How to Add a Column to a Grouped DataFrame After Groupby Operations in Pandas?

Add Column to Grouped DataFrame in pandas

When working with GroupBy operations in pandas, it can be beneficial to add additional information to the resulting dataframe. This article explores a question regarding how to efficiently add a column to a grouped dataframe after performing groupby aggregations.

Consider the following dataframe:

df = pd.DataFrame({'c':[1,1,1,2,2,2,2],'type':['m','n','o','m','m','n','n']})

The goal is to count the values of the 'type' column for each value of 'c', and add a new column to the grouped dataframe representing the 'size' of each 'c' group. After performing the groupby aggregation:

g = df.groupby('c')['type'].value_counts().reset_index(name='t')

the dataframe 'g' now contains the count of 'type' for each 'c':

   c type  t
0  1    m  1
1  1    n  1
2  1    o  1
3  2    m  2
4  2    n  2

To add the 'size' column, one option is to use the map function:

a.index = a['c']
g['size'] = g['c'].map(a['size'])

However, there is a more straightforward approach using the transform function:

g['size'] = df.groupby('c')['type'].transform('size')

Using transform, the size column can be added directly to the 'g' dataframe, aligning the index with the original dataframe. The resulting dataframe:

   c type  t  size
0  1    m  1     3
1  1    n  1     3
2  1    o  1     3
3  2    m  2     4
4  2    n  2     4

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