Add Column to GroupBy DataFrame Using Pandas Transform

When working with groupby operations in pandas, it's often useful to add a new column to the resulting dataframe. One method for accomplishing this is using the .map() function, as demonstrated in the example. However, an alternative and more straightforward approach is to employ the .transform() function.

.transform() allows us to apply a function to each group in the dataframe and return a Series with the results. The returned Series will have an index aligned with the original dataframe.

To illustrate, let's start with the provided dataframe:

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

Our goal is to count the values of type for each c and add a column with the size of c.

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

This code counts the values for each group and creates a new column named t.

To add the size column using .transform(), we can do the following:

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

.transform('size') applies the size function to each group, which returns the size of each group. The resulting Series is aligned with the index of the original dataframe, allowing us to add it as a new column to g.

The output will be a dataframe with an additional column named size:

   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

Using .transform() provides a more concise and straightforward way to add a column back to the original dataframe from a groupby aggregation.