Introduction

Fifa is one of the most played console games in the world. Fifa 21 is a series of this. Fifa 21 is a soccer game.FIFA 21 is a football simulation video game published by Electronic Arts as part of the FIFA series. It is the 28th installment in the FIFA series, and was released on 9 October 2020 for Microsoft Windows, Nintendo Switch, PlayStation 4 and Xbox One. Enhanced versions for the PlayStation 5 and Xbox Series X and Series S were released on 3 December 2020, in addition to a version for Stadia. I performed Exploratory Data Analysis using the Fifa 21 data set. Later, I made visualizations using matplotlib & seaborn libraries.

Content :

  1. Importing our libraries for EDA
  2. Load And Check Data
  3. Variable Description
  4. Subtract 2021 from Date Of Birth to get Age
  5. Dropping columns in our Dataset with Pandas
  6. Checking for the Shape of Dataset in Rows and Columns
  7. Unique Features in Each Column

Importing our libraries for EDA

In [1]:
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import matplotlib.pyplot as plt #visualize
plt.style.use("seaborn-whitegrid")

import seaborn as sns #visualize

from collections import Counter

import warnings # don't show warnings
warnings.filterwarnings("ignore")
from wordcloud import WordCloud
%matplotlib inline

Load and Check Data

In [4]:
#Reading Our Dataset with Pandas Library
data_fifa = pd.read_csv("players.csv")
data_fifa.sample(8)
Out[4]:
int_player_id str_player_name str_positions dt_date_of_birth int_height int_weight int_overall_rating int_potential_rating str_best_position int_best_overall_rating ... int_international_reputations str_work_rate str_body_type int_dribbling int_curve int_fk_accuracy int_long_passing int_ball_control str_player_speciality str_trait
11973 11974 Thijmen Joel Sander Nijhuis GK 7/25/1998 196 83 64 70 GK 64 ... 1 Medium/ Medium Lean (185+) 11 14 12 23 14 NaN NaN
112 113 Nicolás Alejandro Tagliafico LB 8/31/1992 172 65 84 84 LB 84 ... 3 High/ High Normal (170-185) 73 68 40 67 77 NaN ['Solid Player', 'Power Header', 'Team Player']
4893 4894 Nana Opoku Ampomah LM, ST 1/2/1996 175 68 70 74 RM 71 ... 1 High/ Medium Normal (170-185) 76 59 34 53 73 NaN ['Long Shot Taker (AI)', 'Speed Dribbler (AI)']
8826 8826 Terrence Anthony Boyd ST 2/16/1991 188 82 66 66 ST 66 ... 1 Medium/ Low Normal (185+) 56 42 34 33 62 NaN NaN
60 61 David Josué Jiménez Silva CAM, CM 1/8/1986 173 67 86 86 CAM 86 ... 4 High/ Medium Normal (170-185) 86 82 77 84 91 ['Dribbler', 'Playmaker\xa0', 'Complete Midfie... ['Finesse Shot', 'Flair', 'Playmaker (AI)', 'O...
16426 16427 Milan Obradović CB 12/27/1999 189 81 58 70 CB 60 ... 1 Medium/ High Normal (185+) 50 42 33 43 42 NaN NaN
12221 12222 Manuel Schwenk LM, LW, CAM 3/7/1992 178 77 63 63 LM 63 ... 1 Medium/ Medium Normal (170-185) 66 50 41 54 64 NaN ['Injury Prone']
3443 3444 Saúl Savin Salcedo Zárate CB 8/29/1997 183 75 72 83 CB 74 ... 1 Medium/ High Normal (170-185) 47 33 29 48 54 NaN NaN

8 rows × 56 columns

In [8]:
#Viweing the columns available in our Dataset
data_fifa.columns
Out[8]:
Index(['int_player_id', 'str_player_name', 'str_positions', 'dt_date_of_birth',
       'int_height', 'int_weight', 'int_overall_rating',
       'int_potential_rating', 'str_best_position', 'int_best_overall_rating',
       'int_value', 'int_wage', 'int_team_id', 'str_nationality',
       'int_crossing', 'int_finishing', 'int_heading_accuracy',
       'int_short_passing', 'int_volleys', 'int_defensive_awareness',
       'int_standing_tackle', 'int_sliding_tackle', 'int_diving',
       'int_handling', 'int_kicking', 'int_gk_positioning', 'int_reflexes',
       'int_aggression', 'int_interceptions', 'int_positioning', 'int_vision',
       'int_penalties', 'int_composure', 'int_acceleration',
       'int_sprint_speed', 'int_agility', 'int_reactions', 'int_balance',
       'int_shot_power', 'int_jumping', 'int_stamina', 'int_strength',
       'int_long_shots', 'str_preferred_foot', 'int_weak_foot',
       'int_skill_moves', 'int_international_reputations', 'str_work_rate',
       'str_body_type', 'int_dribbling', 'int_curve', 'int_fk_accuracy',
       'int_long_passing', 'int_ball_control', 'str_player_speciality',
       'str_trait'],
      dtype='object')

Variable Description

  1. ID : unique id number to each footballer
  2. Name : name of footballer
  3. Age : age of footballer
  4. Photo : photo of footballer
  5. Nationality : the nationality of the player
  6. Overall : in-game power
  7. Potential : the potential of the football player
  8. Clup: football player's club
  9. Value : value of the player
  10. Wage : wages paid by the player
  11. Special : special
  12. Preferred Foot : foot used by the footballer(left,Right)
  13. International Reputation : the international reputation of the football player
  14. Weak Foot : weak foot of the footballer
  15. Skill Moves : football player's skills moves
  16. Work Rate : football player's work rate
  17. Body Type : body type of the football player
  18. Real Face : real face of the player(false,true)
  19. Position : position played by the football player
  20. Jersey Number : jersey number of the football player
  21. Joined : Joined
  22. Loaned From : is the football player for loaned from
  23. Contract Valid Until : the expiry date of the player contract
  24. Height : footballer's height
  25. Weight : footballer's weight
  26. Crossing : long cross pass by the footballer
  27. Finishing : football player finishing
  28. HeadingAccuracy : HeadingAccuracy
  29. ShortPassing : ShortPassing
  30. Dribbling : player's dribbling speed
  31. Curve : spin on the ball
  32. LongPassing : football player's long pass
  33. BallControl : football player control the ball
  34. Acceleration : the speed of the football player
  35. SprintSpeed : the sprintSpeed of the player
  36. Agility : the agility of the football player
  37. Reactions : the reaction of the footballer
  38. Balance : football player's balance
  39. ShotPower : football player's shotpower
  40. Jumping : the footballer's jumping capacity
  41. Stamina : the footballer's stamina
  42. Strength : the strength of the football player
  43. LongShots : footballer's longest shot
  44. Aggression : football player's aggression
  45. Positioning : the position of the football player in the football field
  46. Vision : football player vision
  47. Penalties : footballer's penalties
  48. Composure : the calmness of the football player on the field
  49. Marking : marking
  50. StandingTackle : the fight of the football player
  51. SlidingTackle : slide intervention
  52. GKDiving : diving
  53. GKHandling : handling
  54. GKKicking : kicking
  55. GKPositioning : Positioning
  56. GKReflexes : reflexes
  57. Release Clause : the player's release clause

    Football Player Position : LS, ST, RS, LW, LF, CF, RF, RW, LAM, CAM, RAM, LM, LCM, CM, RCM, RM, LWB, LDM, CDM, RDM, RWB, LB, LCB, BC, RCB, RB.

Check for Missing Value In Dataset

In [9]:
#Viweing the columns available in our Dataset
data_fifa.columns


#Checking for columns with missing values
data_fifa.columns[data_fifa.isnull().any()] 
Out[9]:
Index(['int_team_id', 'str_player_speciality', 'str_trait'], dtype='object')

Rename Columns

In [11]:
# Renaming the Columns in Dataset in a suitable Pythonic way
data_fifa = data_fifa.rename(columns = {'str_player_name': 'PlayerName', 'str_positions': 'Positions',
                                       'dt_date_of_birth': 'D.O.B', 'int_height': 'PlayerHeight',
                                       'int_weight': 'PlayerWeight', 'int_overall_rating': 'OverallRating',
                                       'int_potential_rating': 'PotentialRating', 'str_best_position': 'BestPositions',
                                       'int_best_overall_rating': 'BestOverallRating', 'int_value': 'PlayerValue',
                                       'int_wage': 'Wage', 'str_nationality': 'Nationality',
                                       'int_crossing': 'Crossing', 'int_finishing': 'FinishingAccuracy',
                                       'int_heading_accuracy': 'HeadingAccuracy', 'int_short_passing': 'ShortPassing',
                                       'int_volleys': 'Volleys', 'int_defensive_awareness': 'DefensiveAwareness',
                                       'int_standing_tackle': 'StandingTackle', 'int_sliding_tackle': 'SlidingTackle',
                                       'int_diving': 'Diving', 'int_handling': 'Handling',
                                       'int_kicking': 'Kicking', 'int_gk_positioning': 'GkPositioning',
                                       'int_reflexes': 'Reflexes', 'int_aggression': 'Aggression',
                                       'int_interceptions': 'Interceptions', 'int_positioning': 'Positioning',
                                       'int_vision': 'Vision', 'int_penalties': 'Penalties',
                                       'int_composure': 'Composure', 'int_acceleration': 'Acceleration',
                                       'int_sprint_speed': 'SprintSpeed', 'int_agility': 'Agility',
                                       'int_reactions': 'Reactions', 'int_balance': 'Balance',
                                       'int_shot_power': 'ShotPower', 'int_jumping': 'JumpingPower',
                                       'int_stamina': 'Stamina', 'int_strength': 'Strength',
                                       'int_long_shots': 'LongShots', 'str_preferred_foot': 'PreferredFoot',
                                        'int_weak_foot': 'WeakFoot', 'int_skill_moves': 'SkillMoves',
                                       'int_international_reputations': 'InternationalReputations', 'str_work_rate': 'WorkRate',
                                       'str_body_type': 'BodyType', 'int_dribbling': 'Dribbling',
                                       'int_curve': 'Curve', 'int_fk_accuracy': 'FreekickAccuracy',
                                       'int_long_passing': 'LongPassing', 'int_ball_control': 'BallControl'}, inplace = False)

Covert Datetime(D.O.B) Column to Numerical Data

In [12]:
#Converting Our Datatime column to Numerical Column @year e.g 1996 and @Month e.g 11th month
data_fifa['D.O.B'] = pd.to_datetime(data_fifa['D.O.B'])
data_fifa['year']= data_fifa['D.O.B'].dt.year
data_fifa['month']= data_fifa['D.O.B'].dt.month

Subtract 2021 from Date Of Birth to get Age

In [13]:
#Subtract 2021 from Date Of Birth to get Age
today = pd.to_datetime('2021-03-10')
data_fifa['age'] = today.year - data_fifa['D.O.B'].dt.year

Dropping columns in our Dataset with Pandas

In [14]:
#Dropping columns in our Dataset with Pandas
columns = ['int_team_id', 'str_player_speciality', 'str_trait','int_player_id','D.O.B','year','Positions','OverallRating']
data_fifa = data_fifa.drop(columns, axis=1, inplace=False )
data_fifa.sample(4).T
Out[14]:
2982 14918 9178 1242
PlayerName Nuno Miguel da Costa Jóia Miguel Angel Vargas Mañan Javier Alván Salas Salazar John Brooks
PlayerHeight 182 183 183 193
PlayerWeight 70 81 73 78
PotentialRating 73 68 67 78
BestPositions CF GK CM CB
BestOverallRating 74 60 67 78
PlayerValue 3000000 475000 1000000 8000000
Wage 43000 500 3000 37000
Nationality Cape Verde Chile Mexico United States
Crossing 63 18 58 36
FinishingAccuracy 74 19 51 39
HeadingAccuracy 74 18 47 82
ShortPassing 72 38 72 72
Volleys 61 15 54 32
DefensiveAwareness 15 16 55 75
StandingTackle 17 22 61 78
SlidingTackle 20 18 58 75
Diving 14 59 12 8
Handling 15 57 10 7
Kicking 7 58 16 10
GkPositioning 12 58 11 9
Reflexes 6 62 11 10
Aggression 48 37 73 75
Interceptions 37 26 64 76
Positioning 76 19 60 40
Vision 67 29 64 57
Penalties 69 25 59 45
Composure 64 46 56 81
Acceleration 77 48 66 63
SprintSpeed 78 51 63 79
Agility 79 38 65 56
Reactions 68 61 62 73
Balance 74 28 61 51
ShotPower 73 44 64 50
JumpingPower 81 62 43 72
Stamina 65 42 72 61
Strength 62 71 73 83
LongShots 68 20 55 29
PreferredFoot Right Right Right Left
WeakFoot 3 2 3 3
SkillMoves 3 1 3 2
InternationalReputations 1 1 1 2
WorkRate Medium/ Medium Medium/ Medium Medium/ Medium Medium/ High
BodyType Lean (170-185) Normal (170-185) Lean (170-185) Normal (185+)
Dribbling 76 22 64 65
Curve 52 19 54 30
FreekickAccuracy 46 20 42 28
LongPassing 54 31 72 74
BallControl 78 21 68 64
month 2 6 8 1
age 30 25 28 28
In [15]:
data_fifa['BodyType'].value_counts()
Out[15]:
Normal (170-185)    6535
Lean (170-185)      4116
Normal (185+)       4056
Lean (185+)         1933
Normal (170-)        681
Stocky (170-185)     626
Lean (170-)          462
Stocky (185+)        373
Unique               119
Stocky (170-)        101
Name: BodyType, dtype: int64

Checking for the Shape of Dataset in Rows and Columns

In [9]:
#Checking for the Shape of Dataset in Rows and Columns
data_fifa.shape
Out[9]:
(19002, 51)

Data types in our dataset

In [10]:
#Checking for the number of Categorical and Numerical Data
data_fifa.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 19002 entries, 0 to 19001
Data columns (total 51 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   PlayerName                19002 non-null  object
 1   PlayerHeight              19002 non-null  int64 
 2   PlayerWeight              19002 non-null  int64 
 3   PotentialRating           19002 non-null  int64 
 4   BestPositions             19002 non-null  object
 5   BestOverallRating         19002 non-null  int64 
 6   PlayerValue               19002 non-null  int64 
 7   Wage                      19002 non-null  int64 
 8   Nationality               19002 non-null  object
 9   Crossing                  19002 non-null  int64 
 10  FinishingAccuracy         19002 non-null  int64 
 11  HeadingAccuracy           19002 non-null  int64 
 12  ShortPassing              19002 non-null  int64 
 13  Volleys                   19002 non-null  int64 
 14  DefensiveAwareness        19002 non-null  int64 
 15  StandingTackle            19002 non-null  int64 
 16  SlidingTackle             19002 non-null  int64 
 17  Diving                    19002 non-null  int64 
 18  Handling                  19002 non-null  int64 
 19  Kicking                   19002 non-null  int64 
 20  GkPositioning             19002 non-null  int64 
 21  Reflexes                  19002 non-null  int64 
 22  Aggression                19002 non-null  int64 
 23  Interceptions             19002 non-null  int64 
 24  Positioning               19002 non-null  int64 
 25  Vision                    19002 non-null  int64 
 26  Penalties                 19002 non-null  int64 
 27  Composure                 19002 non-null  int64 
 28  Acceleration              19002 non-null  int64 
 29  SprintSpeed               19002 non-null  int64 
 30  Agility                   19002 non-null  int64 
 31  Reactions                 19002 non-null  int64 
 32  Balance                   19002 non-null  int64 
 33  ShotPower                 19002 non-null  int64 
 34  JumpingPower              19002 non-null  int64 
 35  Stamina                   19002 non-null  int64 
 36  Strength                  19002 non-null  int64 
 37  LongShots                 19002 non-null  int64 
 38  PreferredFoot             19002 non-null  object
 39  WeakFoot                  19002 non-null  int64 
 40  SkillMoves                19002 non-null  int64 
 41  InternationalReputations  19002 non-null  int64 
 42  WorkRate                  19002 non-null  object
 43  BodyType                  19002 non-null  object
 44  Dribbling                 19002 non-null  int64 
 45  Curve                     19002 non-null  int64 
 46  FreekickAccuracy          19002 non-null  int64 
 47  LongPassing               19002 non-null  int64 
 48  BallControl               19002 non-null  int64 
 49  month                     19002 non-null  int64 
 50  age                       19002 non-null  int64 
dtypes: int64(45), object(6)
memory usage: 7.4+ MB

Statistical Analysis of our Dataset

In [17]:
#Giving A Statistical Analysis of our Dataset
data_fifa.describe().apply(lambda s: s.apply(lambda x: format(x, 'f')))
Out[17]:
PlayerHeight PlayerWeight PotentialRating BestOverallRating PlayerValue Wage Crossing FinishingAccuracy HeadingAccuracy ShortPassing ... WeakFoot SkillMoves InternationalReputations Dribbling Curve FreekickAccuracy LongPassing BallControl month age
count 19002.000000 19002.000000 19002.000000 19002.000000 19002.000000 19002.000000 19002.000000 19002.000000 19002.000000 19002.000000 ... 19002.000000 19002.000000 19002.000000 19002.000000 19002.000000 19002.000000 19002.000000 19002.000000 19002.000000 19002.000000
mean 181.225660 75.046311 71.148932 66.784286 2891449.005368 9113.167035 49.690454 45.877329 51.970056 58.808494 ... 2.942217 2.365540 1.090569 55.607462 47.271603 42.394432 52.779971 58.552416 5.836123 26.592464
std 6.847472 7.078378 6.115352 6.733926 7733189.305884 19735.324238 18.141514 19.580879 17.323647 14.517060 ... 0.669568 0.766687 0.359019 18.786894 18.217325 17.240399 15.172601 16.580120 3.446935 4.715048
min 155.000000 50.000000 48.000000 48.000000 0.000000 0.000000 6.000000 3.000000 5.000000 7.000000 ... 1.000000 1.000000 1.000000 5.000000 4.000000 5.000000 5.000000 5.000000 1.000000 18.000000
25% 176.000000 70.000000 67.000000 62.000000 475000.000000 1000.000000 38.000000 30.000000 44.000000 54.000000 ... 3.000000 2.000000 1.000000 49.000000 35.000000 31.000000 43.000000 54.000000 3.000000 23.000000
50% 181.000000 75.000000 71.000000 67.000000 950000.000000 3000.000000 54.000000 50.000000 55.000000 62.000000 ... 3.000000 2.000000 1.000000 61.000000 49.000000 41.000000 56.000000 63.000000 5.000000 26.000000
75% 186.000000 80.000000 75.000000 71.000000 2000000.000000 8000.000000 63.000000 62.000000 64.000000 68.000000 ... 3.000000 3.000000 1.000000 68.000000 61.000000 55.000000 64.000000 69.000000 9.000000 30.000000
max 206.000000 110.000000 95.000000 93.000000 185500000.000000 560000.000000 94.000000 95.000000 93.000000 94.000000 ... 5.000000 5.000000 5.000000 96.000000 94.000000 94.000000 93.000000 96.000000 12.000000 54.000000

8 rows × 45 columns

Unique Features in Each Column

In [18]:
#Checking for the Number Unique Features in Each Column.
data_fifa.nunique()
Out[18]:
PlayerName                  18914
PlayerHeight                   50
PlayerWeight                   56
PotentialRating                46
BestPositions                  15
BestOverallRating              46
PlayerValue                   256
Wage                          133
Nationality                   165
Crossing                       89
FinishingAccuracy              93
HeadingAccuracy                89
ShortPassing                   86
Volleys                        88
DefensiveAwareness             92
StandingTackle                 87
SlidingTackle                  85
Diving                         69
Handling                       70
Kicking                        79
GkPositioning                  75
Reflexes                       70
Aggression                     88
Interceptions                  89
Positioning                    94
Vision                         86
Penalties                      87
Composure                      85
Acceleration                   85
SprintSpeed                    84
Agility                        81
Reactions                      70
Balance                        82
ShotPower                      76
JumpingPower                   75
Stamina                        85
Strength                       77
LongShots                      91
PreferredFoot                   2
WeakFoot                        5
SkillMoves                      5
InternationalReputations        5
WorkRate                        9
BodyType                       10
Dribbling                      91
Curve                          91
FreekickAccuracy               90
LongPassing                    86
BallControl                    91
month                          12
age                            28
dtype: int64

Fastest Players for FIFA 2021

In [49]:
player_name = data_fifa[["Acceleration","PlayerName","BestPositions",'age','Nationality','SprintSpeed']].nlargest(7, ['Acceleration']).set_index('PlayerName')
player_name
Out[49]:
Acceleration BestPositions age Nationality SprintSpeed
PlayerName
Adama Traoré Diarra 97 RM 25 Spain 96
Kylian Mbappé Lottin 96 ST 23 France 96
Raheem Shaquille Sterling 96 LW 27 England 90
Moussa Diaby 96 LM 22 France 90
Alphonso Davies 96 LB 21 Canada 96
Daniel James 96 RM 24 Wales 95
Jérémy Doku 96 RW 19 Belgium 91

Tallest Players in FIFA 2021

In [28]:
player_name = data_fifa[["PlayerHeight","PlayerName","PlayerWeight","BestPositions",'age','Nationality']].nlargest(10, ['PlayerHeight']).set_index('PlayerName')
player_name
Out[28]:
PlayerHeight PlayerWeight BestPositions age Nationality
PlayerName
Tomáš Holý 206 102 GK 30 Czech Republic
Costel Fane Pantilimon 203 96 GK 34 Romania
Abdoul Bocar Bâ 203 94 CB 27 Mauritania
Aaron James Chapman 203 92 GK 31 England
Vanja Milinković-Savić 202 92 GK 24 Serbia
Kjell Scherpen 202 85 GK 21 Netherlands
Stefan Maierhofer 202 98 ST 39 Austria
Demba Thiam Ngagne 202 87 GK 23 Senegal
Lovre Kalinić 201 99 GK 31 Croatia
Fraser Forster 201 93 GK 33 England

Best Defender in FIFA 2021

In [20]:
player_name = data_fifa[["DefensiveAwareness","PlayerName","BestPositions",'age','Nationality']].nlargest(10, ['DefensiveAwareness']).set_index('PlayerName')
player_name
Out[20]:
DefensiveAwareness BestPositions age Nationality
PlayerName
Giorgio Chiellini 94 CB 37 Italy
Virgil van Dijk 93 CB 30 Netherlands
Milan Škriniar 92 CB 26 Slovakia
Kalidou Koulibaly 91 CB 30 Senegal
Mats Hummels 90 CB 33 Germany
Clément Nicolas Laurent Lenglet 90 CB 26 France
Leonardo Bonucci 90 CB 34 Italy
Diego Roberto Godín Leal 90 CB 35 Uruguay
N'Golo Kanté 89 CDM 30 France
Aymeric Laporte 89 CB 27 France

Strongest Players in FIFA 2021

In [42]:
player_name = data_fifa[["Strength","PlayerName","PlayerHeight","Stamina",'age','Nationality','PlayerWeight','BestPositions']].nlargest(7, ['Strength']).set_index('PlayerName')
player_name
Out[42]:
Strength PlayerHeight Stamina age Nationality PlayerWeight BestPositions
PlayerName
Adebayo Akinfenwa 97 178 65 39 England 110 ST
Daryl Dike 96 188 59 21 United States 100 ST
Romelu Lukaku Menama 95 191 73 28 Belgium 94 ST
Armando Jesús Méndez Alcorta 95 176 79 25 Uruguay 82 RB
Aleksandar Vukotić 95 201 61 26 Serbia 95 CB
Joyskim Aurélien Dawa Tchakonte 95 194 66 25 Cameroon 95 CB
Kalidou Koulibaly 94 187 70 30 Senegal 89 CB

Best Player's with LongPasses in FIFA 2021

In [46]:
player_name = data_fifa[["LongPassing","PlayerName","ShortPassing","Stamina",'age','Nationality','PlayerWeight','BestPositions']].nlargest(10, ['LongPassing']).set_index('PlayerName')
player_name
Out[46]:
LongPassing ShortPassing Stamina age Nationality PlayerWeight BestPositions
PlayerName
Kevin De Bruyne 93 94 89 30 Belgium 70 CAM
Toni Kroos 93 93 75 31 Germany 76 CM
Lionel Andrés Messi Cuccittini 91 91 72 34 Argentina 72 RW
Paul Pogba 91 86 83 28 France 84 CM
Daniel Parejo Muñoz 90 92 78 32 Spain 74 CM
Trent Alexander-Arnold 89 85 88 23 England 69 RB
Luka Modrić 89 91 83 36 Croatia 66 CM
Marco Verratti 89 90 76 29 Italy 60 CM
Hakim Ziyech 89 86 80 28 Morocco 65 CAM
Luis Alberto Romero Alconchel 89 90 75 29 Spain 74 CAM

Best Player's with ShortPasses in FIFA 2021

In [50]:
player_name = data_fifa[["ShortPassing","PlayerName","LongPassing","Stamina",'age','Nationality','PlayerWeight','BestPositions']].nlargest(10, ['ShortPassing']).set_index('PlayerName')
player_name
Out[50]:
ShortPassing LongPassing Stamina age Nationality PlayerWeight BestPositions
PlayerName
Kevin De Bruyne 94 93 89 30 Belgium 70 CAM
Toni Kroos 93 93 75 31 Germany 76 CM
David Josué Jiménez Silva 92 84 70 35 Spain 67 CAM
Daniel Parejo Muñoz 92 90 78 32 Spain 74 CM
Lionel Andrés Messi Cuccittini 91 91 72 34 Argentina 72 RW
Luka Modrić 91 89 83 36 Croatia 66 CM
Marco Verratti 90 89 76 29 Italy 60 CM
Frenkie de Jong 90 86 90 24 Netherlands 74 CM
Luis Alberto Romero Alconchel 90 89 75 29 Spain 74 CAM
Christian Dannemann Eriksen 90 87 92 29 Denmark 76 CAM

Most Paid Players

In [57]:
player_name = data_fifa[["Wage","PlayerName","PlayerValue","BestOverallRating",'age','Nationality','PotentialRating','InternationalReputations']].nlargest(10, ['Wage']).set_index('PlayerName')
player_name
Out[57]:
Wage PlayerValue BestOverallRating age Nationality PotentialRating InternationalReputations
PlayerName
Lionel Andrés Messi Cuccittini 560000 103500000 93 34 Argentina 93 5
Kevin De Bruyne 370000 129000000 91 30 Belgium 91 4
Karim Benzema 350000 83500000 89 34 France 89 4
Eden Hazard 350000 89500000 88 30 Belgium 88 4
Carlos Henrique Venancio Casimiro 310000 90500000 89 29 Brazil 89 3
Toni Kroos 310000 87500000 88 31 Germany 88 4
Sergio Ramos García 300000 33500000 89 35 Spain 89 4
Sergio Leonel Agüero del Castillo 300000 83500000 89 33 Argentina 89 4
Antoine Griezmann 290000 79500000 87 30 France 87 4
Neymar da Silva Santos Júnior 270000 132000000 91 29 Brazil 91 5

Best GoalKeeper by Reflex

In [21]:
player_name = data_fifa[["Reflexes","PlayerName",'age','Nationality','Kicking','Handling']].nlargest(10, ['Reflexes']).set_index('PlayerName')
player_name
Out[21]:
Reflexes age Nationality Kicking Handling
PlayerName
Jan Oblak 90 28 Slovenia 78 92
Marc-André ter Stegen 90 29 Germany 88 85
Keylor Navas Gamboa 90 35 Costa Rica 75 81
Hugo Lloris 90 35 France 68 82
Alisson Ramsés Becker 89 29 Brazil 85 88
Manuel Neuer 89 35 Germany 91 87
Samir Handanovič 89 37 Slovenia 73 85
David De Gea Quintana 89 31 Spain 78 81
Gianluigi Donnarumma 89 22 Italy 76 81
Kasper Schmeichel 89 35 Denmark 83 77