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pythonpandasdataframegroup-bypandas-groupby
ARASH
ARASH

Reputation: 428

How to groupby based on two columns in pandas?

A similar question might have been asked before, but I couldn't find the exact one fitting to my problem. I want to group by a dataframe based on two columns. For exmaple to make this 

id product quantity
1  A       2
1  A       3
1  B       2
2  A       1
2  B       1
3  B       2
3  B       1

Into this:

id product quantity
1  A       5
1  B       2
2  A       1
2  B       1
3  B       3

Meaning that summation on "quantity" column for same "id" and same "product".

Upvotes: 15

Views: 48732

Answers (3)

Shubham R
Shubham R

Reputation: 7644

You can use groupby and aggregate function

import pandas as pd
df = pd.DataFrame({
    'id': [1,1,1,2,2,3,3],
    'product': ['A','A','B','A','B','B','B'],
    'quantity': [2,3,2,1,1,2,1]
})

print df

   id  product  quantity
0   1     A      2
1   1     A      3
2   1     B      2
3   2     A      1
4   2     B      1
5   3     B      2
6   3     B      1


df = df.groupby(['id','product']).agg({'quantity':'sum'}).reset_index()
print df

    id  product  quantity
0   1     A       5
1   1     B       2
2   2     A       1
3   2     B       1
4   3     B       3

Upvotes: 4

jezrael
jezrael

Reputation: 862406

You need groupby with parameter as_index=False for return DataFrame and aggregating mean:

df = df.groupby(['id','product'], as_index=False)['quantity'].sum()
print (df)
   id product  quantity
0   1       A         5
1   1       B         2
2   2       A         1
3   2       B         1
4   3       B         3

Or add reset_index:

df = df.groupby(['id','product'])['quantity'].sum().reset_index()
print (df)
   id product  quantity
0   1       A         5
1   1       B         2
2   2       A         1
3   2       B         1
4   3       B         3

Upvotes: 25

piRSquared
piRSquared

Reputation: 294218

You can use pivot_table with aggfunc='sum'

df.pivot_table('quantity', ['id', 'product'], aggfunc='sum').reset_index()

   id product  quantity
0   1       A         5
1   1       B         2
2   2       A         1
3   2       B         1
4   3       B         3

Upvotes: 7

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