玩转世界杯，用Python告诉你哪队是夺冠大热门-(附源代码)

睿江云计算

import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
df = pd.read_csv('results.csv')
df.head()

1、获取所有世界杯比赛的数据（不含预选赛）

df_FIFA_all = df[df['tournament'].str.contains('FIFA', regex=True)]
df_FIFA = df_FIFA_all[df_FIFA_all['tournament']=='FIFA World Cup']
df_FIFA.head()

结果如下：

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数据做一个初步整理

df_FIFA.loc[:,'date'] = pd.to_datetime(df_FIFA.loc[:,'date'])
df_FIFA['year'] = df_FIFA['date'].dt.year
df_FIFA['diff_score'] = df_FIFA['home_score']-df_FIFA['away_score']
df_FIFA['win_team'] = ''
df_FIFA['diff_score'] = pd.to_numeric(df_FIFA['diff_score'])

创建一个新的列数据，包含获胜队伍的信息

# The first method to get the winners
df_FIFA.loc[df_FIFA['diff_score']> 0, 'win_team'] = df_FIFA.loc[df_FIFA['diff_score']> 0, 'home_team']
df_FIFA.loc[df_FIFA['diff_score']< 0, 'win_team'] = df_FIFA.loc[df_FIFA['diff_score']< 0, 'away_team']
df_FIFA.loc[df_FIFA['diff_score']== 0, 'win_team'] = 'Draw'
df_FIFA.head()
# The second method to get the winners
def find_win_team(df):
    winners = []
    for i, row in df.iterrows():
        if row['home_score'] > row['away_score']:
            winners.append(row['home_team'])
        elif row['home_score'] < row['away_score']:
            winners.append(row['away_team'])
        else:
            winners.append('Draw')
    return winners
df_FIFA['winner'] = find_win_team(df_FIFA)
df_FIFA.head()

结果如下：

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2、获取世界杯所有比赛的前20强数据情况

2.1 获取世界杯所有比赛获胜场数最多的前20强数据

s = df_FIFA.groupby('win_team')['win_team'].count()
s.sort_values(ascending=False, inplace=True)
s.drop(labels=['Draw'], inplace=True)

用pandas可视化如下：

柱状图

s.head(20).plot(kind='bar', figsize=(10,6), title='Top 20 Winners of World Cup')

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水平柱状图

s.sort_values(ascending=True,inplace=True)
s.tail(20).plot(kind='barh', figsize=(10,6), title='Top 20 Winners of World Cup')

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饼图

s_percentage = s/s.sum()
s_percentage
s_percentage.tail(20).plot(kind='pie', figsize=(10,10), autopct='%.1f%%',
                           startangle=173, title='Top 20 Winners of World Cup', label='')

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分析结论1：

从赢球场数来看，巴西、德国、意大利、阿根廷四支球队实力最强。

通过上面的分析，我们还可以来查看部分国家的获胜情况

s.get('China', default = 'NA')
s.get('Japan', default = 'NA')
s.get('Korea DPR', default = 'NA')
s.get('Korea Republic', default = 'NA')
s.get('Egypt', default = 'NA')

运行结果分别是 ‘NA’，4，1，5，‘NA’。

从结果来看，中国队，在世界杯比赛上（不含预选赛）还没有赢过。当然，本次世界杯的黑马-埃及队，之前两度进入世界杯上，但也没有赢过~~

上面分析的是赢球场数的情况，下面我们来看下进球总数情况。

2.2 各个国家队进球总数量情况

df_score_home = df_FIFA[['home_team', 'home_score']]
column_update = ['team', 'score']
df_score_home.columns = column_update
df_score_away = df_FIFA[['away_team', 'away_score']]
df_score_away.columns = column_update
df_score = pd.concat([df_score_home,df_score_away], ignore_index=True)
s_score = df_score.groupby('team')['score'].sum()
s_score.sort_values(ascending=False, inplace=True)
s_score.sort_values(ascending=True, inplace=True)
s_score.tail(20).plot(kind='barh', figsize=(10,6), title='Top 20 in Total Scores of World Cup')

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team_list = ['Russia', 'Germany', 'Brazil', 'Portugal', 'Argentina', 'Belgium', 'Poland', 'France',
             'Spain', 'Peru', 'Switzerland', 'England', 'Colombia', 'Mexico', 'Uruguay', 'Croatia',
            'Denmark', 'Iceland', 'Costa Rica', 'Sweden', 'Tunisia', 'Egypt', 'Senegal', 'Iran',
            'Serbia', 'Nigeria', 'Australia', 'Japan', 'Morocco', 'Panama', 'Korea Republic', 'Saudi Arabia']
for item in team_list:
    if item not in s_score.index:
        print(item)
out:
Iceland
Panama

通过上述分析可知，冰岛队和巴拿马队是首次打入世界杯的。

由于冰岛队和巴拿马队是首次进入世界杯，所以这里的32强数据，事实上是没有这两支队伍的历史数据的。

df_top32 = df_FIFA[(df_FIFA['home_team'].isin(team_list))&(df_FIFA['away_team'].isin(team_list))]

3.1 自1872年以来，32强数据情况

赢球场数情况

s_32 = df_top32.groupby('win_team')['win_team'].count()
s_32.sort_values(ascending=False, inplace=True)
s_32.drop(labels=['Draw'], inplace=True)
s_32.sort_values(ascending=True,inplace=True)
s_32.plot(kind='barh', figsize=(8,12), title='Top 32 of World Cup since year 1872')

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进球数据情况

df_score_home_32 = df_top32[['home_team', 'home_score']]
column_update = ['team', 'score']
df_score_home_32.columns = column_update
df_score_away_32 = df_top32[['away_team', 'away_score']]
df_score_away_32.columns = column_update
df_score_32 = pd.concat([df_score_home_32,df_score_away_32], ignore_index=True)
s_score_32 = df_score_32.groupby('team')['score'].sum()
s_score_32.sort_values(ascending=False, inplace=True)
s_score_32.sort_values(ascending=True, inplace=True)
s_score_32.plot(kind='barh', figsize=(8,12), title='Top 32 in Total Scores of World Cup since year 1872')