Python pandas groupby then transform based on condition in another column

I have the following DataFrame.

   name  score  cond
0    A     10    0
1    A      5    1
2    A     20    2
3    A     30    3
4    B     10    0
5    B     20    0
6    B     30    4
7    B     40    6

What I want to do is group based on name, select the rows with cond == 0, then calculate the average score for those rows and assign that value to a new column for every member of that group.

The end result should look like this.

    name  score  cond group_avg
0    A     10     0       10
1    A      5     1       10
2    A     20     2       10
3    A     30     3       10
4    B     10     0       15
5    B     20     0       15
6    B     30     4       15
7    B     40     6       15

I have been looking into groupby, transform, and filter but I cannot figure out the right combination to use.

>Solution :

Just mask the unwanted values before groupby.transform:

df['group_avg'] = (df['score'].where(df['cond'].eq(0))
                   .groupby(df['name']).transform('mean')
                  )

Alternatively, filter, aggregate, map:

df['group_avg'] = df['name'].map(df.loc[df['cond'].eq(0)]
                                   .groupby('name')['score'].mean())

Output:

  name  score  cond  group_avg
0    A     10     0       10.0
1    A      5     1       10.0
2    A     20     2       10.0
3    A     30     3       10.0
4    B     10     0       15.0
5    B     20     0       15.0
6    B     30     4       15.0
7    B     40     6       15.0

Intermediate:

df['score'].where(df['cond'].eq(0))

0    10.0
1     NaN
2     NaN
3     NaN
4    10.0
5    20.0
6     NaN
7     NaN
Name: score, dtype: float64