Python 什么是可视化头对头记录的最佳方式？

我有一个如下所示的数据框：

A = pd.DataFrame({'team':[1,2,3,2,1,1,3,4,1,2], 'opp_team':[2,1,2,3,3,4,1,1,2,1], 'result':[1,0,1,0,1,1,0,0,1,0]})

结果列有1表示胜利，0表示失败。我想找出在图表上显示正面记录的最佳方法

我在考虑一个双人阴谋，但我认为它不会起作用，因为它不会显示胜利和失败的计数。在上面的例子中，第1队与第2队进行了2次比赛，两次都赢了。因此，图表也应该显示计数

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我能试着解决这个问题吗？

只需在两个单独的图中显示它们，一个图显示正面对正面记录的总数，另一个图显示一个团队对另一个团队的总获胜率（另一个团队的获胜率=团队的失败率）

要做到这一点，我认为需要重新构造数据帧，使其每行仅显示1个游戏ID。为了便于分组，对

团队

和

opp_团队

进行排序，使

团队

的索引始终小于

opp_团队

我生成了一个示例数据集，我将如何总结和绘制它，供您参考：

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

def generate_data(num_data=100, num_teams=4):

    team = np.random.randint(1,1 + num_teams,num_data)
    opp_team = np.random.randint(1,1 + num_teams,num_data)

    while len(opp_team[opp_team == team]) > 0:
        opp_team[opp_team == team] = np.random.randint(1,1 + num_teams,len(opp_team[opp_team == team]))

    results = np.round(np.random.rand(100))    

    return team, opp_team, results

def reorder_team(team, opp_team, result):

    if team > opp_team:
        team, opp_team = opp_team, team
        result = 1 - result

    return team, opp_team, result

# Generate data and get summary by team match-ups
team, opp_team, results = generate_data()

df = pd.DataFrame(data={'team':team,'opp_team':opp_team,'results':results}, dtype='int')
df = pd.DataFrame(df.apply(lambda x : reorder_team(x['team'], x['opp_team'], x['results']),axis=1).to_list(), 
                  columns=df.columns)
df[['team','opp_team']] = 'Team ' + df[['team','opp_team']].astype(str)

df_summary = df.groupby(['team','opp_team']).agg(['sum','count'])
df_summary.columns = ['wins', 'total']
df_summary.reset_index(inplace=True)
df_summary['team_winrate'] = (df_summary['wins'] / df_summary['total'])
df_summary['opp_team_winrate'] = 1 - df_summary['team_winrate']

这将产生：

您可以使用以下脚本绘制它们（或使用您喜爱的库编写自己的脚本）：

fig, (ax_count, ax_win) = plt.subplots(1,2, figsize=(12,6))

y_locs = list(range(len(df_summary)))

ax_count.barh(y_locs, width=df_summary['total'], color='tab:gray')
ax_count.set_yticks(y_locs)
ax_count.set_yticklabels(df_summary['team'] + ' VS ' + df_summary['opp_team'])
ax_count.set_title('Total No. of Match Ups')
ax_count.set_xticks([])

for loc in ['top','left','right','bottom']:
    ax_count.spines[loc].set_visible(False)

for p in ax_count.patches:
    ax_count.annotate(f'{p.get_width()}',
                      (p.get_x() + p.get_width(), p.get_y() + p.get_height()/2.), 
                      ha='right', va='center', xytext=(-5,0), textcoords='offset points', 
                      color='white',fontweight='heavy')

ax_win.barh(y_locs, width=df_summary['team_winrate'], color='tab:blue')
ax_win2 = ax_win.twinx()
ax_win2.barh(y_locs, width=df_summary['opp_team_winrate'], 
             left=df_summary['team_winrate'], color='tab:red')

ax_win.set_yticks(y_locs)
ax_win.set_yticklabels(df_summary['team'])
ax_win2.set_yticks(y_locs)
ax_win2.set_yticklabels(df_summary['opp_team'])

ax_win.set_xlim(0,1)
ax_win.set_title('Winning Rate')
ax_win.set_xticks([])

for loc in ['top','left','right','bottom']:
    ax_win.spines[loc].set_visible(False)
    ax_win2.spines[loc].set_visible(False)

for p in ax_win.patches:
    ax_win.annotate(f'{p.get_width() * 100 :.0f} %',
                      (0, p.get_y() + p.get_height()/2.), 
                      ha='left', va='center', xytext=(10,0), textcoords='offset points', 
                      color='white',fontweight='heavy')

    ax_win2.annotate(f'{(1 - p.get_width()) * 100 :.0f} %',
                      (1, p.get_y() + p.get_height()/2.), 
                      ha='right', va='center', xytext=(-10,0), textcoords='offset points', 
                      color='white',fontweight='heavy')

plt.show()