Pandas Groupby Lambda函数具有多个条件/列

Question

I am trying to create a new column that groups df by Deal and Month , and applies a percentage (9%) to the Amount column. If all the Amount values for a particular Deal in a particular month add up to 20,000 then apply the percentage to the Amount ; otherwise, if the TYPE is MONTHLY , and the individual Amount is at least 1500, apply the percentage to the Amount ; failing that, multiply by 0.

df.groupby(['Deal', 'Month'])["Amount"].apply(
    lambda x: x.sum() * 0.09 if x.sum() >= 20000 else (
        x * 0.09 if x >= 1500 and x['TYPE'] == 'MONTHLY' else 0
    )
)

This is what I tried but keep getting errors such as ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). OR KeyError: ('TYPE', u'occurred at index 0') etc. I've tried using transform instead of apply too. Would greatly appreciate any help.

This if what my grouped DF looks like + Desired Column

   Deal         TYPE    Month        Amount   Desired Column
0   Com A   ANNUAL  April   10021.34   0
1   Com A   MONTHLY April   35.86 .    0
2   Com B   MONTHLY April   11150.05   1,003.50
3   Com B   ANNUAL  July    661.65     0
4   Com B   ANNUAL  August  303.63     0
5   Com C   ANNUAL  April   25624.59   2,306.21
6   Com D   ANNUAL  June    27309.26   2,457.83  
7   Com D   ANNUAL  July    0.00       0
8   Com D   ANNUAL  August  0.00       0
9   Com E   ANNUAL  April   10.65      0
10  Com E   MONTHLY May     0.00       0
11  Com E   ANNUAL  May     18716.70   1,684.5
12  Com E   MONTHLY June    0.00       0
13  Com E   ANNUAL  June    606.49     0
14  Com E   MONTHLY July    0.00       0
15  Com E   MONTHLY July    8890.17    800.11
16  Com E   MONTHLY August  4000       0
17  Com E   ANNUAL  August  16000      1,800
18  Com E   ANNUAL  September 2157.34  0
19  Com E   ANNUAL  October 3025.24    0

df

Answer 1

You don't need a groupby in this case. There are a couple of ways to do it, conceptually easiest is to first calculate the threshold based on whether it is a monthly amount or an annual one

df['Threshold'] = (df.TYPE=='ANNUAL')*20000 + (df.TYPE=='MONTHLY')*1500

Then you can calculate the amount based on whether the threshold has been met

df['Desired Amount'] = (df.Amount>df.Threshold)*0.09*df.Amount

But this works here because you don't have multiple rows for the same deal, month, and type. If you did then you would first need the groupby to aggregate by all of these

df = df.groupby(['Deal','Month','TYPE']).sum()
df.reset_index(inplace=True)

Then you can proceed as above.

Answer 2

I tried to translate your description into this:

df['Sum'] = df.groupby(['Deal','Month'])['Amount'].transform('sum')

df['Desired Column'] = np.where(df['Sum'] > 20000, df['Sum'] * 0.09, np.where((df['Amount'] >= 1500) & (df['TYPE'] == 'MONTHLY'), df['Amount'] * 0.09, 0))

Though I found some differences between the result I generated and the "Desired Column" you posted, eg in row 16, it's monthly and has amount greater than 1500, so the result should have been 0.09 * 4000 = 360, not sure how you got 0. I guess either you made a mistake during manual calculation or probably I misunderstood your description, please feel free to explain it so that I can update my script, but I guess the general idea should have solved your problem.

PS the result df after running my script

   Deal     TYPE      Month    Amount       Sum  Desired Column
0     A   ANNUAL      April  10021.34  10057.20          0.0000
1     A  MONTHLY      April     35.86  10057.20          0.0000
2     B  MONTHLY      April  11150.05  11150.05       1003.5045
3     B   ANNUAL       July    661.65    661.65          0.0000
4     B   ANNUAL     August    303.63    303.63          0.0000
5     C   ANNUAL      April  25624.59  25624.59       2306.2131
6     D   ANNUAL       June  27309.26  27309.26       2457.8334
7     D   ANNUAL       July      0.00      0.00          0.0000
8     D   ANNUAL     August      0.00      0.00          0.0000
9     E   ANNUAL      April     10.65     10.65          0.0000
10    E  MONTHLY        May      0.00  18716.70          0.0000
11    E   ANNUAL        May  18716.70  18716.70          0.0000
12    E  MONTHLY       June      0.00    606.49          0.0000
13    E   ANNUAL       June    606.49    606.49          0.0000
14    E  MONTHLY       July      0.00   8890.17          0.0000
15    E  MONTHLY       July   8890.17   8890.17        800.1153
16    E  MONTHLY     August   4000.00  18000.00        360.0000
17    E   ANNUAL     August  14000.00  18000.00          0.0000
18    E   ANNUAL  September   2157.34   2157.34          0.0000
19    E   ANNUAL    October   3025.24   3025.24          0.0000