代码讲解 def matches(g_matches): g_matches.insert(2, 'potential1', g_matches['country1'].map(squad_stats.set_index('nationality_name')['potential'])) g_matches.insert(3, 'potential2', g_matches['country2'].map(squad_stats.set_index('nationality_name')['potential'])) g_matches.insert(4, 'rank1', g_matches['country1'].map(last_team_scores.set_index('team')['rank'])) g_matches.insert(5, 'rank2', g_matches['country2'].map(last_team_scores.set_index('team')['rank'])) pred_set = [] for index, row in g_matches.iterrows(): if row['potential1'] > row['potential2'] and abs(row['potential1'] - row['potential2']) > 2: pred_set.append({'Team1': row['country1'], 'Team2': row['country2']}) elif row['potential2'] > row['potential1'] and abs(row['potential2'] - row['potential1']) > 2: pred_set.append({'Team1': row['country2'], 'Team2': row['country1']}) else: if row['rank1'] > row['rank2']: pred_set.append({'Team1': row['country1'], 'Team2': row['country2']}) else: pred_set.append({'Team1': row['country2'], 'Team2': row['country1']}) pred_set = pd.DataFrame(pred_set) pred_set.insert(2, 'Team1_FIFA_RANK', pred_set['Team1'].map(last_team_scores.set_index('team')['rank'])) pred_set.insert(3, 'Team2_FIFA_RANK', pred_set['Team2'].map(last_team_scores.set_index('team')['rank'])) pred_set.insert(4, 'Team1_Goalkeeper_Score', pred_set['Team1'].map(last_team_scores.set_index('team')['goalkeeper_score'])) pred_set.insert(5, 'Team2_Goalkeeper_Score', pred_set['Team2'].map(last_team_scores.set_index('team')['goalkeeper_score'])) pred_set.insert(6, 'Team1_Defense', pred_set['Team1'].map(last_team_scores.set_index('team')['defense_score'])) pred_set.insert(7, 'Team1_Offense', pred_set['Team1'].map(last_team_scores.set_index('team')['offense_score'])) pred_set.insert(8, 'Team1_Midfield', pred_set['Team1'].map(last_team_scores.set_index('team')['midfield_score'])) pred_set.insert(9, 'Team2_Defense', pred_set['Team2'].map(last_team_scores.set_index('team')['defense_score'])) pred_set.insert(10, 'Team2_Offense', pred_set['Team2'].map(last_team_scores.set_index('team')['offense_score'])) pred_set.insert(11, 'Team2_Midfield', pred_set['Team2'].map(last_team_scores.set_index('team')['midfield_score'])) return pred_set

这是一个函数，它的输入是一个包含两个国家名称的 DataFrame，名为 `g_matches`。函数首先用 `map()` 函数将 `squad_stats` 中的 `potential` 列添加到 `g_matches` 中，作为两支球队的潜力值，分别在第 2 和第 3 列。然后，函数使用 `map()` 函数将 `last_team_scores` 中的 `rank` 列添加到 `g_matches` 中，作为两支球队的排名，分别在第 4 和第 5 列。接下来，函数创建一个空列表 `pred_set`，并遍历 `g_matches` 中的每一行，根据条件将预测结果添加到 `pred_set` 列表中。如果第一支球队的潜力值高于第二支球队并且两者之间的差异大于 2，则将第一支球队预测为获胜方；否则，如果第二支球队的潜力值高于第一支球队并且两者之间的差异大于 2，则将第二支球队预测为获胜方。如果两支球队的潜力值差异不足 2，则将预测结果基于排名进行决定。接下来，函数将 `pred_set` 列表转换为 DataFrame，并使用 `map()` 函数将 `last_team_scores` 中的其他列添加到 DataFrame 中，包括两支球队的 FIFA 排名、门将得分、防守得分、进攻得分和中场得分。最后，函数返回包含预测结果和球队评分的 DataFrame。