import pandas as pd import numpy as np import os

import matplotlib.pyplot as plt import seaborn as sns from wordcloud import WordCloud

world_cups = pd.read_csv("WorldCups.csv",sep = ";") world_cup_players = pd.read_csv("WorldCupPlayers.csv", sep = ',') world_cup_matches=pd.read_csv("WorldCupMatches.csv",on_bad_lines='skip',sep = ";")

world_cups.head(3)

world_cup_players.head(3)

world_cup_matches.head(3)

fig=plt.figure() winner=world_cups['Winner'].value_counts() winner.plot(kind='bar',title="Champion of FIFA World Cup",fontsize=14,figsize=(12,10)) plt.xlabel('Teams') plt.ylabel('Number of Winnings') plt.show()

all_pos = world_cups[['Winner','Runners-Up','Third','Fourth']] all_positions = all_pos.apply(pd.value_counts).fillna(0).astype(int)#对于空值，我们用0填充 all_positions.plot(y=['Winner','Runners-Up','Third','Fourth'],kind='bar',fontsize=12,figsize=(18,8)) plt.xlabel('Teams') plt.ylabel('Number of winnings') #sns.despine(right=True,top=True) plt.show()

goal=world_cups[['Year','GoalsScored']] plt.figure(figsize=(16,8)) plt.plot(goal['Year'],goal['GoalsScored'],'-p',color='gray',markersize=15,linewidth=6,markerfacecolor='white',markeredgecolor='gray') plt.xlim(1930,2018) plt.ylim(60,180) plt.xlabel('Year',fontstyle='italic') plt.ylabel('GoalScored') plt.show()

#attendance of crowd in WorldCup from 1930 to 2014 world_cups['Attendance']=world_cups['Attendance'].apply(lambda x:x.replace('.',''))#删除小数点 Crowd=world_cups[['Year','Attendance']] Crowd #plt.figure(figsize=(15,10)) #sns.barplot(Crowd['Year'].astype(int),Crowd['Attendance'].astype(int)) #plt.title('Crowd present at stadium in year 1930 to 2014') #plt.show()

plt.figure(figsize=(15,8)) sns.barplot(Crowd['Year'].astype(int),Crowd['Attendance'].astype(int)) plt.ylabel('Attendance') plt.xlabel('Year') plt.title('Crowd present at stadium in year 1930 to 2014') plt.show()

#Average attendance of the crowd in WorldCup Matches from 1930 to 2014 #avg_att=np.array(world_cups['Attendance'].astype(int))/np.array(world_cups['MatchesPlayed'].astype(int)) #avg_att=np.transpose(avg_att) #avg_att1=world_cups.groupby(world_cups['Year'],avg_att) avg_att = world_cup_matches.groupby('Year')['Attendance'].mean().reset_index() #按照‘Year’进行分组计算'Attendance'的均值 # 可以理解为avg_att=world_cups['Attendance']/world_cups['MatchesPlayed']每一场比赛参与人数

plt.figure(figsize=(15,8)) plt.plot(avg_att['Year'],avg_att['Attendance'],'-p',linewidth=4,color='gray',markersize=12) plt.xlabel('Year') plt.ylabel('Attendance') plt.title('Average attendance of crowd in WorldCup Matches') plt.grid(True) plt.show()

#Half time Goals of home and away team Half_time=world_cup_matches.groupby('Year')['Half-time Home Goals','Half-time Away Goals'].sum().reset_index().astype(int) df=pd.DataFrame({'Half-time Away Goals':Half_time['Half-time Away Goals'],'Half-time Home Goals':Half_time['Half-time Home Goals']}) df.plot(kind='bar',stacked=True,figsize=(18,15)) plt.title('Half time goals of home and away team') r=range(0,20) plt.xticks(r,Half_time['Year']) plt.xlabel('Year') plt.show()

home_team1=world_cup_matches[['Year','Home Team Goals']] home_team1.head(840)

#主队 home_team=world_cup_matches[['Year','Home Team Goals']].head(840).astype(int) plt.figure(figsize=(15,10)) sns.violinplot(x=home_team['Year'],y=home_team['Home Team Goals'],palette='Blues') plt.grid(True,color='grey',alpha=0.3) plt.title("Home Team Goals") plt.show()

#客队 Away Team Goals away_team=world_cup_matches[['Year','Away Team Goals']].head(840).astype(int) plt.figure(figsize=(15,10)) sns.violinplot(x=away_team['Year'],y=away_team['Away Team Goals'],palette='Blues') plt.grid(True,color='grey',alpha=0.3) plt.title("Away Team Goals") plt.show()

#Home Team Name plt.figure(figsize=(15,10)) wordcloud=WordCloud( ).generate(' '.join(world_cup_matches['Home Team Name'].head(840))) plt.imshow(wordcloud,interpolation='bilinear') plt.axis('off') plt.show()

#Away Team Name plt.figure(figsize=(15,10)) wordcloud=WordCloud( ).generate(' '.join(world_cup_matches['Away Team Name'].head(840))) plt.imshow(wordcloud,interpolation='bilinear') plt.axis('off') plt.show()