '€425K', '€575K',
       '€210K', '€325K', '€900K', '€875K', '€650K', '€700K', '€500K',
       '€975K', '€375K', '€775K', '€275K', '€180K', '€450K', '€675K',
       '€150K', '€240K', '€300K', '€130K', '€220K', '€200K', '€110K',
       '€170K', '€230K', '€90K', '€120K', '€80K', '€190K', '€140K',
       '€160K', '€100K', '€60K', '€50K', '€70K', '€45K', '€35K', '€40K',
       '€25K', '€20K', '€15K', '€30K', '€9K'], dtype=object)


# Convert the values to strings
fdc['Value'] = fdc['Value'].astype(str)

# The values are strings, so we replace the € currency
fdc['Value'] = fdc['Value'].str.replace('€','')
fdc['Value'].dtype

dtype('O')


fdc['Value'].unique()

array(['103.5M', '63M', '120M', '129M', '132M', '111M', '120.5M', '102M',
       '185.5M', '110M', '113M', '90.5M', '82M', '17.5M', '83.5M',
       '33.5M', '114.5M', '78M', '103M', '109M', '92M', '10M', '76.5M',
       '89.5M', '87.5M', '79.5M', '124M', '114M', '95M', '92.5M',
       '105.5M', '88.5M', '85M', '81.5M', '26M', '21M', '56M', '67.5M',
       '53M', '36.5M', '51M', '65.5M', '46.5M', '61.5M', '72.5M', '77.5M',
       '43.5M', '32.5M', '36M', '32M', '54M', '49.5M', '57M', '66.5M',
       '74.5M', '71.5M', '121M', '99M', '67M', '86.5M', '93.5M', '70M',
       '62M', '66M', '58M', '44M', '81M', '37M', '14.5M', '46M', '47.5M',
       '52.5M', '54.5M', '34.5M', '57.5M', '51.5M', '44.5M', '55M', '48M',
       '60.5M', '63.5M', '61M', '29M', '58.5M', '55.5M', '42M', '40.5M',
       '43M', '45.5M', '34M', '26.5M', '42.5M', '35.5M', '45M', '41.5M',
       '40M', '11M', '13.5M', '29.5M', '27M', '15.5M', '38.5M', '52M',
       '33M', '19M', '73.5M', '38M', '35M', '47M', '24M', '30.5M', '18M',
       '28M', '25.5M', '25M', '31M', '23.5M', '30M', '31.5M', '22.5M',
       '28.5M', '4M', '12.5M', '37.5M', '27.5M', '16M', '15M', '20.5M',
       '22M', '3.4M', '5M', '56.5M', '62.5M', '0', '39M', '24.5M',
       '21.5M', '13M', '8M', '20M', '8.5M', '2.9M', '9M', '4.6M', '50M',
       '23M', '18.5M', '7M', '19.5M', '5.5M', '7.5M', '3.8M', '14M',
       '10.5M', '16.5M', '3.6M', '9.5M', '39.5M', '17M', '12M', '11.5M',
       '4.9M', '3M', '1.9M', '6.5M', '1.7M', '2.4M', '3.1M', '6M', '3.7M',
       '4.7M', '4.3M', '2.1M', '1.2M', '1.8M', '4.8M', '3.2M', '1.3M',
       '825K', '2.3M', '1.5M', '3.9M', '2.6M', '3.5M', '2.8M', '2.7M',
       '4.4M', '4.1M', '950K', '1.6M', '625K', '1.1M', '4.5M', '4.2M',
       '2.2M', '3.3M', '1.4M', '2M', '475K', '925K', '750K', '725K',
       '2.5M', '1M', '350K', '525K', '600K', '850K', '800K', '550K',
       '250K', '400K', '425K', '575K', '210K', '325K', '900K', '875K',
       '650K', '700K', '500K', '975K', '375K', '775K', '275K', '180K',
       '450K', '675K', '150K', '240K', '300K', '130K', '220K', '200K',
       '110K', '170K', '230K', '90K', '120K', '80K', '190K', '140K',
       '160K', '100K', '60K', '50K', '70K', '45K', '35K', '40K', '25K',
       '20K', '15K', '30K', '9K'], dtype=object)


# Check for missing values or null
fdc['Value'].isna()

0        False
1        False
2        False
3        False
4        False
         ...  
18974    False
18975    False
18976    False
18977    False
18978    False
Name: Value, Length: 18979, dtype: bool


missing_values = fdc['Value'].isnull().sum()
print("Number of missing values: ", missing_values)

Number of missing values:  0


print(fdc[['Value','Surname']].head(15))

     Value                     Surname
0   103.5M                       Messi
1      63M   Ronaldo dos Santos Aveiro
2     120M                       Oblak
3     129M                   De Bruyne
4     132M         da Silva Santos Jr.
5     111M                 Lewandowski
6   120.5M                       Salah
7     102M               Ramses Becker
8   185.5M                      Mbappé
9     110M                  ter Stegen
10    113M                    van Dijk
11  120.5M                        Mané
12   90.5M  Henrique Venancio Casimiro
13     82M                    Courtois
14   17.5M                       Neuer


# Use a lambda function to represent the values as they are
fdc['Value'] = fdc['Value'].apply(lambda x: float(x[:-1]) * 1e6 
                                  if x[-1] == 'M' and x[:-1] else float(x[:-1]) * 1e3 
                                  if x[-1] == 'K' and x[:-1] else float(x) if x else 0)

# # Because our values are expressed as float numbers, let´s convert them to int
fdc['Value'] = fdc['Value'].astype(int)
fdc['Value'].head(10)

0    103500000
1     63000000
2    120000000
3    129000000
4    132000000
5    111000000
6    120500000
7    102000000
8    185500000
9    110000000
Name: Value, dtype: int32


fdc = fdc.rename(columns={'Value':'Market Price(€)'})
fdc['Market Price(€)'].sample(3)

1302     11500000
255      35500000
17039      375000
Name: Market Price(€), dtype: int32


fdc['Wage'].dtype

dtype('O')


fdc['Wage'].unique()

array(['€560K', '€220K', '€125K', '€370K', '€270K', '€240K', '€250K',
       '€160K', '€260K', '€210K', '€310K', '€130K', '€350K', '€300K',
       '€190K', '€145K', '€195K', '€100K', '€140K', '€290K', '€82K',
       '€110K', '€230K', '€155K', '€200K', '€165K', '€95K', '€170K',
       '€105K', '€115K', '€150K', '€135K', '€55K', '€58K', '€81K', '€34K',
       '€120K', '€59K', '€90K', '€65K', '€56K', '€71K', '€18K', '€75K',
       '€47K', '€20K', '€84K', '€86K', '€74K', '€78K', '€27K', '€68K',
       '€85K', '€25K', '€46K', '€83K', '€54K', '€79K', '€175K', '€43K',
       '€49K', '€45K', '€38K', '€41K', '€39K', '€23K', '€51K', '€50K',
       '€87K', '€30K', '€14K', '€69K', '€31K', '€64K', '€53K', '€35K',
       '€21K', '€28K', '€17K', '€33K', '€70K', '€32K', '€89K', '€26K',
       '€40K', '€76K', '€72K', '€48K', '€36K', '€29K', '€60K', '€16K',
       '€37K', '€24K', '€52K', '€0', '€62K', '€73K', '€63K', '€19K',
       '€1K', '€66K', '€80K', '€12K', '€2K', '€42K', '€13K', '€900',
       '€57K', '€77K', '€61K', '€22K', '€67K', '€44K', '€15K', '€11K',
       '€8K', '€850', '€10K', '€88K', '€500', '€7K', '€6K', '€9K', '€5K',
       '€700', '€950', '€750', '€3K', '€650', '€600', '€4K', '€800',
       '€550'], dtype=object)


missing_values = fdc['Wage'].isnull().sum()
print("Number of missing values: ", missing_values)

Number of missing values:  0


# Replace the '€'
fdc['Wage'] = fdc['Wage'].str.replace('€', '')
print(fdc['Wage'])

0        560K
1        220K
2        125K
3        370K
4        270K
         ... 
18974      1K
18975     500
18976     500
18977      2K
18978      1K
Name: Wage, Length: 18979, dtype: object


# Convert the values
fdc['Wage'] = fdc['Wage'].replace({'K': '*1e3'}, regex=True).map(pd.eval).astype(int)
print(fdc['Wage'])

0        560000
1        220000
2        125000
3        370000
4        270000
          ...  
18974      1000
18975       500
18976       500
18977      2000
18978      1000
Name: Wage, Length: 18979, dtype: int32


fdc['Release Clause'].dtype

dtype('O')


missing_values = fdc['Release Clause'].isnull().sum()
print("Number of missing values: ", missing_values)

Number of missing values:  0


fdc['Release Clause'].unique()

array(['€138.4M', '€75.9M', '€159.4M', ..., '€59K', '€35K', '€64K'],
      dtype=object)


# Convert the values to strings
fdc['Release Clause'] = fdc['Release Clause'].astype(str)

# The values are strings, so we replace the € currency
fdc['Release Clause'] = fdc['Release Clause'].str.replace('€','')

# Use a lambda function to represent the values as they are
fdc['Release Clause'] = fdc['Release Clause'].apply(lambda x: float(x[:-1]) * 1e6 
                                  if x[-1] == 'M' and x[:-1] else float(x[:-1]) * 1e3 
                                  if x[-1] == 'K' and x[:-1] else float(x) if x else 0)

# # Because our values are expressed as float numbers, let´s convert them to int
fdc['Release Clause'] = fdc['Release Clause'].astype(int)
fdc['Release Clause'].head(10)

0    138400000
1     75900000
2    159400000
3    161000000
4    166500000
5    132000000
6    144300000
7    120300000
8    203100000
9    147700000
Name: Release Clause, dtype: int32


# Change the name of the column
fdc = fdc.rename(columns={'Release Clause': 'Release Clause(€)'})
fdc['Release Clause(€)'].head()

0    138400000
1     75900000
2    159400000
3    161000000
4    166500000
Name: Release Clause(€), dtype: int32


fdc['Attacking'].dtype

dtype('int64')


missing_values = fdc['Attacking'].isna().sum()
print("Number of missing values: ", missing_values)

Number of missing values:  0


fdc['Attacking'].unique()

array([429, 437,  95, 407, 408, 423, 392, 114, 118, 316, 410, 349,  86,
       119, 426, 374, 411, 360, 328, 383, 405, 123, 420, 224, 388, 397,
       425, 373, 365, 371, 311, 396, 345, 399, 400,  78, 280, 330, 403,
       379, 380,  94, 394, 419, 339, 293, 344, 390,  84, 359, 372, 377,
       346, 389, 386, 308, 277, 382, 368, 402, 292, 298, 366, 352, 363,
       322, 361,  91, 364, 341, 385, 355, 305, 321, 262,  93, 375, 387,
       356, 253, 285, 391, 353, 367,  90, 295, 378, 256, 338, 331,  69,
       105,  85, 358, 343, 319, 271, 113, 350, 406, 340, 393, 247, 334,
       351, 342, 302, 329, 354,  98, 301, 115, 384, 208,  72, 376,  92,
       258, 362,  74, 417,  99, 263,  88, 279, 101, 395, 100,  81,  87,
        55, 310,  82, 117, 409, 318, 323, 248, 315, 381, 348, 327, 309,
       130, 283, 336, 369, 106, 252, 320, 290, 370, 126, 251, 108, 335,
       297, 284,  80,  75, 357, 270,  97, 306, 337,  73, 286, 325, 326,
       324, 333, 103, 259, 273, 313, 296,  61, 312, 347, 401, 304, 278,
        83,  43, 314, 291, 264, 272, 317, 231, 250, 268,  54, 261, 255,
        70, 281, 265, 299, 287,  68, 294,  77, 219, 300, 269, 332, 289,
       288, 107, 282, 122, 244,  89, 112, 274, 276, 307, 229,  96, 109,
        76, 125, 102, 239, 227, 241, 257, 254, 228, 233, 124, 215, 246,
       110, 245, 214, 242, 266, 104,  66, 303, 260,  63, 230, 275,  50,
       238, 249, 111,  67, 240, 221, 237,  56, 235, 234, 243, 267, 232,
       203, 223,  64, 213, 222, 226, 225, 211, 207,  52, 173,  57, 217,
       236,  71, 204, 216, 199,  59, 189,  60, 194, 116, 205, 201, 193,
        65, 192, 209, 218, 128, 210,  79,  45, 206, 162, 220,  49, 197,
       202, 212,  58, 190, 181,  51,  62, 200, 198, 195, 191, 131, 185,
        42, 180, 182, 196, 188, 169, 187, 178,  53, 183, 184, 186, 165,
       172,  47, 171, 176, 159,  46, 179, 175, 167, 174, 161, 170, 177,
       164, 134, 168, 163, 166, 158, 150, 143,  48, 152, 160, 148, 151,
       157, 154, 141, 146, 147, 149, 156, 153, 138, 145, 142, 139, 155,
       144, 136, 137], dtype=int64)


fdc['Crossing'].dtype

dtype('int64')


missing_values = fdc['Crossing'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Crossing'].unique()

array([85, 84, 13, 94, 71, 79, 17, 78, 18, 53, 76, 58, 14, 15, 75, 66, 70,
       68, 91, 82, 20, 12, 30, 77, 88, 83, 93, 90, 87, 81, 73, 11, 54, 62,
       86, 80, 55, 42, 57, 65, 63, 64, 52, 40, 69, 47, 60,  9, 16, 44, 72,
       50, 56, 46, 89, 34, 45, 74, 49, 67, 24, 35, 36, 61, 19, 27, 25, 10,
       51, 38, 43, 59, 39, 48, 23,  8, 28, 92, 41, 29, 32, 22, 26, 37, 33,
       31, 21,  7,  6], dtype=int64)


fdc['Finishing'].dtype

dtype('int64')


missing_values = fdc['Finishing'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Finishing'].unique()

array([95, 11, 82, 87, 94, 91, 13, 14, 52, 90, 64, 88, 65, 85, 66, 84, 10,
       22, 76, 81, 56, 79, 57, 45, 77, 63, 86, 80, 15, 33, 67, 12, 72, 92,
       93, 51, 46, 60, 75, 55, 73, 83, 50, 42, 39, 40,  9, 68, 48, 37, 70,
       78, 69,  8, 53, 89, 25, 62, 71, 74, 44, 26, 19, 32, 18, 61, 58, 30,
       54, 36, 29, 16, 38, 59, 27, 34, 47, 20, 31, 49, 43, 41, 28,  5,  7,
        6, 21, 17, 35, 23, 24,  4,  3], dtype=int64)


fdc['Heading Accuracy'].dtype

dtype('int64')


missing_values = fdc['Heading Accuracy'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Heading Accuracy'].unique()

array([70, 90, 15, 55, 62, 85, 59, 19, 73, 11, 87, 84, 80, 13, 25, 91, 92,
       78, 46, 54, 72, 64, 14, 10, 61, 58, 83, 38, 69, 51, 67, 86, 75, 68,
       16, 81, 21, 79, 53, 65, 82, 12, 42, 48, 88, 66, 76, 74, 52, 23, 40,
       49, 60, 44, 20, 37, 71, 17, 45, 77, 50, 63, 43, 39, 57, 56, 47, 24,
       18, 31, 28, 35, 34, 41, 36, 93,  7, 30, 89,  8, 26, 33, 27, 32, 22,
       29,  9,  5,  6], dtype=int64)


fdc['Short Passing'].dtype

dtype('int64')


missing_values = fdc['Short Passing'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Short Passing'].unique()

array([91, 82, 43, 94, 87, 84, 45, 83, 61, 79, 85, 33, 55, 86, 57, 81, 42,
       74, 93, 88, 30, 65, 89, 77, 32, 50, 80, 78, 90, 69, 40, 92, 75, 73,
       34, 76, 35, 70, 37, 23, 44, 38, 48, 26, 60, 25, 46, 28, 24, 36, 51,
       17, 18, 39, 71, 67, 27, 72, 66, 20, 31, 68, 29, 11, 64, 62, 41, 63,
       19, 54, 16, 22, 49, 59, 56, 14, 58, 15, 21, 52, 53, 12, 47, 13,  8,
        7], dtype=int64)


fdc['Volleys'].dtype

dtype('int64')


missing_values = fdc['Volleys'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Volleys'].unique()

array([88, 86, 13, 82, 87, 89, 79, 20, 83, 14, 45, 75, 63, 12, 11, 69, 67,
       56, 18, 85, 62, 70, 32, 40, 47, 81, 44, 84, 78, 76, 90, 49, 42, 64,
       57, 60,  8, 72, 71, 59, 74, 80, 73, 37, 31, 38, 61, 10, 77, 68, 58,
       66, 30, 33, 65, 27, 51, 15, 16, 50, 43, 35, 24, 17, 34, 28,  9, 39,
       52, 46, 22, 19, 53, 55, 48, 54, 23,  5, 41, 25, 21, 36, 26, 29,  6,
        7,  4,  3], dtype=int64)


fdc['Skill'].dtype

dtype('int64')


missing_values = fdc['Skill'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Skill'].unique()

array([470, 414, 109, 441, 448, 407, 406, 138, 394, 144, 363, 391, 369,
       110, 160, 404, 381, 397, 387, 336, 400, 436, 157, 395, 100, 262,
       427, 432, 429, 380, 426, 411, 358, 351, 433, 365, 403,  98, 276,
       386, 383,  99, 413, 115, 341, 375, 143, 359, 309, 435, 330, 325,
       355,  96, 420, 412, 388, 319, 269, 399, 106, 402, 425, 297, 312,
       418, 372, 352, 439, 409, 349, 116, 371, 428, 104, 345, 430, 295,
       405, 440, 422, 252, 401, 417, 396, 233, 377, 251, 382, 368,  84,
       356, 342, 410, 271, 350,  83, 126, 103, 370, 362, 343, 328, 344,
       415, 378, 275, 416, 119, 127, 373, 384,  77, 393, 348, 317, 408,
       376, 300, 220,  89, 107, 334,  72, 390, 419, 305, 289, 398, 281,
       354, 102, 339, 385, 139, 292,  97, 421,  91, 105,  73, 335, 101,
       340, 337, 306, 113, 122, 123, 302, 364, 250, 347, 333, 323, 389,
       361, 322,  86, 367, 258, 392,  92,  90, 310, 331, 338, 121, 260,
        82, 245, 324, 346, 379, 299, 284, 320, 283, 108, 278, 286, 296,
       315, 274,  88, 114, 264, 288,  94, 326, 366, 117, 360, 424,  93,
       318, 124, 125, 327, 249,  75, 332, 303, 374, 239, 272, 357, 353,
       266, 321, 277, 268, 314, 294, 240,  95, 227, 112, 118, 263, 280,
       140, 282,  81, 329, 201,  87, 221, 257, 285, 316, 287, 307, 270,
       256, 313, 311, 228, 247, 254, 130,  80,  85, 232, 293, 298, 301,
       213, 168, 291, 216, 290, 308, 261, 171, 267, 242, 219, 248, 237,
       243, 279, 246, 273,  78, 255, 253, 230,  74, 210, 235, 231, 208,
       259, 304, 241, 199, 224, 206,  61, 129, 222, 223, 141, 149, 131,
       225,  71, 189, 265, 226,  70, 179, 192, 134, 209, 173, 234,  76,
       236, 212,  69, 218, 120, 177, 238, 204, 229, 215, 165, 211, 195,
        64, 202, 194, 190, 193, 203,  67, 214,  79, 205, 244, 196, 111,
       187,  65, 200,  63, 198, 217, 135,  68, 184, 167, 148, 207, 142,
       185, 133, 191, 181, 197,  43,  66, 175, 182,  51, 180, 169, 186,
       137, 188, 176, 132,  60, 178, 147, 163, 183, 162, 152, 170, 172,
       174, 159, 161, 154, 153, 128,  62, 166,  53, 155,  56, 151, 164,
       158,  46, 150,  59,  55,  58, 156, 146,  52, 136,  54,  47,  48,
       145,  40,  57], dtype=int64)


fdc['Dribbling'].dtype

dtype('int64')


missing_values = fdc['Dribbling'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Dribbling'].unique()

array([96, 88, 12, 95, 85, 90, 27, 92, 21, 70, 91, 69, 13, 30, 87, 65, 79,
       83, 23, 80, 18, 93, 77, 63, 76, 16, 59, 81, 11, 84, 10, 75, 78, 55,
       15, 86, 66, 67, 28, 57, 64, 82, 62, 19, 53, 72, 50, 26, 43, 89, 73,
       20, 14, 68, 71, 74, 22, 54, 56, 61,  9, 24, 60, 25,  8, 17, 47, 58,
       46, 42, 51, 52, 49, 44, 35, 48, 39, 29, 40, 45, 34, 31, 33, 38, 41,
       32,  7, 37, 36,  5,  6], dtype=int64)


fdc['Curve'].dtype

dtype('int64')


missing_values = fdc['Curve'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Curve'].unique()

array([93, 81, 13, 85, 88, 79, 83, 19, 18, 60, 76, 63, 14, 74, 77, 49, 15,
       80, 12, 28, 86, 84, 82, 61, 71, 11, 66, 16, 89, 70, 21, 46, 78, 67,
       58, 65, 48, 34, 90, 59, 55, 87, 62,  9, 56, 36, 30, 32, 73, 69, 68,
       75, 45, 10, 72, 64, 41, 23, 47, 20, 51, 25, 44, 17, 54, 57, 53, 33,
       40, 50, 39, 35, 52, 42, 37, 43, 26, 31, 92, 91, 29, 94, 27, 38, 22,
       24,  8,  6,  7,  5,  4], dtype=int64)


fdc['FK Accuracy'].dtype

dtype('int64')


missing_values = fdc['FK Accuracy'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['FK Accuracy'].unique()

array([94, 76, 14, 83, 89, 85, 69, 18, 63, 12, 70, 64, 74, 20, 11, 73, 49,
       61, 88, 68, 28, 79, 84, 48, 67, 38, 87, 53, 65, 15, 31, 78, 82, 10,
       51, 59, 19, 47, 52, 57, 43, 13, 77, 54, 75, 86, 55, 30, 62, 32, 58,
       93,  8, 66, 71, 81, 92, 44, 17, 60, 40, 16, 72, 46, 35, 45, 29, 21,
       56, 80, 24, 22, 39, 42, 26, 41,  9, 37, 27, 50, 33, 25, 36, 91, 34,
       23,  7,  6, 90,  5], dtype=int64)


fdc['Long Passing'].dtype

dtype('int64')


missing_values = fdc['Long Passing'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Long Passing'].unique()

array([91, 77, 40, 93, 81, 70, 75, 44, 63, 86, 71, 84, 35, 59, 73, 83, 64,
       69, 79, 82, 68, 89, 76, 80, 87, 37, 65, 36, 50, 53, 78, 47, 74, 48,
       31, 85, 24, 55, 90, 54, 62, 32, 49, 66, 67, 51, 28, 46, 52, 72, 56,
       41, 45, 22, 88, 61, 33, 12, 60, 17, 27, 29, 23, 38, 16, 58, 34, 25,
       39, 21, 30, 42, 43, 57, 20, 26, 18, 19, 13, 15, 11, 14,  9, 10,  5,
        8], dtype=int64)


fdc['Ball Control'].dtype

dtype('int64')


missing_values = fdc['Ball Control'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Ball Control'].unique()

array([96, 92, 30, 95, 88, 89, 90, 77, 79, 23, 46, 83, 80, 85, 94, 40, 84,
       16, 74, 91, 87, 82, 78, 19, 61, 22, 34, 38, 81, 25, 86, 76, 69, 28,
       93, 75, 35, 60, 63, 73, 18, 71, 15, 21, 72, 14, 65, 20, 24, 27, 70,
       33, 17, 62, 64,  9, 68, 67, 32, 26, 66, 52, 11, 57, 58, 29, 12, 37,
       10, 36, 13, 31, 55, 59, 39, 54, 56, 48, 44, 51, 50, 47, 49, 53,  5,
       42,  8, 45, 43, 41,  7], dtype=int64)


fdc['Movement'].dtype

dtype('int64')


missing_values = fdc['Movement'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Movement'].unique()

array([451, 431, 307, 398, 453, 407, 460, 268, 458, 254, 354, 343, 284,
       286, 388, 378, 424, 464, 420, 399, 437, 322, 367, 272, 328, 448,
       332, 425, 435, 391, 434, 400, 331, 349, 429, 416, 312, 326, 418,
       419, 417, 386, 321, 409, 374, 304, 403, 351, 401, 365, 414, 292,
       323, 299, 433, 350, 348, 413, 320, 281, 427, 353, 364, 410, 428,
       316, 381, 442, 375, 288, 395, 385, 251, 319, 444, 383, 298, 411,
       412, 415, 393, 397, 443, 423, 387, 422, 327, 390, 362, 352, 406,
       277, 361, 421, 396, 384, 450, 338, 363, 359, 287, 297, 430, 382,
       377, 380, 438, 449, 257, 371, 339, 341, 404, 345, 394, 295, 246,
       265, 258, 366, 294, 314, 266, 405, 218, 337, 267, 220, 376, 309,
       283, 426, 347, 244, 240, 291, 340, 250, 305, 290, 317, 334, 355,
       333, 389, 330, 318, 441, 402, 344, 335, 219, 264, 408, 274, 373,
       379, 256, 229, 392, 372, 360, 262, 346, 278, 248, 368, 279, 269,
       336, 342, 236, 370, 243, 315, 249, 227, 329, 239, 369, 223, 282,
       358, 271, 313, 270, 356, 263, 184, 311, 436, 432, 221, 301, 190,
       259, 308, 235, 260, 217, 275, 285, 210, 234, 276, 310, 447, 180,
       446, 300, 303, 209, 247, 252, 231, 357, 226, 238, 280, 440, 237,
       245, 296, 325, 273, 306, 196, 242, 199, 178, 222, 445, 324, 293,
       302, 289, 214, 192, 206, 225, 197, 241, 230, 188, 202, 208, 203,
       216, 213, 224, 439, 212, 232, 253, 228, 189, 204, 205, 207, 198,
       168, 255, 215, 194, 191, 185, 145, 261, 156, 201, 193, 181, 233,
       195, 183, 152, 211, 160, 173, 170, 176, 147, 143, 159, 187, 169,
       200, 165, 163, 177, 179, 167, 139, 162, 175, 155, 166, 172, 174,
       154, 164, 182, 150, 186, 146, 138, 157, 137, 135, 171, 158, 161,
       149, 124, 144, 151, 148, 141, 134, 153, 126, 142, 125, 132, 127,
       140, 133, 130, 131, 136, 122], dtype=int64)


fdc['Acceleration'].dtype

dtype('int64')


missing_values = fdc['Acceleration'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Acceleration'].unique()

array([91, 87, 43, 77, 94, 56, 96, 38, 72, 95, 60, 42, 54, 79, 89, 64, 66,
       51, 73, 57, 80, 86, 85, 78, 40, 82, 76, 65, 68, 90, 48, 46, 88, 70,
       83, 84, 93, 52, 74, 92, 55, 58, 59, 67, 81, 62, 44, 71, 69, 50, 53,
       45, 49, 75, 41, 61, 63, 35, 47, 34, 36, 37, 39, 30, 97, 31, 33, 32,
       27, 28, 26, 29, 25, 17, 19, 24, 15, 23, 21, 20, 22, 16, 18, 13, 14],
      dtype=int64)


fdc['Sprint Speed'].dtype

dtype('int64')


missing_values = fdc['Sprint Speed'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Sprint Speed'].unique()

array([80, 91, 60, 76, 89, 78, 92, 47, 96, 50, 79, 93, 69, 52, 72, 70, 90,
       66, 82, 63, 55, 77, 86, 81, 83, 85, 65, 68, 53, 43, 94, 62, 58, 61,
       87, 64, 67, 54, 88, 75, 95, 73, 49, 84, 56, 44, 74, 51, 57, 46, 59,
       71, 37, 34, 33, 42, 30, 35, 48, 39, 45, 40, 18, 38, 41, 27, 32, 29,
       28, 36, 26, 31, 22, 25, 23, 15, 20, 17, 16, 24, 19, 21, 12, 14],
      dtype=int64)


fdc['Agility'].dtype

dtype('int64')


missing_values = fdc['Agility'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Agility'].unique()

array([91, 87, 67, 78, 96, 77, 40, 92, 37, 61, 93, 51, 79, 84, 94, 82, 60,
       69, 47, 52, 63, 74, 59, 66, 86, 85, 57, 55, 76, 75, 73, 62, 72, 90,
       68, 64, 80, 56, 48, 83, 41, 81, 54, 88, 33, 65, 49, 71, 89, 45, 70,
       43, 50, 32, 42, 39, 58, 36, 34, 53, 46, 95, 44, 38, 21, 29, 35, 31,
       19, 26, 30, 22, 28, 24, 25, 23, 27, 14, 18, 15, 20], dtype=int64)


fdc['Reactions'].dtype

dtype('int64')


missing_values = fdc['Reactions'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Reactions'].unique()

array([94, 95, 88, 91, 93, 92, 86, 89, 87, 84, 90, 83, 85, 82, 81, 79, 80,
       74, 75, 78, 77, 73, 76, 71, 70, 68, 72, 66, 69, 65, 67, 64, 59, 60,
       62, 63, 61, 58, 57, 56, 50, 54, 53, 55, 52, 32, 49, 48, 45, 51, 46,
       47, 37, 34, 44, 40, 38, 43, 41, 35, 42, 33, 39, 31, 36, 30, 24, 29,
       28], dtype=int64)


fdc['Balance'].dtype

dtype('int64')


missing_values = fdc['Balance'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Balance'].unique()

array([95, 71, 49, 76, 83, 82, 91, 37, 43, 53, 86, 66, 45, 35, 69, 94, 92,
       84, 90, 48, 73, 36, 41, 93, 74, 60, 79, 65, 78, 61, 57, 50, 68, 51,
       54, 77, 81, 39, 75, 58, 87, 85, 63, 38, 88, 67, 72, 62, 80, 44, 46,
       42, 55, 40, 70, 32, 89, 52, 59, 47, 64, 27, 56, 30, 31, 25, 34, 29,
       24, 96, 33, 28, 20, 23, 22, 26, 21, 17, 97, 19, 12, 18],
      dtype=int64)


fdc['Power'].dtype

dtype('int64')


missing_values = fdc['Power'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Power'].unique()

array([389, 444, 268, 408, 357, 420, 393, 240, 404, 402, 406, 437, 249,
       284, 400, 403, 358, 381, 382, 273, 424, 264, 316, 361, 355, 328,
       370, 350, 365, 348, 411, 395, 385, 257, 337, 250, 379, 371, 409,
       223, 398, 388, 241, 347, 308, 426, 378, 343, 341, 262, 325, 345,
       359, 399, 421, 396, 315, 253, 368, 336, 340, 366, 387, 369, 375,
       260, 326, 346, 373, 412, 364, 279, 376, 372, 415, 356, 333, 338,
       342, 410, 407, 430, 394, 354, 331, 239, 234, 392, 270, 422, 374,
       360, 391, 300, 335, 242, 327, 215, 397, 321, 390, 339, 383, 265,
       288, 224, 351, 252, 429, 416, 380, 413, 377, 405, 349, 232, 386,
       362, 192, 320, 251, 329, 271, 237, 427, 259, 255, 266, 227, 353,
       258, 243, 263, 291, 302, 306, 332, 363, 256, 247, 301, 287, 322,
       419, 312, 245, 297, 401, 344, 235, 289, 233, 317, 334, 216, 367,
       352, 318, 226, 324, 219, 319, 292, 244, 423, 323, 304, 208, 314,
       313, 193, 299, 303, 311, 229, 211, 225, 309, 330, 238, 305, 220,
       296, 212, 231, 283, 207, 198, 281, 384, 307, 272, 298, 248, 310,
       267, 214, 282, 274, 280, 230, 228, 221, 277, 276, 285, 290, 269,
       246, 294, 293, 195, 236, 295, 217, 189, 275, 201, 278, 194, 206,
       218, 176, 205, 185, 196, 222, 204, 188, 197, 209, 286, 168, 254,
       200, 183, 179, 159, 180, 187, 164, 178, 190, 213, 202, 186, 191,
       261, 210, 203, 173, 199, 169, 152, 181, 175, 184, 182, 170, 160,
       162, 167, 177, 139, 161, 172, 165, 171, 128, 174, 158, 153, 166,
       155, 163, 151, 122, 142, 143, 156, 149, 144, 157, 147, 154, 150,
       134, 140], dtype=int64)


fdc['Shot Power'].dtype

dtype('int64')


missing_values = fdc['Shot Power'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Shot Power'].unique()

array([86, 94, 59, 91, 80, 89, 64, 66, 81, 84, 88, 56, 68, 79, 78, 71, 82,
       70, 55, 76, 61, 83, 51, 52, 90, 87, 62, 72, 77, 74, 50, 57, 58, 85,
       60, 75, 67, 65, 93, 46, 54, 69, 41, 73, 40, 53, 95, 43, 63, 42, 48,
       31, 44, 37, 49, 39, 45, 38, 47, 30, 33, 25, 34, 36, 28, 27, 32, 26,
       35, 23, 22, 29, 20, 24, 21, 18], dtype=int64)


fdc['Jumping'].dtype

dtype('int64')


missing_values = fdc['Jumping'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Jumping'].unique()

array([68, 95, 78, 63, 62, 84, 69, 52, 77, 79, 90, 86, 87, 93, 57, 75, 66,
       82, 56, 32, 51, 76, 72, 81, 74, 71, 67, 65, 73, 64, 70, 80, 85, 37,
       89, 60, 49, 50, 83, 58, 53, 59, 88, 38, 92, 34, 61, 46, 43, 36, 91,
       39, 45, 42, 40, 54, 33, 55, 31, 44, 35, 47, 48, 30, 41, 94, 28, 29,
       27, 24, 19, 26, 17, 15, 22], dtype=int64)


fdc['Stamina'].dtype

dtype('int64')


missing_values = fdc['Stamina'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Stamina'].unique()

array([72, 84, 41, 89, 81, 76, 85, 32, 86, 35, 75, 88, 90, 38, 43, 78, 79,
       96, 95, 70, 82, 77, 93, 94, 87, 39, 54, 80, 45, 83, 69, 65, 73, 91,
       34, 66, 71, 92, 62, 67, 64, 63, 68, 36, 61, 74, 42, 40, 23, 44, 31,
       57, 20, 37, 29, 30, 56, 60, 52, 48, 58, 25, 51, 26, 27, 59, 28, 53,
       33, 49, 97, 55, 50, 46, 24, 21, 22, 15, 47, 17, 19, 16, 18, 14, 12],
      dtype=int64)


fdc['Strength'].dtype

dtype('int64')


missing_values = fdc['Strength'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Strength'].unique()

array([69, 78, 74, 50, 86, 75, 76, 92, 70, 91, 80, 85, 65, 72, 67, 60, 84,
       71, 94, 63, 73, 62, 54, 81, 64, 87, 58, 43, 77, 66, 53, 89, 68, 46,
       44, 61, 79, 88, 59, 83, 55, 34, 82, 95, 56, 37, 90, 57, 93, 49, 39,
       51, 52, 40, 48, 41, 47, 35, 42, 33, 45, 32, 38, 30, 31, 36, 29, 27,
       24, 28, 16, 97, 96, 20, 25, 26, 23], dtype=int64)


fdc['Long Shots'].dtype

dtype('int64')


missing_values = fdc['Long Shots'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Long Shots'].unique()

array([94, 93, 12, 91, 84, 85, 14, 79, 10, 64, 78, 81, 17, 16, 65, 87, 18,
       86, 19, 15, 82, 63, 74, 76, 47, 89, 70, 90, 77, 13, 49, 54, 88, 80,
       53, 58, 51, 73, 66, 75, 83, 30, 46, 35, 71, 61, 72, 69, 43, 48, 62,
       41, 60, 11, 26, 57, 59, 68, 67,  7, 27, 56, 20, 52, 92, 50, 22, 40,
       39, 44, 31, 42,  9,  6, 55, 28, 23, 38, 24, 25, 34, 36, 29,  4,  8,
       45, 33, 37, 21, 32,  5], dtype=int64)


fdc['Mentality'].dtype

dtype('int64')


missing_values = fdc['Mentality'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Mentality'].unique()

array([347, 353, 140, 408, 356, 391, 376, 341, 171, 358, 396, 122, 188,
       363, 414, 332, 386, 379, 348, 172, 382, 123, 294, 378, 313, 371,
       331, 412, 345, 377, 161, 306, 387, 339, 135, 360, 138, 369, 359,
       170, 361, 321, 397, 394, 385, 366, 162, 337, 362, 344, 319, 315,
       144, 336, 340, 373, 398, 324, 300, 338, 384, 139, 364, 372, 134,
       354, 342, 308, 322, 383, 263, 149, 304, 367, 357, 390, 291, 279,
       310, 388, 375, 349, 351, 365, 133, 334, 303, 380, 153, 392, 169,
       318, 350, 352, 401, 302, 325, 346, 132, 399, 281, 335, 403, 307,
       368, 141, 126, 328, 245, 131, 320, 127, 421, 400, 137, 374, 305,
        92, 316, 311, 120, 389, 145, 355, 148, 343, 142, 130, 121, 157,
       329, 323, 115, 150, 298, 154, 317, 295, 100, 301, 326, 327, 197,
       273, 287, 370, 290, 103, 393, 312, 297,  89, 271, 299, 124, 333,
       258, 309, 158, 272, 118, 314, 330, 292, 404, 101, 280, 277, 296,
       248, 285, 278, 109,  93, 146, 286, 284, 288, 105, 152, 111, 160,
       119, 156,  95,  99, 238, 104, 266, 276, 275, 265, 106, 254, 293,
       282, 168, 260, 136, 102, 267, 113, 289,  96, 270, 176, 164, 128,
       268, 283, 244, 182, 243, 240, 116, 264, 112, 274, 261, 114, 269,
       110, 257, 179, 155, 252, 262, 151, 247, 108, 256, 117, 249, 253,
       231, 159, 163,  84, 251,  97,  91,  75, 147, 129, 230, 242, 250,
       259, 125, 381,  77, 175,  82,  88,  90, 165,  83, 195,  87, 246,
       255,  85,  94, 226, 216, 236, 220, 107, 241, 228, 198, 239, 225,
       181, 233, 219, 166, 183,  98, 237, 235,  86, 229, 217, 143, 232,
       209, 234, 224, 206, 227, 222,  80,  78, 186, 221, 173, 214, 187,
        79,  68, 167,  81, 218, 212, 199, 210,  74, 223, 208, 213, 201,
        72, 215, 202, 205, 203, 204, 190,  76, 211, 207, 192,  70, 194,
       196, 189,  66, 193, 200,  67, 191, 184,  71,  64,  65,  69, 177,
        63,  73,  51,  58, 180, 185, 174,  60,  55, 178,  62,  50,  59],
      dtype=int64)


fdc['Aggression'].dtype

dtype('int64')


missing_values = fdc['Aggression'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Aggression'].unique()

array([44, 63, 34, 76, 51, 81, 27, 62, 43, 83, 75, 91, 23, 29, 90, 65, 59,
       89, 48, 38, 25, 87, 54, 60, 73, 74, 69, 85, 70, 86, 32, 40, 31, 77,
       84, 80, 78, 79, 71, 56, 42, 30, 61, 58, 28, 82, 46, 52, 36, 92, 55,
       35, 67, 37, 72, 57, 50, 64, 39, 47, 20, 68, 15, 66, 33, 93, 88, 22,
       24, 45, 17, 18, 26, 21, 11, 41, 53, 19, 12, 49, 94, 16, 95, 13, 14,
       96, 10,  9], dtype=int64)


fdc['Interceptions'].dtype

dtype('int64')


missing_values = fdc['Interceptions'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Interceptions'].unique()

array([40, 29, 19, 66, 36, 49, 55, 11, 38, 22, 90, 35, 87, 15, 30, 39, 88,
       24, 91, 82, 42, 27, 41, 79, 74, 58, 20, 85, 48, 83, 64, 21, 50, 81,
       78, 28, 86, 26, 34, 52, 37, 80, 25, 56, 23, 47, 45, 77, 84, 44, 53,
       18, 46, 72, 61, 89, 54, 63, 65, 73, 16, 32, 76, 59, 13, 70, 31, 69,
       33, 17, 75, 68, 60, 51, 71, 12, 57, 10, 43, 67, 14,  9, 62,  8,  7,
        6,  4,  5,  3], dtype=int64)


fdc['Positioning'].dtype

dtype('int64')


missing_values = fdc['Positioning'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Positioning'].unique()

array([93, 95, 11, 88, 87, 94, 91, 13, 47, 92, 72, 12, 90, 73, 80, 85, 20,
       35, 76, 89, 83, 77, 54, 70, 86, 16, 28, 14, 84, 78, 10, 75, 52, 71,
       81, 64, 56, 15, 82, 79, 44, 30, 59,  7, 68, 38, 48, 67, 24, 26, 34,
       69, 74, 32, 66, 62, 65, 51, 18, 31,  9, 25, 49, 55, 63, 27, 61, 17,
       39, 58, 29, 50, 40, 19,  8, 42, 60, 57, 37, 45, 43, 53,  5,  4, 36,
        6, 46, 41, 23, 22, 33, 21,  3,  2], dtype=int64)


fdc['Vision'].dtype

dtype('int64')


missing_values = fdc['Vision'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Vision'].unique()

array([95, 82, 65, 94, 90, 79, 84, 66, 80, 70, 85, 44, 87, 71, 83, 41, 52,
       86, 68, 50, 77, 48, 88, 30, 61, 74, 59, 73, 72, 64, 91, 78, 63, 57,
       89, 62, 56, 69, 42, 67, 27, 76, 81, 55, 75, 60, 49, 45, 58, 22, 53,
       46, 25, 43, 51, 40, 93, 33, 31, 34, 35, 39, 47, 21, 32, 28, 37, 36,
       38, 54, 24, 23, 14, 11, 15, 26, 19, 18, 12, 20, 17, 10, 29, 13, 16,
        9], dtype=int64)


fdc['Penalties'].dtype

dtype('int64')


missing_values = fdc['Penalties'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Penalties'].unique()

array([75, 84, 11, 92, 88, 83, 23, 70, 25, 62, 71, 66, 27, 47, 69, 54, 44,
       86, 17, 90, 33, 87, 73, 60, 55, 68, 91, 72, 50, 78, 18, 82, 40, 29,
       45, 43, 64, 24, 59, 46, 56, 81, 67, 49, 61, 74, 58, 63, 79, 38, 80,
       32, 20, 76, 77, 41, 19, 26, 85, 21, 52, 34, 53, 65, 57, 16, 42, 89,
       15, 13, 14, 22, 51, 37,  9, 48, 12, 31, 36, 39, 10, 30, 35, 28,  8,
        7,  6], dtype=int64)


fdc['Composure'].dtype

dtype('int64')


missing_values = fdc['Composure'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Composure'].unique()

array([96, 95, 68, 91, 93, 88, 90, 65, 84, 70, 66, 80, 85, 69, 82, 89, 81,
       87, 83, 86, 67, 92, 94, 57, 78, 79, 75, 45, 61, 76, 58, 62, 77, 74,
       59, 55, 48, 40, 64, 73, 39, 71, 72, 63, 60, 52, 53, 56, 44, 54, 41,
       32, 49, 46, 31, 51, 50, 25, 18, 38, 30, 24, 21, 36, 33, 26, 23, 47,
       22, 28, 34, 35, 37, 43, 27, 12, 42, 17, 29, 13, 19, 14, 16, 20, 15],
      dtype=int64)


fdc['Defending'].dtype

dtype('int64')


missing_values = fdc['Defending'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Defending'].unique()

array([ 91,  84,  57, 186,  94,  96, 122,  50, 100,  48, 272, 259,  54,
        38,  89, 263,  83, 147, 264, 245, 120,  52, 130, 267, 205, 162,
       105, 241, 148, 248, 266, 194, 258, 117, 166,  56, 249,  92,  45,
       214, 140,  99, 150,  59, 251, 262, 243, 195, 160,  40, 114, 236,
       244, 231,  80, 123, 253, 132, 103, 257, 261,  98,  78, 209, 229,
       230,  60, 101, 206, 242, 138,  61, 256, 171, 260, 226, 224,  44,
       131, 113, 240,  77, 232, 225, 109, 228, 247,  93, 121, 238, 111,
       128, 188, 173, 250, 255,  41, 144, 239, 217, 106, 165, 246, 235,
       126, 118, 203, 234, 135, 215, 175, 192, 108,  39,  33, 151, 156,
       174,  47, 216, 237, 102, 227, 161, 233,  67, 213,  75, 212,  36,
       254, 196,  88,  81, 134,  53, 155, 223,  43, 125,  46,  51, 137,
        71,  95,  35, 208, 110, 170,  87, 107,  55, 204, 177,  69, 152,
       163,  37, 181, 252, 159, 133, 124, 207,  82,  97,  65,  42,  79,
       104, 211, 129,  49, 157, 153, 185, 189, 146,  86, 112,  73, 127,
        31, 220, 164, 191, 219, 139,  64, 183,  66, 197,  90, 218,  34,
        72, 221, 222, 142,  63, 136, 179,  85, 169, 180,  74, 210,  62,
       187, 145, 198, 184, 199,  32,  30,  58, 172, 178, 116, 176,  70,
       202, 141, 115, 193, 149,  29, 201, 167, 168, 182, 119, 190, 200,
        76, 143, 158, 154,  68,  28,  27,  25,  24,  26,  23,  20,  21],
      dtype=int64)


fdc['Marking'].dtype

dtype('int64')


missing_values = fdc['Marking'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Marking'].unique()

array([32, 28, 27, 68, 35, 38, 15, 34, 25, 93, 42, 84, 20, 17, 47, 85, 30,
       89, 82, 29, 56, 91, 72, 59, 79, 49, 83, 86, 50, 60, 94, 41, 57, 78,
       63, 88, 90,  9, 58, 74, 39, 92, 45, 36, 44, 87, 70, 76, 53, 80, 67,
       77, 12, 48, 55, 75, 81, 11, 64, 69, 14, 24, 52, 65, 19, 31, 13, 10,
       66, 71, 54, 46, 22, 40, 18, 51, 37, 43, 61, 26, 73, 21,  7, 33, 62,
       16, 23,  8,  6,  5,  4,  3], dtype=int64)


fdc['Standing Tackle'].dtype

dtype('int64')


missing_values = fdc['Standing Tackle'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Standing Tackle'].unique()

array([35, 32, 12, 65, 30, 42, 43, 19, 34, 13, 93, 88, 18, 10, 24, 29, 53,
       90, 84, 48, 15, 36, 89, 27, 73, 54, 41, 83, 59, 67, 87, 64, 14, 55,
       75, 45, 33, 57, 21, 82, 50, 86, 80, 79, 31, 46, 85, 40, 44, 56, 20,
       70, 76, 81, 71, 16, 68, 37, 38, 78, 39, 77, 11, 74, 28, 49, 47, 72,
       61, 51, 22, 17, 52, 63, 23, 60, 25, 26,  9, 62, 58, 66, 69,  7,  8,
        6,  5], dtype=int64)


fdc['Sliding Tackle'].dtype

dtype('int64')


missing_values = fdc['Sliding Tackle'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Sliding Tackle'].unique()

array([24, 18, 53, 29, 19, 41, 16, 32, 10, 86, 38, 87, 11, 90, 47, 85, 79,
       40,  8, 13, 22, 60, 49, 81, 88, 55, 33, 42, 14, 80, 36, 12, 52, 71,
       46, 83, 65, 84, 34, 82, 77, 78, 74, 20, 43, 35, 69, 70, 30, 68, 45,
       57, 44, 21, 75, 26, 51, 76, 39, 48, 28, 63, 59, 66, 72, 17, 67, 64,
       31, 25, 15, 54, 58, 62, 56, 23, 37, 73, 50, 27,  9, 61,  7,  6,  4],
      dtype=int64)


fdc['Goalkeeping'].dtype

dtype('int64')


missing_values = fdc['Goalkeeping'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Goalkeeping'].unique()

array([ 54,  58, 437,  56,  59,  51,  62, 439,  42,  67, 420, 440,  41,
        46,  63,  60,  26, 435, 424,  43,  45,  52,  50,  47,  53,  44,
       418,  15,  48, 416, 153, 413,  65,  64,  20, 423,  49,  55,  66,
        40,  57, 421,  13,  39,  61, 419,  21, 409,  37, 406, 410,  36,
       408,  34,  29, 405, 403, 402, 407,  16,  69, 391, 401, 398, 400,
        22,  68, 396,  38,  78,  73, 399, 390, 393, 395, 397,  80,  70,
       389, 394, 388,  27,  30,  75,  71, 386,  74, 378, 385, 384, 380,
       392, 381,  10, 387, 383, 375, 382,  19, 379,  24, 369, 356, 368,
       373, 370, 372,  72, 374, 376, 364,  25, 367,  17, 377, 371, 365,
       352, 362, 359, 363, 366,  82,  35, 361, 358,  76, 294,  83, 357,
       360, 355, 354,  77, 229, 350, 353, 347, 351,  32, 349, 169, 346,
       348, 343, 345, 339, 342,  33, 341,  28, 119, 337, 338, 340, 344,
       335,  98, 324, 248, 334, 298, 336, 328, 331, 321, 332,  81,  79,
       333, 278, 329, 261, 325,  31, 327, 330, 322, 305, 326, 283, 320,
       323, 318,  18, 319, 316, 317, 272, 315,  88, 311, 310, 314, 313,
       307, 312, 309, 308, 301, 304, 292, 303, 306, 296, 289, 300, 302,
       297, 290, 299, 293, 295, 291, 288,  93, 284, 287, 286, 285, 273,
       282, 279, 281, 280, 277, 275, 276, 274, 270, 271, 268, 269, 267,
       260, 265, 262, 266, 263, 264, 251, 259, 254, 257, 252, 255, 256,
       258, 247, 250, 243, 253, 249, 245, 236, 246, 234, 241, 231],
      dtype=int64)


fdc['GK Diving'].dtype

dtype('int64')


missing_values = fdc['GK Diving'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['GK Diving'].unique()

array([ 6,  7, 87, 15,  9, 14, 86, 13, 88, 10, 84, 11,  8,  5, 12, 90,  3,
       27, 89, 80, 16, 85,  2, 82, 79, 83,  4, 81, 77, 18, 78, 17, 75, 74,
       76, 73, 71, 72, 52, 68, 70, 54, 69, 32, 66, 65, 67, 61, 22, 64, 23,
       40, 63, 55, 19, 50, 62, 58, 60, 59, 56, 57, 53, 51, 49, 46, 48, 47,
       45], dtype=int64)


fdc['GK Handling'].dtype

dtype('int64')


missing_values = fdc['GK Handling'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['GK Handling'].unique()

array([11, 92, 13,  9,  6, 14, 88,  5, 85, 10, 89, 87,  8, 15, 12,  4, 82,
       81,  3,  7, 25, 86, 83,  2, 80, 16, 77, 79, 78, 76, 84, 75, 72, 74,
       71, 69, 73, 70, 67, 68, 65, 61, 62, 64, 41, 63, 66, 33, 22, 17, 57,
       18, 54, 55, 59, 49, 19, 40, 60, 58, 43, 45, 53, 47, 56, 51, 52, 50,
       48, 46], dtype=int64)


fdc['GK Kicking'].dtype

dtype('int64')


missing_values = fdc['GK Kicking'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['GK Kicking'].unique()

array([15, 78,  5, 12,  9, 85,  7, 88, 13, 16, 74, 91,  6, 10,  4, 93, 11,
       73, 14, 75,  2, 31, 68, 76,  8, 80, 82,  3, 87, 72, 83, 77, 79, 81,
       69, 71, 20, 67, 70, 64, 65, 63, 44, 60, 84, 54, 48, 61, 18, 66, 17,
       59, 62, 90, 43, 38, 58, 57, 28, 40, 53, 23, 47, 46, 19, 51, 55, 52,
       56, 22, 30, 25, 42, 35, 21, 49, 50, 36, 45], dtype=int64)


fdc['GK Positioning'].dtype

dtype('int64')


missing_values = fdc['GK Positioning'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['GK Positioning'].unique()

array([14, 90, 10, 15,  8, 11, 91, 88,  7, 12, 85, 86,  5, 89, 13,  6, 82,
        4,  9, 87, 33, 84, 16, 83,  2,  3, 79, 81, 80, 76, 78, 19, 77, 17,
       75, 74, 73, 71, 18, 72, 70, 69, 66, 68, 40, 64, 20, 32, 67, 62, 65,
       63, 24, 23, 50, 55, 58, 51, 59, 56, 61, 57, 60, 46, 54, 53, 52, 47,
       49, 48, 43, 45, 42, 38, 44, 41], dtype=int64)


fdc['GK Reflexes'].dtype

dtype('int64')


missing_values = fdc['GK Reflexes'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['GK Reflexes'].unique()

array([ 8, 11, 90, 13, 10, 14, 89,  6, 12, 88,  7,  9, 15,  5,  3, 37, 85,
       86,  4, 16, 82, 83, 84, 87, 78, 80, 20, 18, 79, 81, 19, 77, 17,  2,
       74, 71, 76, 73, 75, 72, 69, 46, 66, 51, 70, 34, 67, 23, 68, 45, 65,
       21, 59, 54, 47, 61, 64, 63, 62, 60, 58, 56, 57, 55, 53, 50, 52, 49,
       48, 44], dtype=int64)


fdc['Total Stats'].dtype

dtype('int64')


missing_values = fdc['Total Stats'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Total Stats'].unique()

array([2231, 2221, 1413, ...,  757,  747,  956], dtype=int64)


fdc['Base Stats'].dtype

dtype('int64')


missing_values = fdc['Base Stats'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['Base Stats'].unique()

array([466, 464, 489, 485, 451, 457, 470, 490, 484, 455, 469, 463, 468,
       497, 442, 439, 473, 452, 498, 449, 477, 401, 446, 447, 465, 430,
       461, 422, 476, 460, 453, 467, 471, 399, 424, 441, 459, 438, 437,
       454, 428, 445, 431, 474, 421, 435, 448, 475, 403, 444, 443, 419,
       405, 420, 423, 396, 388, 482, 478, 385, 394, 480, 433, 450, 462,
       456, 436, 434, 429, 400, 440, 425, 410, 458, 398, 413, 373, 406,
       408, 472, 426, 407, 432, 427, 415, 481, 417, 372, 380, 418, 383,
       414, 409, 412, 411, 386, 362, 402, 390, 404, 391, 416, 375, 389,
       361, 397, 366, 392, 393, 382, 368, 387, 352, 376, 384, 378, 379,
       341, 354, 369, 395, 357, 381, 377, 344, 360, 370, 338, 333, 367,
       363, 349, 355, 345, 358, 348, 374, 351, 343, 342, 353, 321, 350,
       365, 364, 371, 327, 331, 359, 347, 356, 339, 319, 317, 335, 346,
       329, 315, 324, 322, 325, 332, 336, 337, 330, 316, 313, 306, 307,
       328, 310, 340, 308, 318, 334, 301, 289, 302, 320, 323, 326, 311,
       297, 314, 304, 292, 305, 312, 294, 287, 300, 299, 285, 303, 288,
       278, 296, 277, 309, 291, 283, 286, 293, 295, 298, 276, 282, 272,
       284, 290, 271, 275, 279, 281, 262, 263, 280, 268, 270, 269, 264,
       273, 265, 252, 267, 257, 274, 266, 259, 247, 261, 251, 233, 239,
       253, 258, 254, 260, 244, 240, 255, 256, 250, 238, 243, 249, 248,
       245, 241, 232], dtype=int64)


fdc['W/F'].dtype

dtype('O')


fdc['W/F'].unique()

array(['4 ★', '3 ★', '5 ★', '2 ★', '1 ★'], dtype=object)


# If we want to do some calculations we need to remove the stars
fdc['W/F'] = fdc['W/F'].str.replace('★', '')
fdc['W/F'].unique()

array(['4 ', '3 ', '5 ', '2 ', '1 '], dtype=object)


# Remove the whitespace
fdc['W/F'] = fdc['W/F'].str.strip()
fdc['W/F'].unique()

array(['4', '3', '5', '2', '1'], dtype=object)


fdc['SM'].dtype

dtype('O')


fdc['SM'].unique()

array(['4★', '5★', '1★', '2★', '3★'], dtype=object)


# Replace the stars
fdc['SM'] = fdc['SM'].str.replace('★','').astype(int)
fdc['SM'].dtype

dtype('int32')


# Using a function to iterate the rows and replace the numbers with stars
def replace_num(value):
    if value == 5:
        return '★★★★★'
    elif value == 4:
        return '★★★★'
    elif value == 3:
        return '★★★'
    elif value == 2:
        return'★★'
    else:
        return '★'
        
fdc['SM'] = fdc['SM'].apply(replace_num)
fdc['SM'] = fdc['SM'].str.strip()
fdc['SM'].head(10)

0     ★★★★
1    ★★★★★
2        ★
3     ★★★★
4    ★★★★★
5     ★★★★
6     ★★★★
7        ★
8    ★★★★★
9        ★
Name: SM, dtype: object


fdc['A/W'].dtype

dtype('O')


fdc['A/W'].unique()

array(['Medium', 'High', 'Low'], dtype=object)


fdc['A/W'] = fdc['A/W'].str.strip()


missing_values = fdc['A/W'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['D/W'].dtype

dtype('O')


fdc['D/W'].unique()

array(['Low', 'Medium', 'High'], dtype=object)


fdc['D/W'] = fdc['D/W'].str.strip()
fdc['D/W'].head(10)

0       Low
1       Low
2    Medium
3      High
4    Medium
5    Medium
6    Medium
7    Medium
8       Low
9    Medium
Name: D/W, dtype: object


missing_values = fdc['D/W'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['IR'].dtype

dtype('O')


fdc['IR'].unique()

array(['5 ★', '3 ★', '4 ★', '2 ★', '1 ★'], dtype=object)


fdc['IR'] = fdc['IR'].str.replace('★', '')
fdc['IR'] = fdc['IR'].str.strip()
fdc['IR'].unique()

array(['5', '3', '4', '2', '1'], dtype=object)


missing_values = fdc['PAC'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['PAC'].unique()

array([85, 89, 87, 76, 91, 78, 93, 86, 96, 88, 94, 65, 84, 74, 71, 77, 68,
       75, 54, 79, 83, 80, 81, 82, 63, 67, 90, 66, 42, 73, 70, 64, 57, 58,
       69, 72, 50, 59, 92, 60, 62, 55, 52, 56, 61, 53, 45, 37, 95, 43, 44,
       46, 48, 49, 47, 34, 39, 40, 51, 41, 36, 32, 33, 30, 31, 38, 35, 28,
       29, 25], dtype=int64)


missing_values = fdc['SHO'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['SHO'].unique()

array([92, 93, 86, 85, 91, 88, 60, 73, 89, 87, 70, 90, 81, 66, 72, 82, 28,
       74, 77, 62, 50, 83, 69, 80, 46, 76, 54, 49, 61, 58, 79, 68, 59, 41,
       45, 64, 78, 55, 75, 65, 63, 48, 42, 56, 51, 30, 47, 84, 40, 57, 25,
       71, 37, 43, 53, 67, 38, 52, 39, 35, 36, 44, 32, 34, 33, 31, 27, 22,
       29, 26, 23, 18, 24, 20, 16, 21, 19, 17], dtype=int64)


fdc['PAS'].unique()

array([91, 81, 78, 93, 86, 85, 88, 71, 80, 76, 74, 77, 79, 84, 73, 55, 83,
       87, 72, 75, 58, 89, 82, 68, 67, 64, 66, 59, 69, 90, 65, 53, 63, 62,
       70, 56, 42, 54, 61, 57, 60, 48, 52, 47, 46, 44, 45, 50, 51, 49, 43,
       36, 38, 40, 41, 35, 39, 34, 33, 37, 30, 32, 29, 31, 26, 28, 25, 27],
      dtype=int64)


missing_values = fdc['PAS'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['DRI'].unique()

array([95, 89, 90, 88, 94, 85, 91, 71, 72, 86, 73, 81, 84, 92, 80, 68, 77,
       87, 60, 83, 78, 64, 67, 79, 69, 66, 65, 70, 82, 75, 61, 74, 54, 76,
       49, 63, 59, 62, 56, 55, 50, 57, 58, 52, 53, 51, 48, 47, 46, 39, 44,
       43, 36, 40, 45, 41, 37, 34, 35, 42, 32, 38, 31, 33, 30, 29, 28, 25,
       27], dtype=int64)


missing_values = fdc['DRI'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['DEF'].unique()

array([38, 35, 52, 64, 36, 43, 45, 51, 39, 91, 44, 86, 48, 57, 40, 88, 33,
       81, 63, 47, 53, 89, 71, 37, 80, 68, 85, 61, 90, 83, 49, 56, 58, 82,
       87, 79, 66, 55, 78, 32, 50, 76, 77, 70, 75, 41, 29, 73, 65, 59, 84,
       54, 72, 46, 42, 69, 34, 31, 30, 74, 24, 62, 25, 20, 26, 60, 27, 23,
       28, 67, 22, 19, 18, 21, 17, 15, 16, 12], dtype=int64)


missing_values = fdc['DEF'].isnull().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


fdc['PHY'].unique()

array([65, 77, 90, 78, 59, 82, 75, 91, 76, 88, 86, 85, 73, 67, 79, 63, 83,
       89, 66, 69, 72, 64, 71, 81, 87, 68, 84, 80, 55, 70, 44, 62, 51, 57,
       60, 58, 56, 74, 52, 61, 53, 45, 50, 54, 47, 48, 49, 42, 37, 40, 39,
       43, 38, 46, 41, 34, 35, 36, 31, 32, 33, 29, 28], dtype=int64)


missing_values = fdc['PHY'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  0


cols_to_check = ['SHO', 'PAS', 'DRI', 'DEF', 'PHY']

unique_values = fdc[cols_to_check].astype(str).apply(lambda x: ' '.join(x), axis=1).unique()

unique_values

array(['92 91 95 38 65', '93 81 89 35 77', '92 78 90 52 90', ...,
       '39 44 46 40 53', '49 41 49 30 44', '22 39 42 45 55'], dtype=object)


cols_to_check = ['SHO', 'PAS', 'DRI', 'DEF', 'PHY']

null_values = fdc[cols_to_check].astype(str).apply(lambda x: ' '.join(x), axis=1).isnull().sum()

print('Number of null values are: ', null_values)

Number of null values are:  0


fdc['Hits'].dtype

dtype('O')


fdc['Hits'].unique()

array(['771', '562', '150', '207', '595', '248', '246', '120', '1.6K',
       '130', '321', '189', '175', '96', '118', '216', '212', '154',
       '205', '202', '339', '408', '103', '332', '86', '173', '161',
       '396', '1.1K', '433', '242', '206', '177', '1.5K', '198', '459',
       '117', '119', '209', '84', '187', '165', '203', '65', '336', '126',
       '313', '124', '145', '538', '182', '101', '45', '377', '99', '194',
       '403', '414', '593', '374', '245', '3.2K', '266', '299', '309',
       '215', '265', '211', '112', '337', '70', '159', '688', '116', '63',
       '144', '123', '71', '224', '113', '168', '61', '89', '137', '278',
       '75', '148', '176', '197', '264', '214', '247', '402', '440',
       '1.7K', '2.3K', '171', '320', '657', '87', '259', '200', '255',
       '253', '196', '60', '97', '85', '169', '256', '132', '239', '166',
       '121', '109', '32', '46', '122', '48', '527', '199', '282', '51',
       '1.9K', '642', '155', '323', '288', '497', '509', '79', '49',
       '270', '511', '80', '128', '115', '156', '204', '143', '140',
       '152', '220', '134', '225', '94', '74', '135', '142', '50', '77',
       '40', '107', '193', '179', '34', '64', '453', '57', '81', '28',
       '78', '133', '43', '425', '88', '42', '36', '233', '376', '210',
       '444', '100', '263', '98', '29', '160', '39', '257', '6', '310',
       '138', '62', '293', '285', '362', '66', '69', '58', '21', '20',
       '131', '38', '406', '68', '108', '110', '93', '512', '443', '306',
       '352', '422', '585', '346', '178', '841', '76', '394', '72', '172',
       '44', '407', '230', '367', '295', '157', '243', '56', '111', '326',
       '679', '18', '92', '59', '25', '184', '53', '12', '90', '55', '73',
       '11', '566', '180', '83', '262', '17', '26', '31', '280', '359',
       '213', '297', '387', '480', '381', '677', '486', '8', '244', '129',
       '388', '275', '319', '2K', '52', '91', '421', '153', '27', '41',
       '222', '35', '102', '23', '30', '33', '146', '13', '19', '14',
       '106', '276', '568', '353', '47', '478', '249', '254', '369',
       '219', '565', '237', '227', '434', '375', '162', '605', '654', '3',
       '7', '9', '104', '114', '186', '446', '756', '22', '139', '500',
       '67', '147', '149', '16', '82', '54', '37', '15', '1.3K', '3K',
       '952', '5', '749', '541', '330', '393', '517', '770', '409', '170',
       '125', '283', '342', '363', '580', '105', '217', '24', '141', '10',
       '427', '158', '426', '4', '666', '181', '324', '979', '1.4K',
       '302', '751', '298', '411', '944', '2', '947', '292', '349', '621',
       '1', '2.8K', '338', '287', '261', '218', '1.8K', '240', '279',
       '229', '188', '315', '664', '613', '190', '706', '127', '462',
       '386', '695', '491', '167', '281', '250', '307', '95', '231',
       '174', '680', '633', '221', '348', '602', '183', '653', '195',
       '164', '151', '258', '8.4K', '343', '419', '655', '136', '399',
       '531', '357', '228', '385', '312', '340', '238', '487', '355',
       '499', '4.3K', '296', '515', '943', '1.2K', '903', '335', '191',
       '594', '267', '617', '516', '504', '331', '652', '410', '550',
       '473', '442', '344', '208', '1K', '2.5K', '273', '485', '826',
       '192', '405', '941', '477', '644', '303', '417', '6K', nan, 11.0,
       2.0, 1.0, 31.0, 3.0, 10.0, 9.0, 17.0, 7.0, 4.0, 6.0], dtype=object)


missing_values = fdc['Hits'].isna().sum()
print('Number of missing values: ', missing_values)

Number of missing values:  2595


fdc['Hits'].fillna(0, inplace=True)
fdc['Hits'].sample(5)

11750    2
14087    2
17601    0
16785    0
13875    1
Name: Hits, dtype: object


fdc['Hits'].unique()

array(['771', '562', '150', '207', '595', '248', '246', '120', '1.6K',
       '130', '321', '189', '175', '96', '118', '216', '212', '154',
       '205', '202', '339', '408', '103', '332', '86', '173', '161',
       '396', '1.1K', '433', '242', '206', '177', '1.5K', '198', '459',
       '117', '119', '209', '84', '187', '165', '203', '65', '336', '126',
       '313', '124', '145', '538', '182', '101', '45', '377', '99', '194',
       '403', '414', '593', '374', '245', '3.2K', '266', '299', '309',
       '215', '265', '211', '112', '337', '70', '159', '688', '116', '63',
       '144', '123', '71', '224', '113', '168', '61', '89', '137', '278',
       '75', '148', '176', '197', '264', '214', '247', '402', '440',
       '1.7K', '2.3K', '171', '320', '657', '87', '259', '200', '255',
       '253', '196', '60', '97', '85', '169', '256', '132', '239', '166',
       '121', '109', '32', '46', '122', '48', '527', '199', '282', '51',
       '1.9K', '642', '155', '323', '288', '497', '509', '79', '49',
       '270', '511', '80', '128', '115', '156', '204', '143', '140',
       '152', '220', '134', '225', '94', '74', '135', '142', '50', '77',
       '40', '107', '193', '179', '34', '64', '453', '57', '81', '28',
       '78', '133', '43', '425', '88', '42', '36', '233', '376', '210',
       '444', '100', '263', '98', '29', '160', '39', '257', '6', '310',
       '138', '62', '293', '285', '362', '66', '69', '58', '21', '20',
       '131', '38', '406', '68', '108', '110', '93', '512', '443', '306',
       '352', '422', '585', '346', '178', '841', '76', '394', '72', '172',
       '44', '407', '230', '367', '295', '157', '243', '56', '111', '326',
       '679', '18', '92', '59', '25', '184', '53', '12', '90', '55', '73',
       '11', '566', '180', '83', '262', '17', '26', '31', '280', '359',
       '213', '297', '387', '480', '381', '677', '486', '8', '244', '129',
       '388', '275', '319', '2K', '52', '91', '421', '153', '27', '41',
       '222', '35', '102', '23', '30', '33', '146', '13', '19', '14',
       '106', '276', '568', '353', '47', '478', '249', '254', '369',
       '219', '565', '237', '227', '434', '375', '162', '605', '654', '3',
       '7', '9', '104', '114', '186', '446', '756', '22', '139', '500',
       '67', '147', '149', '16', '82', '54', '37', '15', '1.3K', '3K',
       '952', '5', '749', '541', '330', '393', '517', '770', '409', '170',
       '125', '283', '342', '363', '580', '105', '217', '24', '141', '10',
       '427', '158', '426', '4', '666', '181', '324', '979', '1.4K',
       '302', '751', '298', '411', '944', '2', '947', '292', '349', '621',
       '1', '2.8K', '338', '287', '261', '218', '1.8K', '240', '279',
       '229', '188', '315', '664', '613', '190', '706', '127', '462',
       '386', '695', '491', '167', '281', '250', '307', '95', '231',
       '174', '680', '633', '221', '348', '602', '183', '653', '195',
       '164', '151', '258', '8.4K', '343', '419', '655', '136', '399',
       '531', '357', '228', '385', '312', '340', '238', '487', '355',
       '499', '4.3K', '296', '515', '943', '1.2K', '903', '335', '191',
       '594', '267', '617', '516', '504', '331', '652', '410', '550',
       '473', '442', '344', '208', '1K', '2.5K', '273', '485', '826',
       '192', '405', '941', '477', '644', '303', '417', '6K', 0, 11.0,
       2.0, 1.0, 31.0, 3.0, 10.0, 9.0, 17.0, 7.0, 4.0, 6.0], dtype=object)


# Convert the values
fdc['Hits'] = fdc['Hits'].replace({'K': '*1e3'}, regex=True).map(pd.eval).astype(int)
fdc['Hits'].head(10)

0     771
1     562
2     150
3     207
4     595
5     248
6     246
7     120
8    1600
9     130
Name: Hits, dtype: int32


fdc.columns

Index(['ID', 'Name', 'Surname', 'Nationality', 'Age', 'Overall', 'Potential',
       'Club', 'Contract', 'Contract Start', 'Contract End',
       'Contract Length(years)', 'Contract Status', 'Positions', 'Height(cm)',
       'Weight(kg)', 'Preferred Foot', 'BOV', 'Best Position', 'Joined',
       'Loan Date End', 'Market Price(€)', 'Wage', 'Release Clause(€)',
       'Attacking', 'Crossing', 'Finishing', 'Heading Accuracy',
       'Short Passing', 'Volleys', 'Skill', 'Dribbling', 'Curve',
       'FK Accuracy', 'Long Passing', 'Ball Control', 'Movement',
       'Acceleration', 'Sprint Speed', 'Agility', 'Reactions', 'Balance',
       'Power', 'Shot Power', 'Jumping', 'Stamina', 'Strength', 'Long Shots',
       'Mentality', 'Aggression', 'Interceptions', 'Positioning', 'Vision',
       'Penalties', 'Composure', 'Defending', 'Marking', 'Standing Tackle',
       'Sliding Tackle', 'Goalkeeping', 'GK Diving', 'GK Handling',
       'GK Kicking', 'GK Positioning', 'GK Reflexes', 'Total Stats',
       'Base Stats', 'W/F', 'SM', 'A/W', 'D/W', 'IR', 'PAC', 'SHO', 'PAS',
       'DRI', 'DEF', 'PHY', 'Hits'],
      dtype='object')


# Export the data to an Excel file
fdc.to_excel('fifa21_clean_data.xlsx', sheet_name="fifa21_data_analysis", index=False)


# Export the data to a .csv file
fdc.to_csv('fifa21_clean_data.csv', index=False)


f21 = fdc.copy()
f21.sample(5)


# Deleting the columns
f21 = f21.drop(['Potential','Contract','BOV','Best Position'], axis=1)
f21.columns

Index(['ID', 'Name', 'Surname', 'Nationality', 'Age', 'Overall', 'Club',
       'Contract Start', 'Contract End', 'Contract Length(years)',
       'Contract Status', 'Positions', 'Height(cm)', 'Weight(kg)',
       'Preferred Foot', 'Joined', 'Loan Date End', 'Market Price(€)', 'Wage',
       'Release Clause(€)', 'Attacking', 'Crossing', 'Finishing',
       'Heading Accuracy', 'Short Passing', 'Volleys', 'Skill', 'Dribbling',
       'Curve', 'FK Accuracy', 'Long Passing', 'Ball Control', 'Movement',
       'Acceleration', 'Sprint Speed', 'Agility', 'Reactions', 'Balance',
       'Power', 'Shot Power', 'Jumping', 'Stamina', 'Strength', 'Long Shots',
       'Mentality', 'Aggression', 'Interceptions', 'Positioning', 'Vision',
       'Penalties', 'Composure', 'Defending', 'Marking', 'Standing Tackle',
       'Sliding Tackle', 'Goalkeeping', 'GK Diving', 'GK Handling',
       'GK Kicking', 'GK Positioning', 'GK Reflexes', 'Total Stats',
       'Base Stats', 'W/F', 'SM', 'A/W', 'D/W', 'IR', 'PAC', 'SHO', 'PAS',
       'DRI', 'DEF', 'PHY', 'Hits'],
      dtype='object')


# Me are going to use the .describe() method
f21.describe()


# Count the values from the column
f21_pf = f21['Preferred Foot'].value_counts()
print(f21_pf)

# Creating the pie chart with the data
explode = [0.0, 0.1]
f21['Preferred Foot'].value_counts().plot(
    kind='pie', 
    explode=explode, 
    title="Player's preferred foot", 
    autopct='%1.1f%%', 
    shadow=True, 
    startangle=90, 
    ylabel="")

Right    14445
Left      4534
Name: Preferred Foot, dtype: int64

<Axes: title={'center': "Player's preferred foot"}>


# Count the values of the column
f21_cs = f21['Contract Status'].value_counts()
print(f21_cs)

# Creating the bar plot
bars = plt.bar(f21_cs.index, f21_cs.values, color=['blue', 'orange', 'green'])

# Adding total values at the top of each bar
for i, v in enumerate(f21_cs):
    plt.text(i, v + 100, str(v), ha='center', va='bottom', fontsize=10)

# Customize labels and title
plt.xlabel("Contract categories")
plt.ylabel("Player's count")
plt.title("Counts of Contracts Status")
plt.xticks(rotation=45)

plt.show()

Contract    17729
On Loan      1013
Free          237
Name: Contract Status, dtype: int64


# The data is group by clubs and sum their player's salaries
f21_csum = f21.groupby('Club')['Wage'].sum().sort_values()
print(f21_csum)

# Because there are a lot of clubs, I decided to apply a filter
ylab = f21_csum[f21_csum >= 1250000]

# Now let's create a barh chart to visualize our data
plt.barh(ylab.index, ylab.values, color="red")
plt.xlabel('Total Salary')
plt.ylabel('Clubs')
plt.title("Total Salaries for Clubs (>= 1250000)")

plt.show()

Club
No Club                         0
Llaneros de Guanare         10000
Central Coast Mariners      10350
Aragua FC                   10500
Waterford FC                10650
                           ...   
Manchester United         2986000
Liverpool                 3028500
Manchester City           3639000
FC Barcelona              4083000
Real Madrid               4687000
Name: Wage, Length: 682, dtype: int32


f21_ov = f21[(f21['Overall'] >= 85)]

list_ov = f21_ov[['Name', 'Surname', 'Overall']].sort_values(by='Overall', ascending=False)
print(list_ov)

          Name                    Surname  Overall
0       Lionel                      Messi       93
1    Cristiano  Ronaldo dos Santos Aveiro       92
2          Jan                      Oblak       91
3        Kevin                  De Bruyne       91
4       Neymar        da Silva Santos Jr.       91
..         ...                        ...      ...
73     Clément                    Lenglet       85
74  Marquinhos                Aoás Corrêa       85
75       Riyad                     Mahrez       85
76     Ricardo            Barbosa Pereira       85
98       Marco                       Reus       85

[99 rows x 3 columns]


f21_count = f21['Nationality'].value_counts()
f21_count

England                1705
Germany                1195
Spain                  1065
France                 1003
Argentina               943
                       ... 
Malawi                    1
Rwanda                    1
São Tomé & Príncipe       1
Aruba                     1
Indonesia                 1
Name: Nationality, Length: 164, dtype: int64


# Let's see the list of countries with 100 players or fewer
f21_less_than_100 = f21_count[f21_count <= 100].index.tolist()
print(f21_less_than_100)

['Greece', 'Northern Ireland', 'Cameroon', 'Morocco', 'Russia', 'Canada', 'South Africa', 'Bosnia Herzegovina', 'Ukraine', 'Slovakia', 'DR Congo', 'Finland', 'Mali', 'Iceland', 'Slovenia', 'Algeria', 'Albania', 'Kosovo', 'New Zealand', 'Hungary', 'Bulgaria', 'Tunisia', 'Egypt', 'India', 'Costa Rica', 'Montenegro', 'Guinea', 'Cape Verde', 'United Arab Emirates', 'Jamaica', 'North Macedonia', 'Gambia', 'Georgia', 'Burkina Faso', 'Israel', 'Iran', 'Guinea Bissau', 'Angola', 'Gabon', 'Honduras', 'Congo', 'Togo', 'Zimbabwe', 'Comoros', 'Panama', 'Moldova', 'Luxembourg', 'Benin', 'Haiti', 'Curacao', 'Zambia', 'Kenya', 'Lithuania', 'Sierra Leone', 'Madagascar', 'Uganda', 'Cyprus', 'Guyana', 'Uzbekistan', 'Mauritania', 'Latvia', 'Burundi', 'Kazakhstan', 'Azerbaijan', 'Equatorial Guinea', 'Dominican Republic', 'Trinidad & Tobago', 'Faroe Islands', 'Cuba', 'Estonia', 'Mozambique', 'Liechtenstein', 'Libya', 'Iraq', 'El Salvador', 'Niger', 'Antigua & Barbuda', 'Syria', 'Grenada', 'Liberia', 'Armenia', 'Thailand', 'Sudan', 'Montserrat', 'Jordan', 'Belarus', 'Lebanon', 'Philippines', 'Central African Republic', 'Namibia', 'Belize', 'Ethiopia', 'South Sudan', 'Palestine', 'Hong Kong', 'Eritrea', 'Afghanistan', 'Chinese Taipei', 'Saint Kitts and Nevis', 'Guatemala', 'Malaysia', 'Nicaragua', 'Chad', 'Singapore', 'Tanzania', 'Macau', 'Barbados', 'Korea DPR', 'Malta', 'Andorra', 'Guam', 'Bermuda', 'New Caledonia', 'Puerto Rico', 'Papua New Guinea', 'Saint Lucia', 'Malawi', 'Rwanda', 'São Tomé & Príncipe', 'Aruba', 'Indonesia']


# Make a copy of the column
new_f21_count = f21['Nationality'].copy()
print(new_f21_count)

0        Argentina
1         Portugal
2         Slovenia
3          Belgium
4           Brazil
           ...    
18974     China PR
18975      England
18976      England
18977     China PR
18978     China PR
Name: Nationality, Length: 18979, dtype: object


# Replace the countries with 100 or fewer players
new_f21_count.loc[new_f21_count.isin(f21_less_than_100)] = 'Others'
print(new_f21_count)

0        Argentina
1         Portugal
2           Others
3          Belgium
4           Brazil
           ...    
18974     China PR
18975      England
18976      England
18977     China PR
18978     China PR
Name: Nationality, Length: 18979, dtype: object


# Count the new values
new_count = new_f21_count.value_counts(ascending=True)
new_count

Czech Republic          107
Ivory Coast             107
Ghana                   115
Wales                   127
Nigeria                 129
Serbia                  129
Senegal                 129
Croatia                 134
Bolivia                 148
Peru                    164
Chile                   189
Venezuela               199
Switzerland             211
Australia               238
Paraguay                239
Ecuador                 249
Scotland                285
Belgium                 295
Denmark                 300
Turkey                  317
Austria                 319
Romania                 321
Saudi Arabia            330
Colombia                339
Republic of Ireland     340
Korea Republic          340
Uruguay                 348
Poland                  354
Portugal                361
Norway                  362
Mexico                  362
China PR                364
United States           376
Sweden                  380
Italy                   387
Netherlands             438
Japan                   485
Brazil                  887
Argentina               943
France                 1003
Spain                  1065
Germany                1195
England                1705
Others                 2164
Name: Nationality, dtype: int64


# Creating our chart barh
colors = ['red' if index != 'Others' else 'blue' for index in new_count.index]
ax = new_count.plot(kind='barh', figsize=(10,20), color=colors)
plt.xlabel('Count')
plt.ylabel('Nationality')
plt.title('Player Count by Nationality')
for i, v in enumerate(new_count):
    ax.text(v +10, i, str(v), color='black', va='center')

	ID	Age	Overall	Contract Length(years)	Height(cm)	Weight(kg)	Market Price(€)	Wage	Release Clause(€)	Attacking	...	GK Reflexes	Total Stats	Base Stats	PAC	SHO	PAS	DRI	DEF	PHY	Hits
count	18979.000000	18979.000000	18979.000000	18979.000000	18979.000000	18979.000000	1.897900e+04	18979.000000	1.897900e+04	18979.000000	...	18979.000000	18979.000000	18979.000000	18979.000000	18979.000000	18979.000000	18979.000000	18979.000000	18979.000000	18979.000000
mean	226403.384794	25.194109	65.718636	3.491965	181.200221	75.019021	2.865063e+06	9092.062279	3.962951e+06	248.938142	...	16.519627	1595.286949	355.702197	67.453975	53.457031	57.681016	62.875020	49.866221	64.368934	22.936720
std	27141.054157	4.710520	6.968999	2.401495	6.840054	7.073542	7.685154e+06	19707.021089	9.772762e+06	74.299428	...	17.854079	269.874789	40.761117	10.677859	13.827425	10.081857	9.927415	16.443213	9.601883	119.861937
min	41.000000	16.000000	47.000000	0.000000	155.000000	50.000000	0.000000e+00	0.000000	0.000000e+00	42.000000	...	2.000000	747.000000	232.000000	25.000000	16.000000	25.000000	25.000000	12.000000	28.000000	0.000000
25%	210135.000000	21.000000	61.000000	2.000000	176.000000	70.000000	4.750000e+05	1000.000000	4.235000e+05	222.000000	...	8.000000	1452.000000	327.000000	61.000000	44.000000	51.000000	57.000000	35.000000	58.000000	1.000000
50%	232418.000000	25.000000	66.000000	3.000000	181.000000	75.000000	9.500000e+05	3000.000000	1.000000e+06	263.000000	...	11.000000	1627.000000	356.000000	68.000000	56.000000	58.000000	64.000000	53.000000	65.000000	4.000000
75%	246922.500000	29.000000	70.000000	5.000000	186.000000	80.000000	2.000000e+06	8000.000000	2.800000e+06	297.000000	...	14.000000	1781.000000	384.000000	75.000000	64.000000	64.000000	69.000000	63.000000	71.000000	12.000000
max	259216.000000	53.000000	93.000000	23.000000	206.000000	110.000000	1.855000e+08	560000.000000	2.031000e+08	437.000000	...	90.000000	2316.000000	498.000000	96.000000	93.000000	93.000000	95.000000	91.000000	91.000000	8400.000000

	ID	Name	LongName	photoUrl	playerUrl	Nationality	Age	↓OVA	POT	Club	...	A/W	D/W	IR	PAC	SHO	PAS	DRI	DEF	PHY	Hits
0	158023	L. Messi	Lionel Messi	https://cdn.sofifa.com/players/158/023/21_60.png	http://sofifa.com/player/158023/lionel-messi/2...	Argentina	33	93	93	\n\n\n\nFC Barcelona	...	Medium	Low	5 ★	85	92	91	95	38	65	771
1	20801	Cristiano Ronaldo	C. Ronaldo dos Santos Aveiro	https://cdn.sofifa.com/players/020/801/21_60.png	http://sofifa.com/player/20801/c-ronaldo-dos-s...	Portugal	35	92	92	\n\n\n\nJuventus	...	High	Low	5 ★	89	93	81	89	35	77	562
2	200389	J. Oblak	Jan Oblak	https://cdn.sofifa.com/players/200/389/21_60.png	http://sofifa.com/player/200389/jan-oblak/210006/	Slovenia	27	91	93	\n\n\n\nAtlético Madrid	...	Medium	Medium	3 ★	87	92	78	90	52	90	150
3	192985	K. De Bruyne	Kevin De Bruyne	https://cdn.sofifa.com/players/192/985/21_60.png	http://sofifa.com/player/192985/kevin-de-bruyn...	Belgium	29	91	91	\n\n\n\nManchester City	...	High	High	4 ★	76	86	93	88	64	78	207
4	190871	Neymar Jr	Neymar da Silva Santos Jr.	https://cdn.sofifa.com/players/190/871/21_60.png	http://sofifa.com/player/190871/neymar-da-silv...	Brazil	28	91	91	\n\n\n\nParis Saint-Germain	...	High	Medium	5 ★	91	85	86	94	36	59	595
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18974	247223	Xia Ao	Ao Xia	https://cdn.sofifa.com/players/247/223/21_60.png	http://sofifa.com/player/247223/ao-xia/210006/	China PR	21	47	55	\n\n\n\nWuhan Zall	...	Medium	Medium	1 ★	64	28	26	38	48	51	NaN
18975	258760	B. Hough	Ben Hough	https://cdn.sofifa.com/players/258/760/21_60.png	http://sofifa.com/player/258760/ben-hough/210006/	England	17	47	67	\n\n\n\nOldham Athletic	...	Medium	Medium	1 ★	64	40	48	49	35	45	NaN
18976	252757	R. McKinley	Ronan McKinley	https://cdn.sofifa.com/players/252/757/21_60.png	http://sofifa.com/player/252757/ronan-mckinley...	England	18	47	65	\n\n\n\nDerry City	...	Medium	Medium	1 ★	63	39	44	46	40	53	NaN
18977	243790	Wang Zhen'ao	Zhen'ao Wang	https://cdn.sofifa.com/players/243/790/21_60.png	http://sofifa.com/player/243790/zhenao-wang/21...	China PR	20	47	57	\n\n\n\nDalian YiFang FC	...	Medium	Medium	1 ★	58	49	41	49	30	44	NaN
18978	252520	Zhou Xiao	Xiao Zhou	https://cdn.sofifa.com/players/252/520/21_60.png	http://sofifa.com/player/252520/xiao-zhou/210006/	China PR	21	47	57	\n\n\n\nDalian YiFang FC	...	Medium	Medium	1 ★	62	22	39	42	45	55	NaN

	Name	Surname
0	Lionel	Messi
1	Cristiano	Ronaldo dos Santos Aveiro
2	Jan	Oblak
3	Kevin	De Bruyne
4	Neymar	da Silva Santos Jr.
5	Robert	Lewandowski
6	Mohamed	Salah
7	Alisson	Ramses Becker
8	Kylian	Mbappé
9	Marc-André	ter Stegen

	Contract	Contract Start	Contract End	Contract Length(years)
0	2004 ~ 2021	2004	2021	17.0
1	2018 ~ 2022	2018	2022	4.0
2	2014 ~ 2023	2014	2023	9.0
3	2015 ~ 2023	2015	2023	8.0
4	2017 ~ 2022	2017	2022	5.0

	Contract	Contract Start	Contract End	Contract Length(years)	Contract Status
8515	Jun 30, 2021 On Loan	NaN	NaN	0.0	On Loan
7857	2020 ~ 2024	2020	2024	4.0	Contract
18063	2019 ~ 2021	2019	2021	2.0	Contract
17428	2018 ~ 2020	2018	2020	2.0	Contract
2662	2020 ~ 2021	2020	2021	1.0	Contract

	Contract	Contract Status	Loan Date End
205	Jun 30, 2021 On Loan	On Loan	30-Jun-21
248	Jun 30, 2021 On Loan	On Loan	30-Jun-21
254	Jun 30, 2021 On Loan	On Loan	30-Jun-21
302	Jun 30, 2021 On Loan	On Loan	30-Jun-21
306	Jun 30, 2021 On Loan	On Loan	30-Jun-21
...	...	...	...
18472	Aug 31, 2021 On Loan	On Loan	31-Aug-21
18571	Jun 30, 2021 On Loan	On Loan	30-Jun-21
18600	Dec 31, 2020 On Loan	On Loan	31-Dec-20
18622	Dec 31, 2020 On Loan	On Loan	31-Dec-20
18680	Dec 31, 2020 On Loan	On Loan	31-Dec-20

	ID	Name	Surname	Nationality	Age	Overall	Potential	Club	Contract	Contract Start	...	A/W	D/W	IR	PAC	SHO	PAS	DRI	DEF	PHY	Hits
4487	245992	Billy	Gilmour	Scotland	19	71	86	Chelsea	2018 ~ 2023	2018	...	Medium	Medium	1	68	54	70	73	64	50	419
18752	258681	Ryan	Hillier	Wales	17	50	65	Newport County	2020 ~ 2021	2020	...	Medium	Medium	1	68	49	40	49	16	45	0
1560	216335	Yuriy	Gazinskiy	Russia	30	75	75	No Club	Free	NaN	...	Medium	Medium	1	66	55	66	68	72	72	9
15929	256145	Felipe	Zenobio	Argentina	20	59	72	Club Atlético Tigre	2019 ~ 2024	2019	...	Medium	Medium	1	57	59	57	60	31	56	1
276	239231	Cucurella	Cucurella Saseta	Spain	21	81	89	Getafe CF	2020 ~ 2023	2020	...	High	Medium	1	81	67	78	80	73	74	443

Data cleaning project - FIFA 2021 Database¶

1. Import Libraries¶

2. Read the dataset¶

3. Data cleaning¶

3.1 Copy our data¶

3.2 Name and Lastname¶

3.3 Photo and Player¶

3.4 Nationality¶

3.5 Age¶

3.6 OVA and POT¶

3.6.1 OVA¶

3.6.2 POT¶

3.6.3 Changing the column's names¶

3.7 Club¶

3.8 Contract¶

3.8.1 Searching for the values 'On Loan' and 'Free'¶

3.8.2 Extracting the values into new columns¶

3.9 Positions¶

3.10 Height¶

3.10.1 Rename Column¶

3.11 Weight¶

3.11.1 Rename Column¶

3.12 Preferred Foot¶

3.13 BOV¶

3.14 Best Position¶

3.16 Joined¶

3.17 Loan Date End¶

3.18 Value¶

3.18.1 Transform 'Value' Column¶

3.18.2 Change column name¶

3.19 Wage¶

3.20 Release Clause¶

3.20.1 Transform the values¶

3.21 Stats¶

3.21.1 Attacking¶

3.21.2 Crossing¶

3.21.3 Finishing¶

3.21.4 Heading Accuracy¶

3.21.5 Short Passing¶

3.21.6 Volleys¶

3.21.7 Skill¶

3.21.8 Dribbling¶

3.21.9 Curve¶

3.21.10 FK Accuracy¶

3.21.11Long Passing¶

3.21.12 Ball Control¶

3.21.13 Movement¶

3.21.14 Acceleration¶

3.21.15 Sprint Speed¶

3.21.16 Agility¶

3.21.17 Reactions¶

3.21.18 Balance¶

3.21.19 Power¶

3.21.20 Shot Power¶

3.21.21 Jumping¶

3.21.22 Stamina¶

3.21.23 Strength¶

3.21.24 Long Shots¶

3.21.25 Mentality¶

3.21.26 Aggression¶

3.21.27 Interceptions¶

3.21.28 Positioning¶

3.21.29 Vision¶

3.21.30 Penalties¶

3.21.31 Composure¶

3.21.32 Defending¶

3.21.33 Marking¶

3.21.34 Standing Tackle¶

3.21.35 Sliding Tackle¶

Goalkeeper Stats¶

3.21.36 Goalkeeping¶

3.21.37 GK Diving¶

3.21.38 GK Handling¶

3.21.39 GK Kicking¶

3.21.40 GK Positioning¶

3.21.41 GK Reflexes¶

Player´s Total Stats¶

3.21.42 Total Stats¶

3.21.43 Base Stats¶

3.22 W/F (Weak foot)¶