In [1]:

import pandas as pd
import numpy as np
import warnings

In [2]:

warnings.filterwarnings('ignore')

In [3]:

df = pd.read_csv('football_players_data.csv')

In [4]:

df.head(1)

Out[4]:

	Name	Position	Club	Division	Based	Nation	Height	Weight	Age	Preferred Foot	...	Wage	Current Ability	Potential Ability	Vision	Stamina	Pace	Determination	Concentration	Balance	Acceleration
0	Lionel Messi	AM (RC), ST (C)	Barcelona	Spanish First Division	Spain (First Division)	ARG	170	72	32	Left	...	1200000	195	200	20	13	15	20	13	19	18

1 rows × 23 columns

In [ ]:

df.info()

Basic GroupBy Activities¶

1) Extract Players from Argentine and Store in DataFrame named `players_arg`¶

In [5]:

players_arg = df[df['Nation']=='ARG'] # Enter your code here

2) Extract Players with Preferred Foot as `Either` and Store in `players_preferred_foot_either`¶

In [7]:

players_preferred_foot_either = df[df['Preferred Foot']=='Either'] # Enter your code here

3) Identify the Club with the Maximum Number of Players with Preferred Foot as `Either`¶

In [15]:

players_preferred_foot_either.groupby('Club').size().idxmax()

Out[15]:

'Al-Hilal (KSA)'

4) Compute Average Age of Players per Club¶

In [16]:

avg_age_per_club = df.groupby('Club')[['Age']].mean()
avg_age_per_club

Out[16]:

	Age
Club
1. FC Heidenheim 1846	26.000000
1. FC Köln	23.692308
1. FC Köln II	33.000000
1. FC Nürnberg	25.272727
1860 Munich	25.500000
...	...
Östersunds FK	26.000000
Újpest	25.000000
Ümraniyespor	27.750000
İstanbulspor	25.750000
Śląsk Wrocław	24.857143

1369 rows × 1 columns

5) Calculate Total Value of Players per Division¶

In [18]:

total_value_per_division = df.groupby('Division')[['Value']].sum()
total_value_per_division

Out[18]:

	Value
Division
3. Liga	64650000
3F Superliga	144725000
Albanian Superleague	1900000
Angolan Championship	375000
Argentine Metropolitan B Zone	33975000
...	...
United States Soccer League Division Two	9975000
Unknown	89350000
Uruguayan First Division	41650000
Uruguayan Second Professional Division	3625000
Vanarama National League North	400000

193 rows × 1 columns

6) Find Division with Players of Highest Value¶

In [20]:

total_value_per_division.idxmax()

Out[20]:

Value    English Premier Division
dtype: object

7)Count Players by Preferred Foot and Store in `preferred_foot_counts`¶

In [36]:

preferred_foot_counts = df.groupby('Preferred Foot').size().reset_index(name='counts')
preferred_foot_counts

Out[36]:

	Preferred Foot	counts
0	Either	482
1	Left	2423
2	Left Only	927
3	Right	8389
4	Right Only	1766

8) Find Out Which Foot is Preferred by the Majority of Players¶

In [39]:

preferred_foot_counts.loc[preferred_foot_counts['counts'].idxmax()]

Out[39]:

Preferred Foot    Right
counts             8389
Name: 3, dtype: object

9) Determine Maximum Wage by Nation¶

In [41]:

max_wage_per_nation = df.groupby('Nation')[['Wage']].max()
max_wage_per_nation

Out[41]:

	Wage
Nation
ALB	49000
ALG	160000
ANG	30500
ARG	1200000
ARM	180000
...	...
VEN	45500
WAL	525000
YEM	6000
ZAM	31000
ZIM	41500

161 rows × 1 columns

10) What is the Country with the Highest Wage Offering?¶

In [43]:

max_wage_per_nation.idxmax()

Out[43]:

Wage    ARG
dtype: object

11) Calculate the average height and weight for each nation and store the results in a dataframe named `avg_height_weight_per_nation`¶

In [45]:

avg_height_weight_per_nation = df.groupby('Nation')[['Height', 'Weight']].mean()
avg_height_weight_per_nation

Out[45]:

	Height	Weight
Nation
ALB	181.897436	74.897436
ALG	181.678571	76.107143
ANG	178.411765	73.235294
ARG	178.717472	74.137015
ARM	181.500000	74.166667
...	...	...
VEN	180.297872	74.255319
WAL	180.870968	73.870968
YEM	178.000000	69.000000
ZAM	179.750000	75.750000
ZIM	180.777778	70.777778

161 rows × 2 columns

12) Determine the nation with the lowest average height and the nation with the highest average weight among players¶

In [49]:

print('Lowest Height', avg_height_weight_per_nation['Height'].idxmin())
print('Heighest Weight', avg_height_weight_per_nation['Weight'].idxmax())

Lowest Height LES
Heighest Weight GUF

13) Calculate the total stamina for each club and store the result in `total_stamina_per_club`¶

In [51]:

total_stamina_per_club = df.groupby('Club')[['Stamina']].sum()
total_stamina_per_club

Out[51]:

	Stamina
Club
1. FC Heidenheim 1846	170
1. FC Köln	352
1. FC Köln II	12
1. FC Nürnberg	280
1860 Munich	25
...	...
Östersunds FK	13
Újpest	12
Ümraniyespor	46
İstanbulspor	49
Śląsk Wrocław	87

1369 rows × 1 columns

14) Which Club's Players Exhibit Remarkable Stamina Levels?¶

In [53]:

total_stamina_per_club.idxmax()

Out[53]:

Stamina    Vélez
dtype: object

15) Who is the player with most stamina in Club `Barcelona`¶

In [55]:

df.head(1)

Out[55]:

	Name	Position	Club	Division	Based	Nation	Height	Weight	Age	Preferred Foot	...	Wage	Current Ability	Potential Ability	Vision	Stamina	Pace	Determination	Concentration	Balance	Acceleration
0	Lionel Messi	AM (RC), ST (C)	Barcelona	Spanish First Division	Spain (First Division)	ARG	170	72	32	Left	...	1200000	195	200	20	13	15	20	13	19	18

1 rows × 23 columns

In [67]:

club_barcelona = df[df['Club']=='Barcelona']
staminas = club_barcelona.groupby('Name')[['Stamina']].sum()
staminas.idxmax()

Out[67]:

Stamina    Sergi Roberto
dtype: object

16) What is the best role of `Sergi Roberto`?¶

In [72]:

segi_best_role = df[df['Name']=='Sergi Roberto']['Best Role'].iloc[0]
segi_best_role

Out[72]:

'BBM'

17) Calculate the average vision score for each position and Store the result in `avg_vision_per_position`¶

In [73]:

avg_vision_per_position = df.groupby('Position')[['Vision']].mean()

18) What is the average vision score for players in the `AM (C)` position?¶

In [76]:

avg_vision_per_position.loc['AM (C)'].iloc[0]

Out[76]:

12.972972972972974

19) Who is the top player by Potential Ability in the `Spanish First Division`?¶

In [95]:

players_spanish = df[df['Division'] == 'Spanish First Division']
players_spanish.loc[players_spanish['Potential Ability'].idxmax()].iloc[0]

Out[95]:

'Lionel Messi'

20) Calculate the average market value of players within each club and position combination¶

In [106]:

avg_market_value_init = df.groupby(['Club','Position'])['Value'].mean()
avg_market_value = avg_market_value_init.reset_index()
avg_market_value.columns = ['Club', 'Position', 'Value']
avg_market_value

Out[106]:

	Club	Position	Value
0	1. FC Heidenheim 1846	D (C)	525000.0
1	1. FC Heidenheim 1846	D (L), M/AM (LC)	925000.0
2	1. FC Heidenheim 1846	D (LC), DM, M (C)	825000.0
3	1. FC Heidenheim 1846	D/WB/M (L)	525000.0
4	1. FC Heidenheim 1846	D/WB/M/AM (R)	700000.0
...	...	...	...
10658	Śląsk Wrocław	D (RLC)	500000.0
10659	Śląsk Wrocław	D/WB/M/AM (L)	475000.0
10660	Śląsk Wrocław	DM, M (C)	475000.0
10661	Śląsk Wrocław	M (RL), AM (RLC), ST (C)	375000.0
10662	Śląsk Wrocław	ST (C)	800000.0

10663 rows × 3 columns

21) Count the number of players within each nation and preferred foot combination¶

In [111]:

player_counts_nation = df.groupby(['Nation', 'Preferred Foot']).size()
player_counts_nation_pf = player_counts_nation.reset_index()
player_counts_nation_pf.columns=['Nation', 'Preferred Foot', 'Player Count']
player_counts_nation_pf

Out[111]:

	Nation	Preferred Foot	Player Count
0	ALB	Either	2
1	ALB	Left	6
2	ALB	Right	26
3	ALB	Right Only	5
4	ALG	Left	11
...	...	...	...
571	ZAM	Right Only	2
572	ZIM	Left	2
573	ZIM	Left Only	1
574	ZIM	Right	5
575	ZIM	Right Only	1

576 rows × 3 columns

Activities using Aggregation¶

22) Calculate the mean value and maximum current ability of players within each club¶

In [113]:

club_aggregations = df.groupby('Club').agg({
    'Value':'mean',
    'Current Ability':'max'
})
club_aggregations

Out[113]:

	Value	Current Ability
Club
1. FC Heidenheim 1846	1.003846e+06	125
1. FC Köln	5.192308e+06	142
1. FC Köln II	8.250000e+05	118
1. FC Nürnberg	2.385227e+06	129
1860 Munich	4.875000e+05	114
...	...	...
Östersunds FK	4.250000e+05	108
Újpest	4.500000e+05	113
Ümraniyespor	5.562500e+05	115
İstanbulspor	6.812500e+05	110
Śląsk Wrocław	5.678571e+05	115

1369 rows × 2 columns

23) Calculate Age Range by Nation¶

In [126]:

def age_range(x):
    return x.max() - x.min()

age_range_per_nation = df.groupby('Nation')['Age'].agg({age_range}).reset_index()
age_range_per_nation.columns = ['Nation', 'Age Range']
age_range_per_nation = age_range_per_nation.set_index('Nation')
age_range_per_nation

Out[126]:

	Age Range
Nation
ALB	14
ALG	12
ANG	12
ARG	22
ARM	7
...	...
VEN	15
WAL	14
YEM	0
ZAM	11
ZIM	10

161 rows × 1 columns

24) What is the average wage of players in the `3. Liga`, the mean stamina of players in the `3F Superliga`, and the standard deviation of player stamina in the `Albanian Superleague`?¶

Enter the three values in the format: answer1, answer2, answer3.

In [131]:

liga3_players = df[df['Division'] == '3. Liga']
print('Average wage of players in 3. Liga: ', liga3_players['Wage'].sum())
superliga3F_players = df[df['Division'] == '3F Superliga']
print('Mean stamina of players in the 3F Superliga: ', superliga3F_players['Stamina'].mean())
albanian_superleague_players = df[df['Division'] == 'Albanian Superleague']
print('Standard deviation of player stamina in the Albanian Superleague: ', albanian_superleague_players['Stamina'].std())

Average wage of players in 3. Liga:  542400
Mean stamina of players in the 3F Superliga:  12.422619047619047
Standard deviation of player stamina in the Albanian Superleague:  3.055050463303893

25) Calculate Average Value and Current Ability Variance by Club¶

In [138]:

def variance(x):
    return x.var()
    
club_statistics = df.groupby('Club').agg({
    'Value':'mean',
    'Current Ability':variance
})
club_statistics

Out[138]:

	Value	Current Ability
Club
1. FC Heidenheim 1846	1.003846e+06	19.435897
1. FC Köln	5.192308e+06	99.835385
1. FC Köln II	8.250000e+05	NaN
1. FC Nürnberg	2.385227e+06	28.112554
1860 Munich	4.875000e+05	0.500000
...	...	...
Östersunds FK	4.250000e+05	NaN
Újpest	4.500000e+05	NaN
Ümraniyespor	5.562500e+05	16.666667
İstanbulspor	6.812500e+05	6.250000
Śląsk Wrocław	5.678571e+05	8.000000

1369 rows × 2 columns

Activities using Apply¶

26) Classify Players Based on Ability¶

In [144]:

def player_type(player):
    if player['Current Ability'] > 180 and player['Potential Ability']>190:
        return 'Star'
    else:
        return 'Regular'

df['Player Type'] = df.apply(player_type, axis=1)
df

Out[144]:

	Name	Position	Club	Division	Based	Nation	Height	Weight	Age	Preferred Foot	...	Current Ability	Potential Ability	Vision	Stamina	Pace	Determination	Concentration	Balance	Acceleration	Player Type
0	Lionel Messi	AM (RC), ST (C)	Barcelona	Spanish First Division	Spain (First Division)	ARG	170	72	32	Left	...	195	200	20	13	15	20	13	19	18	Star
1	Cristiano Ronaldo	AM (RL), ST (C)	Juventus	Italian Serie A	Italy (Serie A)	POR	185	83	34	Either	...	195	196	14	15	16	20	16	14	15	Star
2	Kylian Mbappé	AM (RL), ST (C)	Paris SG	Ligue 1 Conforama	France (Ligue 1 Conforama)	FRA	178	73	20	Right	...	181	196	13	14	20	14	13	14	20	Star
3	Manuel Neuer	GK	FC Bayern	Bundesliga	Germany (Bundesliga)	GER	192	90	33	Either	...	176	193	12	10	10	18	15	14	13	Regular
4	Neymar	M (L), AM (LC), ST (C)	Paris SG	Ligue 1 Conforama	France (Ligue 1 Conforama)	BRA	175	68	27	Right	...	186	190	16	13	16	16	10	14	17	Regular
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13982	Mirza'ekber	AM (C), ST (C)	XJ TS Leopard	Chinese National First Division	China (First Division)	CHN	186	82	24	Right Only	...	56	63	6	12	13	9	7	6	13	Regular
13983	Miao Ming	D (C)	Meizhou	Chinese National First Division	China (First Division)	CHN	185	73	29	Right	...	59	62	6	9	8	4	9	12	7	Regular
13984	Yu Jiawei	D (LC), M (L)	SY Urban	Chinese National Second Division Northern Group	China (Second Division North)	CHN	183	76	25	Left Only	...	58	62	5	10	6	6	8	12	7	Regular
13985	Liu Xinyu	ST (C)	Renhe	Chinese Super League	China (Super League)	CHN	192	75	19	Right Only	...	50	61	1	6	10	12	1	4	13	Regular
13986	Han Tianlin	M (C)	Changchun	Chinese National First Division	China (First Division)	CHN	182	72	22	Right Only	...	46	60	12	12	12	9	4	4	12	Regular

13987 rows × 24 columns

27) Categorize Players by Market Value¶

In [150]:

def categorize_by_value(value):
    if value['Value'] > 50000000:
        return 'High'
    elif 20000000 < value['Value'] <= 50000000:
        return 'Medium'
    else:
        return 'Low'

df['Value Type'] = df.apply(categorize_by_value, axis=1)
df

Out[150]:

	Name	Position	Club	Division	Based	Nation	Height	Weight	Age	Preferred Foot	...	Potential Ability	Vision	Stamina	Pace	Determination	Concentration	Balance	Acceleration	Player Type	Value Type
0	Lionel Messi	AM (RC), ST (C)	Barcelona	Spanish First Division	Spain (First Division)	ARG	170	72	32	Left	...	200	20	13	15	20	13	19	18	Star	High
1	Cristiano Ronaldo	AM (RL), ST (C)	Juventus	Italian Serie A	Italy (Serie A)	POR	185	83	34	Either	...	196	14	15	16	20	16	14	15	Star	Medium
2	Kylian Mbappé	AM (RL), ST (C)	Paris SG	Ligue 1 Conforama	France (Ligue 1 Conforama)	FRA	178	73	20	Right	...	196	13	14	20	14	13	14	20	Star	High
3	Manuel Neuer	GK	FC Bayern	Bundesliga	Germany (Bundesliga)	GER	192	90	33	Either	...	193	12	10	10	18	15	14	13	Regular	Medium
4	Neymar	M (L), AM (LC), ST (C)	Paris SG	Ligue 1 Conforama	France (Ligue 1 Conforama)	BRA	175	68	27	Right	...	190	16	13	16	16	10	14	17	Regular	High
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13982	Mirza'ekber	AM (C), ST (C)	XJ TS Leopard	Chinese National First Division	China (First Division)	CHN	186	82	24	Right Only	...	63	6	12	13	9	7	6	13	Regular	Low
13983	Miao Ming	D (C)	Meizhou	Chinese National First Division	China (First Division)	CHN	185	73	29	Right	...	62	6	9	8	4	9	12	7	Regular	Low
13984	Yu Jiawei	D (LC), M (L)	SY Urban	Chinese National Second Division Northern Group	China (Second Division North)	CHN	183	76	25	Left Only	...	62	5	10	6	6	8	12	7	Regular	Low
13985	Liu Xinyu	ST (C)	Renhe	Chinese Super League	China (Super League)	CHN	192	75	19	Right Only	...	61	1	6	10	12	1	4	13	Regular	Low
13986	Han Tianlin	M (C)	Changchun	Chinese National First Division	China (First Division)	CHN	182	72	22	Right Only	...	60	12	12	12	9	4	4	12	Regular	Low

13987 rows × 25 columns

28) Categorize Players by Age¶

In [152]:

def categorize_by_age(age):
    if age['Age'] < 25:
        return 'Young'
    elif 25 <= age['Age'] <=29:
        return 'Mid-age'
    else:
        return 'Senior'

df['Age Group'] = df.apply(categorize_by_age, axis=1)

29) Who are the top 2 most valuable players in `1. FC Heidenheim 1846`?¶

In [164]:

fc_diviision = df[df['Club'] == '1. FC Heidenheim 1846']
fc_diviision.sort_values(by='Value', ascending=False)['Name'].head(2)

Out[164]:

5690    Tim Kleindienst
2851      Niklas Dorsch
Name: Name, dtype: object

30) Calculate BMI for Each Player¶

In [168]:

def calculate_bmi(player):
    playerHeight_meter = player['Height']/100
    return player['Weight'] / (playerHeight_meter**2)

df['BMI'] = df.apply(calculate_bmi, axis = 1)
df

Out[168]:

	Name	Position	Club	Division	Based	Nation	Height	Weight	Age	Preferred Foot	...	Stamina	Pace	Determination	Concentration	Balance	Acceleration	Player Type	Value Type	Age Group	BMI
0	Lionel Messi	AM (RC), ST (C)	Barcelona	Spanish First Division	Spain (First Division)	ARG	170	72	32	Left	...	13	15	20	13	19	18	Star	High	Senior	24.913495
1	Cristiano Ronaldo	AM (RL), ST (C)	Juventus	Italian Serie A	Italy (Serie A)	POR	185	83	34	Either	...	15	16	20	16	14	15	Star	Medium	Senior	24.251278
2	Kylian Mbappé	AM (RL), ST (C)	Paris SG	Ligue 1 Conforama	France (Ligue 1 Conforama)	FRA	178	73	20	Right	...	14	20	14	13	14	20	Star	High	Young	23.040020
3	Manuel Neuer	GK	FC Bayern	Bundesliga	Germany (Bundesliga)	GER	192	90	33	Either	...	10	10	18	15	14	13	Regular	Medium	Senior	24.414062
4	Neymar	M (L), AM (LC), ST (C)	Paris SG	Ligue 1 Conforama	France (Ligue 1 Conforama)	BRA	175	68	27	Right	...	13	16	16	10	14	17	Regular	High	Mid-age	22.204082
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13982	Mirza'ekber	AM (C), ST (C)	XJ TS Leopard	Chinese National First Division	China (First Division)	CHN	186	82	24	Right Only	...	12	13	9	7	6	13	Regular	Low	Young	23.702162
13983	Miao Ming	D (C)	Meizhou	Chinese National First Division	China (First Division)	CHN	185	73	29	Right	...	9	8	4	9	12	7	Regular	Low	Mid-age	21.329438
13984	Yu Jiawei	D (LC), M (L)	SY Urban	Chinese National Second Division Northern Group	China (Second Division North)	CHN	183	76	25	Left Only	...	10	6	6	8	12	7	Regular	Low	Mid-age	22.694019
13985	Liu Xinyu	ST (C)	Renhe	Chinese Super League	China (Super League)	CHN	192	75	19	Right Only	...	6	10	12	1	4	13	Regular	Low	Young	20.345052
13986	Han Tianlin	M (C)	Changchun	Chinese National First Division	China (First Division)	CHN	182	72	22	Right Only	...	12	12	9	4	4	12	Regular	Low	Young	21.736505

13987 rows × 27 columns

Activities Using Transform¶

31) Rank Players by Market Value Within Each Nation¶

In [171]:

df['Value Rank'] = df.groupby('Nation')['Value'].transform(lambda x: x.rank(ascending=False).astype(int))
df

Out[171]:

	Name	Position	Club	Division	Based	Nation	Height	Weight	Age	Preferred Foot	...	Pace	Determination	Concentration	Balance	Acceleration	Player Type	Value Type	Age Group	BMI	Value Rank
0	Lionel Messi	AM (RC), ST (C)	Barcelona	Spanish First Division	Spain (First Division)	ARG	170	72	32	Left	...	15	20	13	19	18	Star	High	Senior	24.913495	2
1	Cristiano Ronaldo	AM (RL), ST (C)	Juventus	Italian Serie A	Italy (Serie A)	POR	185	83	34	Either	...	16	20	16	14	15	Star	Medium	Senior	24.251278	12
2	Kylian Mbappé	AM (RL), ST (C)	Paris SG	Ligue 1 Conforama	France (Ligue 1 Conforama)	FRA	178	73	20	Right	...	20	14	13	14	20	Star	High	Young	23.040020	1
3	Manuel Neuer	GK	FC Bayern	Bundesliga	Germany (Bundesliga)	GER	192	90	33	Either	...	10	18	15	14	13	Regular	Medium	Senior	24.414062	12
4	Neymar	M (L), AM (LC), ST (C)	Paris SG	Ligue 1 Conforama	France (Ligue 1 Conforama)	BRA	175	68	27	Right	...	16	16	10	14	17	Regular	High	Mid-age	22.204082	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13982	Mirza'ekber	AM (C), ST (C)	XJ TS Leopard	Chinese National First Division	China (First Division)	CHN	186	82	24	Right Only	...	13	9	7	6	13	Regular	Low	Young	23.702162	624
13983	Miao Ming	D (C)	Meizhou	Chinese National First Division	China (First Division)	CHN	185	73	29	Right	...	8	4	9	12	7	Regular	Low	Mid-age	21.329438	624
13984	Yu Jiawei	D (LC), M (L)	SY Urban	Chinese National Second Division Northern Group	China (Second Division North)	CHN	183	76	25	Left Only	...	6	6	8	12	7	Regular	Low	Mid-age	22.694019	477
13985	Liu Xinyu	ST (C)	Renhe	Chinese Super League	China (Super League)	CHN	192	75	19	Right Only	...	10	12	1	4	13	Regular	Low	Young	20.345052	450
13986	Han Tianlin	M (C)	Changchun	Chinese National First Division	China (First Division)	CHN	182	72	22	Right Only	...	12	9	4	4	12	Regular	Low	Young	21.736505	624

13987 rows × 28 columns

32) Standardize Vision Ratings Within Each Club¶

In [175]:

def standardize(x):
    return (x - x.mean()) / x.std()

df['Standardized Vision'] = df.groupby('Club')['Vision'].transform(standardize)
df

Out[175]:

	Name	Position	Club	Division	Based	Nation	Height	Weight	Age	Preferred Foot	...	Determination	Concentration	Balance	Acceleration	Player Type	Value Type	Age Group	BMI	Value Rank	Standardized Vision
0	Lionel Messi	AM (RC), ST (C)	Barcelona	Spanish First Division	Spain (First Division)	ARG	170	72	32	Left	...	20	13	19	18	Star	High	Senior	24.913495	2	2.212736
1	Cristiano Ronaldo	AM (RL), ST (C)	Juventus	Italian Serie A	Italy (Serie A)	POR	185	83	34	Either	...	20	16	14	15	Star	Medium	Senior	24.251278	12	0.699648
2	Kylian Mbappé	AM (RL), ST (C)	Paris SG	Ligue 1 Conforama	France (Ligue 1 Conforama)	FRA	178	73	20	Right	...	14	13	14	20	Star	High	Young	23.040020	1	0.181786
3	Manuel Neuer	GK	FC Bayern	Bundesliga	Germany (Bundesliga)	GER	192	90	33	Either	...	18	15	14	13	Regular	Medium	Senior	24.414062	12	-0.425385
4	Neymar	M (L), AM (LC), ST (C)	Paris SG	Ligue 1 Conforama	France (Ligue 1 Conforama)	BRA	175	68	27	Right	...	16	10	14	17	Regular	High	Mid-age	22.204082	1	1.272499
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13982	Mirza'ekber	AM (C), ST (C)	XJ TS Leopard	Chinese National First Division	China (First Division)	CHN	186	82	24	Right Only	...	9	7	6	13	Regular	Low	Young	23.702162	624	-1.024695
13983	Miao Ming	D (C)	Meizhou	Chinese National First Division	China (First Division)	CHN	185	73	29	Right	...	4	9	12	7	Regular	Low	Mid-age	21.329438	624	-0.898303
13984	Yu Jiawei	D (LC), M (L)	SY Urban	Chinese National Second Division Northern Group	China (Second Division North)	CHN	183	76	25	Left Only	...	6	8	12	7	Regular	Low	Mid-age	22.694019	477	-1.494818
13985	Liu Xinyu	ST (C)	Renhe	Chinese Super League	China (Super League)	CHN	192	75	19	Right Only	...	12	1	4	13	Regular	Low	Young	20.345052	450	-2.336427
13986	Han Tianlin	M (C)	Changchun	Chinese National First Division	China (First Division)	CHN	182	72	22	Right Only	...	9	4	4	12	Regular	Low	Young	21.736505	624	1.513250

13987 rows × 29 columns

33) Calculate Age Percentiles Within Each Club¶

In [178]:

def calculate_percentile(x):
    return x.rank(pct=True)

df['Age Percentile'] = df.groupby('Club')['Age'].transform(calculate_percentile)
df

Out[178]:

	Name	Position	Club	Division	Based	Nation	Height	Weight	Age	Preferred Foot	...	Concentration	Balance	Acceleration	Player Type	Value Type	Age Group	BMI	Value Rank	Standardized Vision	Age Percentile
0	Lionel Messi	AM (RC), ST (C)	Barcelona	Spanish First Division	Spain (First Division)	ARG	170	72	32	Left	...	13	19	18	Star	High	Senior	24.913495	2	2.212736	0.982759
1	Cristiano Ronaldo	AM (RL), ST (C)	Juventus	Italian Serie A	Italy (Serie A)	POR	185	83	34	Either	...	16	14	15	Star	Medium	Senior	24.251278	12	0.699648	0.948276
2	Kylian Mbappé	AM (RL), ST (C)	Paris SG	Ligue 1 Conforama	France (Ligue 1 Conforama)	FRA	178	73	20	Right	...	13	14	20	Star	High	Young	23.040020	1	0.181786	0.104167
3	Manuel Neuer	GK	FC Bayern	Bundesliga	Germany (Bundesliga)	GER	192	90	33	Either	...	15	14	13	Regular	Medium	Senior	24.414062	12	-0.425385	1.000000
4	Neymar	M (L), AM (LC), ST (C)	Paris SG	Ligue 1 Conforama	France (Ligue 1 Conforama)	BRA	175	68	27	Right	...	10	14	17	Regular	High	Mid-age	22.204082	1	1.272499	0.708333
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13982	Mirza'ekber	AM (C), ST (C)	XJ TS Leopard	Chinese National First Division	China (First Division)	CHN	186	82	24	Right Only	...	7	6	13	Regular	Low	Young	23.702162	624	-1.024695	0.500000
13983	Miao Ming	D (C)	Meizhou	Chinese National First Division	China (First Division)	CHN	185	73	29	Right	...	9	12	7	Regular	Low	Mid-age	21.329438	624	-0.898303	0.825000
13984	Yu Jiawei	D (LC), M (L)	SY Urban	Chinese National Second Division Northern Group	China (Second Division North)	CHN	183	76	25	Left Only	...	8	12	7	Regular	Low	Mid-age	22.694019	477	-1.494818	0.166667
13985	Liu Xinyu	ST (C)	Renhe	Chinese Super League	China (Super League)	CHN	192	75	19	Right Only	...	1	4	13	Regular	Low	Young	20.345052	450	-2.336427	0.055556
13986	Han Tianlin	M (C)	Changchun	Chinese National First Division	China (First Division)	CHN	182	72	22	Right Only	...	4	4	12	Regular	Low	Young	21.736505	624	1.513250	0.250000

13987 rows × 30 columns

34) Calculate Deviation from Mean Pace Within Each Club¶

In [180]:

def deviation_from_mean(x):
    return x - x.mean()

df['Pace Deviation'] = df.groupby('Club')['Pace'].transform(deviation_from_mean)

35) Rank Players by Wage within Each Club¶

In [183]:

def rank_wage(wages):
    return wage.rank(ascending=False)

df['Wage Rank'] = df.groupby('Club')['Wage'].transform(rank_wage)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[183], line 4
      1 def rank_wage(wages):
      2     return wage.rank(ascending=False)
----> 4 df['Wage Rank'] = df.groupby('Club')['Wage'].transform(rank_wage)

File /usr/local/lib/python3.11/site-packages/pandas/core/groupby/generic.py:517, in SeriesGroupBy.transform(self, func, engine, engine_kwargs, *args, **kwargs)
    514 @Substitution(klass="Series", example=__examples_series_doc)
    515 @Appender(_transform_template)
    516 def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
--> 517     return self._transform(
    518         func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
    519     )

File /usr/local/lib/python3.11/site-packages/pandas/core/groupby/groupby.py:2021, in GroupBy._transform(self, func, engine, engine_kwargs, *args, **kwargs)
   2018     warn_alias_replacement(self, orig_func, func)
   2020 if not isinstance(func, str):
-> 2021     return self._transform_general(func, engine, engine_kwargs, *args, **kwargs)
   2023 elif func not in base.transform_kernel_allowlist:
   2024     msg = f"'{func}' is not a valid function name for transform(name)"

File /usr/local/lib/python3.11/site-packages/pandas/core/groupby/generic.py:557, in SeriesGroupBy._transform_general(self, func, engine, engine_kwargs, *args, **kwargs)
    552 for name, group in self._grouper.get_iterator(
    553     self._obj_with_exclusions, axis=self.axis
    554 ):
    555     # this setattr is needed for test_transform_lambda_with_datetimetz
    556     object.__setattr__(group, "name", name)
--> 557     res = func(group, *args, **kwargs)
    559     results.append(klass(res, index=group.index))
    561 # check for empty "results" to avoid concat ValueError

Cell In[183], line 2, in rank_wage(wages)
      1 def rank_wage(wages):
----> 2     return wage.rank(ascending=False)

NameError: name 'wage' is not defined