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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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