I’m having trouble with specifying a lambda function. I would like to have something like the lambda below, but not quite. The code should compare a rejected_time with any paid_out_time within the group and return True, if a rejected_time occurs within 5 minutes after any paid_out_time.

f = lambda x: ((x['rejected_time'].dropna() - x['paid_out_time'].dropna()).between(pd.Timedelta(0), pd.Timedelta(minutes=5)))

Using x['paid_out_time'].min() generates about 100k True values, but dropping .min() results in dramatic reduction. I can’t figure out how to use all paid_out_times in the comparison against a row-wise rejected_time and see if the rejected time occurs 0-5minutes after the paid_out_time.

I’ve been testing this code:

cols = ['paid_out_time', 'rejected_time']
df[cols] = df[cols].apply(pd.to_datetime, errors='coerce')

f = lambda x: ((x['rejected_time'].dropna() - x['paid_out_time'].dropna().min()).between(pd.Timedelta(0), pd.Timedelta(minutes=5)))

df['paid_out_auto_rejection'] = df.groupby('personal_id', group_keys=False).apply(f).astype(int)

Here’s some test data: