Groupby.mean() if condition is true

I got the following dataframe:

   index  user  default_shipping_cost     category  shipping_cost  shipping_coalesce  estimated_shipping_cost0      0     1                      1      clothes            NaN                1.0                      6.01      1     1                      1  electronics            2.0                2.0                      6.02      2     1                     15    furniture            NaN               15.0                      6.03      3     2                     15    furniture            NaN               15.0                     15.04      4     2                     15    furniture            NaN               15.0                     15.0

Per user, combine shipping_cost with default_shipping_cost and calculate the mean of the combined shipping_costs but only if there is at least one shipping_cost given.

Explanation:

• user_1 shipping_cost is given (at least once) so we can calculate the mean
• user_2 there are no shipping_cost, so I would like to go with Nan

Code:

import pandas as pdpd.set_option("display.max_columns", None)pd.set_option("display.max_rows", None)pd.set_option('display.width', 1000)df = pd.DataFrame(    {'user': [1, 1, 1, 2, 2],'default_shipping_cost': [1, 1, 15, 15, 15],'category': ['clothes', 'electronics', 'furniture', 'furniture', 'furniture'],'shipping_cost': [None, 2, None, None, None]    })df.reset_index(inplace=True)df['shipping_coalesce'] = df.shipping_cost.combine_first(df.default_shipping_cost)dfg_user = df.groupby(['user'])df['estimated_shipping_cost'] = dfg_user['shipping_coalesce'].transform("mean")print(df)

Expected output:

   index  user  default_shipping_cost     category  shipping_cost  shipping_coalesce  estimated_shipping_cost0      0     1                      1      clothes            NaN                1.0                      6.01      1     1                      1  electronics            2.0                2.0                      6.02      2     1                     15    furniture            NaN               15.0                      6.03      3     2                     15    furniture            NaN               15.0                      NaN4      4     2                     15    furniture            NaN               15.0                      NaN