Pandas GroupBy apply all

Question

I have a situation. Let me say that I have the following example dataframe loans:

test_df = pd.DataFrame({'name': ['Jack','Jill','John','Jack','Jill'],
                   'date': ['2016-08-08','2016-08-08','2016-08-07','2016-08-08','2016-08-08'],
                   'amount': [1000.0,1500.0,2000.0,2000.0,3000.0],
                   'return_amount': [5000.0,2000.0,3000.0,0.0,0.0],
                   'return_date': ['2017-08-08','2017-08-08','2017-08-07','','2017-08-08']})

test_df.head()

    amount  date        name    return_amount   return_date
0   1000.0  2016-08-08  Jack    5000.0          2017-08-08
1   1500.0  2016-08-08  Jill    2000.0          2017-08-08
2   2000.0  2016-08-07  John    3000.0          2017-08-07
3   2500.0  2016-08-08  Jack    0.0
4   2500.0  2016-08-08  Jill    0.0             2017-08-08

There are several operations that I need to perform after grouping this frame by name (grouping loans per person):

1) return amountmust be distributed in proportion to the amount amount.

2) If there return dateis ANY for a given person for a loan , then all return_dates should be converted to empty lines. ''

I already have a function that I use to distribute the proportional amount of the return:

def allocate_return_amount(group):
    loan_amount = group['amount']
    return_amount = group['return_amount']
    sum_amount = loan_amount.sum()
    sum_return_amount = return_amount.sum()
    group['allocated_return_amount'] = (loan_amount/sum_amount) * sum_return_amount
    return group

And I use grouped_test_df = grouped_test_df.apply(allocate_return_amount)to apply it.

, , - , , , - return_date, , return_dates '..

GroupBy.all pandas , , , - ?

, :

ideal_test_df.head()

    amount  date        name    return_amount   return_date
0   1000.0  2016-08-08  Jack    0.0             ''
1   1500.0  2016-08-08  Jill    666.66          2017-08-08
2   2000.0  2016-08-07  John    3000.0          2017-08-07
3   2500.0  2016-08-08  Jack    0.0             ''
4   2500.0  2016-08-08  Jill    1333.33         2017-08-08

, pandas, , !

Answer 1

, , any, loc:

test_df = pd.DataFrame({'name': ['Jack','Jill','John','Jack','Jill'],
                   'date': ['2016-08-08','2016-08-08','2016-08-07','2016-08-08','2016-08-08'],
                   'amount': [1000.0,1500.0,2000.0,2000.0,3000.0],
                   'return_amount': [5000.0,2000.0,3000.0,0.0,0.0],
                   'return_date': ['2017-08-08','2017-08-08','2017-08-07','','2017-08-08']})

grouped = test_df.groupby('name')

for name, group in grouped:
    if any(group['return_date'] == ''):
        test_df.loc[group.index,'return_date'] = ''

reset return_amount , :

test_df.loc[group.index, 'return_amount'] = 0