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.
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
#Reading Our Dataset with Pandas Library
data_fifa = pd.read_csv("players.csv")
data_fifa.sample(8)
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
#Viweing the columns available in our Dataset
data_fifa.columns
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')
ID : unique id number to each footballer
Name : name of footballer
Age : age of footballer
Photo : photo of footballer
Nationality : the nationality of the player
Overall : in-game power
Potential : the potential of the football player
Clup: football player's club
Value : value of the player
Wage : wages paid by the player
Special : special
Preferred Foot : foot used by the footballer(left,Right)
International Reputation : the international reputation of the football player
Weak Foot : weak foot of the footballer
Skill Moves : football player's skills moves
Work Rate : football player's work rate
Body Type : body type of the football player
Real Face : real face of the player(false,true)
Position : position played by the football player
Jersey Number : jersey number of the football player
Joined : Joined
Loaned From : is the football player for loaned from
Contract Valid Until : the expiry date of the player contract
Height : footballer's height
Weight : footballer's weight
Crossing : long cross pass by the footballer
Finishing : football player finishing
HeadingAccuracy : HeadingAccuracy
ShortPassing : ShortPassing
Dribbling : player's dribbling speed
Curve : spin on the ball
LongPassing : football player's long pass
BallControl : football player control the ball
Acceleration : the speed of the football player
SprintSpeed : the sprintSpeed of the player
Agility : the agility of the football player
Reactions : the reaction of the footballer
Balance : football player's balance
ShotPower : football player's shotpower
Jumping : the footballer's jumping capacity
Stamina : the footballer's stamina
Strength : the strength of the football player
LongShots : footballer's longest shot
Aggression : football player's aggression
Positioning : the position of the football player in the football field
Vision : football player vision
Penalties : footballer's penalties
Composure : the calmness of the football player on the field
Marking : marking
StandingTackle : the fight of the football player
SlidingTackle : slide intervention
GKDiving : diving
GKHandling : handling
GKKicking : kicking
GKPositioning : Positioning
GKReflexes : reflexes
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.
#Viweing the columns available in our Dataset
data_fifa.columns
#Checking for columns with missing values
data_fifa.columns[data_fifa.isnull().any()]
Index(['int_team_id', 'str_player_speciality', 'str_trait'], dtype='object')
# 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)
#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
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
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
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 |
data_fifa['BodyType'].value_counts()
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
data_fifa.shape
(19002, 51)
#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
#Giving A Statistical Analysis of our Dataset
data_fifa.describe().apply(lambda s: s.apply(lambda x: format(x, 'f')))
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
#Checking for the Number Unique Features in Each Column.
data_fifa.nunique()
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
player_name = data_fifa[["Acceleration","PlayerName","BestPositions",'age','Nationality','SprintSpeed']].nlargest(7, ['Acceleration']).set_index('PlayerName')
player_name
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 |
player_name = data_fifa[["PlayerHeight","PlayerName","PlayerWeight","BestPositions",'age','Nationality']].nlargest(10, ['PlayerHeight']).set_index('PlayerName')
player_name
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 |
player_name = data_fifa[["DefensiveAwareness","PlayerName","BestPositions",'age','Nationality']].nlargest(10, ['DefensiveAwareness']).set_index('PlayerName')
player_name
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 |
player_name = data_fifa[["Strength","PlayerName","PlayerHeight","Stamina",'age','Nationality','PlayerWeight','BestPositions']].nlargest(7, ['Strength']).set_index('PlayerName')
player_name
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 |
player_name = data_fifa[["LongPassing","PlayerName","ShortPassing","Stamina",'age','Nationality','PlayerWeight','BestPositions']].nlargest(10, ['LongPassing']).set_index('PlayerName')
player_name
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 |
player_name = data_fifa[["ShortPassing","PlayerName","LongPassing","Stamina",'age','Nationality','PlayerWeight','BestPositions']].nlargest(10, ['ShortPassing']).set_index('PlayerName')
player_name
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 |
player_name = data_fifa[["Wage","PlayerName","PlayerValue","BestOverallRating",'age','Nationality','PotentialRating','InternationalReputations']].nlargest(10, ['Wage']).set_index('PlayerName')
player_name
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 |
player_name = data_fifa[["Reflexes","PlayerName",'age','Nationality','Kicking','Handling']].nlargest(10, ['Reflexes']).set_index('PlayerName')
player_name
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 |