計算熊貓循環中的缺失值

Question

• 如何在Pandas中運行循環，該循環返回包含零值的缺失值的列表？

循環應使用full_scores_list中的值，檢查該值是否在Home_team_scores列中。 如果這樣，則計算並輸出頻率，否則返回零。

我的名單很長，在兩個賽季中一支球隊的得分不同。

       My code is:
       all_scores = []
       for score in range(0,len(full_scores_list)):
           if full_scores_list[score] == '0 0':
                    all_scores.append(data1.Home_team_scores.value_counts()['0 0'])
           elif full_scores_list[score] == '0 1':
                   all_scores.append(data1.Home_team_scores.value_counts()['0     1'])
           elif full_scores_list[score] == '0 2':
                all_scores.append(data1.Home_team_scores.value_counts()['0 2']) 
               all_scores.append(data1.Home_team_scores.value_counts()['0 2'])
           elif full_scores_list[score] == '0 3':
               all_scores.append(data1.Home_team_scores.value_counts()['0 3'])
           elif full_scores_list[score] == '0 4':
               all_scores.append(data1.Home_team_scores.value_counts()['0 4'])
           elif full_scores_list[score] == '0 5':
               all_scores.append(data1.Home_team_scores.value_counts()['0 5'])  
           if full_scores_list[score] == '1 0':
                all_scores.append(data1.Home_team_scores.value_counts()['1 0'])
           elif full_scores_list[score] == '1 1':
               all_scores.append(data1.Home_team_scores.value_counts()['1 1'])
           elif full_scores_list[score] == '1 2':
               all_scores.append(data1.Home_team_scores.value_counts()['1 2'])
           elif full_scores_list[score] == '1 3':
               all_scores.append(data1.Home_team_scores.value_counts()['1 3'])
           elif full_scores_list[score] == '1 4':
               all_scores.append(data1.Home_team_scores.value_counts()['1 4'])
           elif full_scores_list[score] == '1 5':
               all_scores.append(data1.Home_team_scores.value_counts()['1 5'])


       all_scores

Answer 1

大致情況如何：

com_vals = df['Home_team_scores'].unique()
df['full_scores_list'].apply(lambda v: v in com_vals)

根據評論改進：

除了lambda函數，您可以對apply()使用輔助函數：

com_vals = df['Home_team_scores'].unique()
def helper():
  return v in com_vals
df['full_scores_list'].apply(helper)

您可以使用helper()函數對輸出和條件進行更細粒度的控制。