Python fillna using mean of row values for selected columns

In the below table I need to only fillna for Week columns. NaN should be filled with mean value of all weeks in that row.

+----+---------+------+-------+-------+-------+-------+
| ID | Feature | Paid | Week1 | Week2 | Week3 | Week4 |
+----+---------+------+-------+-------+-------+-------+
| 1  | 1       | 1    | 12    | NaN   | NaN   | NaN   |   
+----+---------+------+-------+-------+-------+-------+
| 2  | 0       | 1    | 34    | 23    | NaN   | NaN   |
+----+---------+------+-------+-------+-------+-------+
| 3  | 1       | 0    | 24    | 13    | 14    | NaN   |
+----+---------+------+-------+-------+-------+-------+

Code

df.fillna(df[[Week1,Week2,Week3,Week4]].mean(axis=1),axis=1,inplace=True)

This gives an error saying NotImplementedError: Currently only can fill with dict/Series column by column

Solution

You can try via filter() select columns Named like 'Week' then find mean and store that into a variable(for good performance) and finally fill NaN's by using fillna():

cols=df.filter(regex='Week').columns
m=df[cols].mean(axis=1).round()
df=df.fillna({x:m for x in cols})

output:

    ID  Feature Paid    Week1       Week2   Week3   Week4
0   1       1       1       12      12.0    12.0    12.0
1   2       0       1       34      23.0    28.0    28.0
2   3       1       0       24      13.0    14.0    17.0