Ger Ger - 1 month ago 12
Python Question

How to do a pandas groupby operation on one column but keep the other in the resulting dataframe

My question is about groupby operation with pandas. I have the following DataFrame :

In [4]: df = pd.DataFrame({"A": range(4), "B": ["PO", "PO", "PA", "PA"], "C": ["Est", "Est", "West", "West"]})

In [5]: df
Out[5]:
A B C
0 0 PO Est
1 1 PO Est
2 2 PA West
3 3 PA West


This is what I would like to do : I want to group by column B and do a sum on column A. But at the end, I would like column C to still be in the DataFrame. If I do :

In [8]: df.groupby(by="B").aggregate(pd.np.sum)
Out[8]:
A
B
PA 5
PO 1


It does the job but column C is missing. I can also do this :

In [9]: df.groupby(by=["B", "C"]).aggregate(pd.np.sum)
Out[9]:
A
B C
PA West 5
PO Est 1


or

In [11]: df.groupby(by=["B", "C"], as_index=False).aggregate(pd.np.sum)
Out[11]:
B C A
0 PA West 5
1 PO Est 1


But in both cases it group by B AND C and not just B and keeps the C value. Is what I want to do irrelevant or is there a way to do it ?

Answer

try to use DataFrameGroupBy.agg() method with dict of {column -> function}:

In [6]: df.groupby('B').agg({'A':'sum', 'C':'first'})
Out[6]:
       C  A
B
PA  West  5
PO   Est  1

From docs:

Function to use for aggregating groups. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. If passed a dict, the keys must be DataFrame column names.

or something like this depending on your goals:

In [8]: df = pd.DataFrame({"A": range(4), "B": ["PO", "PO", "PA", "PA"], "C": ["Est1", "Est2", "West1", "West2"]})

In [9]: df.groupby('B').agg({'A':'sum', 'C':'first'})
Out[9]:
        C  A
B
PA  West1  5
PO   Est1  1

In [10]: df['sum_A'] = df.groupby('B')['A'].transform('sum')

In [11]: df
Out[11]:
   A   B      C  sum_A
0  0  PO   Est1      1
1  1  PO   Est2      1
2  2  PA  West1      5
3  3  PA  West2      5
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