# initial imports import numpy as np import pandas as pd from matplotlib import pyplot as plt import seaborn as sns

fifa = pd.read_csv('datasets/players_21.csv') fifa.head()

for col in fifa.columns: print(col)

fifa.shape

fifa['nationality_name'].value_counts()

fifa['nationality_name'].value_counts()[0:10]

top_5_countries = fifa['nationality_name'].value_counts()[0:5] top_5_countries

plt.figure(figsize=(8,5)) plt.bar(list(top_5_countries.keys()), list(top_5_countries)) plt.show()

player_salaries = fifa[['short_name', 'wage_eur', 'club_name']] player_salaries = player_salaries.sort_values(by=['wage_eur'], ascending=False) player_salaries.head()

top_10_highest_paid = player_salaries[0:10] top_10_highest_paid

plt.figure(figsize=(10,5)) plt.bar(list(top_10_highest_paid['club_name'].value_counts().keys()), list(top_10_highest_paid['club_name'].value_counts())) plt.show()

player_values = fifa[['short_name', 'value_eur', 'club_name']] player_values = player_values.sort_values(by=['value_eur'], ascending=False) player_values['Millions_EUROS'] = player_values['value_eur'] / 1000000 player_values.head()

top_10_most_expensive = player_values[0:10] top_10_most_expensive

plt.figure(figsize=(15,10)) plt.bar(list(top_10_most_expensive['club_name'].value_counts().keys()), list(top_10_most_expensive['club_name'].value_counts())) plt.show()

Porto = fifa[fifa['club_name'] == 'FC Porto'] Porto