#loading the required libraries
import pandas as pd
from matplotlib import pyplot as plt
#import seaborn as sns

#loading the Transfer list dataset
pla=pd.read_csv('dataset.csv')

#having a glance at records of the dataset
pla

#Looking at the number of rows(no. of palyers) and columns in dataset
pla.shape

(102, 6)

#Getting the frequency of teams buying most no. players in the list
pla['To Club'].value_counts()

 Chelsea              15
 Manchester City      13
 Manchester United    12
 Barcelona            11
 Real Madrid          10
 PSG                   6
 Juventus              6
 Arsenal               6
 Liverpool             5
 Atletico Madrid       3
 Bayern Munich         2
 Tottenham             2
 Shanghai SIPG         2
 Inter                 2
 Newcastle United      1
 Manchester City       1
 Bayern Munich         1
 Lazio                 1
 Jiangsu Suning        1
 Napoli                1
 Everton               1
Name: To Club, dtype: int64

#making a bar-plot for the top 5 teams buying most no. players in the list
plt.figure(figsize=(9,5))
plt.bar(list(pla['To Club'].value_counts()[0:5].keys()),list(pla['To Club'].value_counts()[0:5]))
plt.xlabel('Teams')
plt.ylabel('No of times')
plt.show()

#Getting the frequency of team with most sales in th list
pla['From Club'].value_counts()[0:9]

Monaco                8
Juventus              5
Benfica               4
Liverpool             4
Manchester City       4
Borussia Dortmund     4
Real Madrid           4
Chelsea               4
Barcelona             3
Name: From Club, dtype: int64

#replacing the pound and 'm' symbol in it
pla['Fee'] = pla['Fee'].str.replace('€', '').str.replace('m', '').astype(float)

pla

#Extracting the records where a team has purchased a player equal to or with a lesser value than 100
pla[pla['Fee']<=100]

PSG_players = pla[pla['To Club'].str.contains('PSG')]
PSG_players

pla.groupby('To Club')['Fee'].sum().sort_values(ascending=False)

To Club
 Chelsea              1127.50
 Barcelona             882.00
 Manchester United     871.00
 Manchester City       856.50
 Real Madrid           777.30
 PSG                   615.60
 Juventus              464.00
 Arsenal               356.65
 Liverpool             342.50
 Atletico Madrid       257.00
 Inter                 129.00
 Shanghai SIPG         118.90
 Tottenham             118.00
 Bayern Munich         116.00
 Bayern Munich          80.00
 Manchester City        75.00
 Newcastle United       65.00
 Lazio                  55.00
 Jiangsu Suning         50.00
 Napoli                 50.00
 Everton                49.20
Name: Fee, dtype: float64

#Total fee and no. of players of the top 5 teams that spent most of the money
team_stats = pla.groupby('To Club').agg({'Fee': ['sum', 'count']})
team_stats.columns = ['Total Fee', 'No. of Players']
team_stats = team_stats.sort_values(by='Total Fee', ascending=False)
team_stats.head()

#Total fee and no. of players of the top 5 teams that spent earned of the money
team_stats = pla.groupby('From Club').agg({'Fee': ['sum', 'count']})
team_stats.columns = ['Total Fee', 'No. of Players']
team_stats = team_stats.sort_values(by='Total Fee', ascending=False)
plt.bar(list(team_stats['Total Fee'][0:5].keys()),list(team_stats['No. of Players'][0:5]))
plt.xlabel('Teams')
plt.ylabel('No. of Players')
plt.show()
team_stats.head()

Out [16]:

	Total Fee	No. of Players
From Club
Monaco	577.5	8
Benfica	390.0	4
Juventus	374.5	5
Barcelona	354.0	3
Liverpool	330.4	4