How to convert iterrows() into lambda function in pandas?

Question

%%time
match_ids_dict = {}

for index ,row in df_match.iterrows():
team1 = row\['team1'\] + ' Vs ' + row\['team2'\]
team2 = row\['team2'\] + ' Vs ' + row\['team1'\]
match_ids_dict\[team1\] = row\['match_id'\]
match_ids_dict\[team2\] = row\['match_id'\]

match_ids_dict

I want more efficient method of this problem?

Answer 1

I tried creating example data, hopefully its the situation you're in. You can use dict and zip to make a dictionary from your columns without having to do the slow loop. The update line is adding the team2 - team1 data to the same dict as the team1 - team2 info

df_match = pd.DataFrame({
    'team1':['A','B','C','D'],
    'team2':['E','F','G','H'],
    'match_id':[1,2,3,4],
})

match_ids_dict = dict(zip(df_match['team1']+' Vs '+df_match['team2'], df_match['match_id']))
match_ids_dict.update(dict(zip(df_match['team2']+' Vs '+df_match['team1'], df_match['match_id'])))