CODEWITHSUNDEEP
pythongroup-bypandas
Simon Righley
Simon Righley

Reputation: 4969

python pandas groupby() result

I have the following python pandas data frame:

df = pd.DataFrame( {
   'A': [1,1,1,1,2,2,2,3,3,4,4,4],
   'B': [5,5,6,7,5,6,6,7,7,6,7,7],
   'C': [1,1,1,1,1,1,1,1,1,1,1,1]
    } );

df
    A  B  C
0   1  5  1
1   1  5  1
2   1  6  1
3   1  7  1
4   2  5  1
5   2  6  1
6   2  6  1
7   3  7  1
8   3  7  1
9   4  6  1
10  4  7  1
11  4  7  1

I would like to have another column storing a value of a sum over C values for fixed (both) A and B. That is, something like:

    A  B  C  D
0   1  5  1  2
1   1  5  1  2
2   1  6  1  1
3   1  7  1  1
4   2  5  1  1
5   2  6  1  2
6   2  6  1  2
7   3  7  1  2
8   3  7  1  2
9   4  6  1  1
10  4  7  1  2
11  4  7  1  2

I have tried with pandas groupby and it kind of works:

res = {}
for a, group_by_A in df.groupby('A'):
    group_by_B = group_by_A.groupby('B', as_index = False)
    res[a] = group_by_B['C'].sum()

but I don't know how to 'get' the results from res into df in the orderly fashion. Would be very happy with any advice on this. Thank you.

Upvotes: 27

Views: 58589

Answers (4)

Mohsen
Mohsen

Reputation: 31

you can use this method :

columns = ['col1','col2',...]
df.groupby('col')[columns].sum()

if you want you can also use .sort_values(by = 'colx', ascending = True/False) after .sum() to sort the final output by a specific column (colx) and in an ascending or descending order.

Upvotes: 2

DrTRD
DrTRD

Reputation: 1718

You could also do a one liner using transform applied to the groupby:

df['D'] = df.groupby(['A','B'])['C'].transform('sum')

Upvotes: 15

andrew
andrew

Reputation: 1843

You could also do a one liner using merge as follows:

df = df.merge(pd.DataFrame({'D':df.groupby(['A', 'B'])['C'].size()}), left_on=['A', 'B'], right_index=True)

Upvotes: 8

Andy Hayden
Andy Hayden

Reputation: 375415

Here's one way (though it feels this should work in one go with an apply, I can't get it).

In [11]: g = df.groupby(['A', 'B'])

In [12]: df1 = df.set_index(['A', 'B'])

The size groupby function is the one you want, we have to match it to the 'A' and 'B' as the index:

In [13]: df1['D'] = g.size()  # unfortunately this doesn't play nice with as_index=False
# Same would work with g['C'].sum()

In [14]: df1.reset_index()
Out[14]:
    A  B  C  D
0   1  5  1  2
1   1  5  1  2
2   1  6  1  1
3   1  7  1  1
4   2  5  1  1
5   2  6  1  2
6   2  6  1  2
7   3  7  1  2
8   3  7  1  2
9   4  6  1  1
10  4  7  1  2
11  4  7  1  2

Upvotes: 26

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