CODEWITHSUNDEEP
pythonpandasdataframegroup-by
Ger
Ger

Reputation: 9736

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 ?

Upvotes: 23

Views: 29564

Answers (1)

MaxU - stand with Ukraine
MaxU - stand with Ukraine

Reputation: 210822

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

Upvotes: 34

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