Counting missing values in a Pandas loop

Question

• How can I run a loop in Pandas that returns a list that contains missing values as zeros?

The loop should take the values in the full_scores_list, check whether that value is in the Home_team_scores column. If so calculate and output the frequency, otherwise return zero.

I have a long list that has different scores for a team for two league seasons.

       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

How about something along the lines:

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

Improved according to comments:

Instead of the lambda-function, you can use a helper function for the apply() :

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

You can have more fine-grained control on the outputs and conditions with the helper() function.