I have data on movies and all movies have IMDB score, however some do not have a meta critic score
Eg:
| Name | IMDB Score | Meta Score |
|---|---|---|
| B | 8 | 86 |
| C | 8 | 90 |
| D | 8 | null |
| E | 8 | 91 |
| F | 7 | 66 |
| G | 3 | 44 |
I want to fill in the null values in the meta critic score with the mean of the values of movies that have the same IMDB score
so the null value in this table should be replaced by the mean of movies B,C,E
How would I achieve this with Numpy / Pandas?
I looked up online and the closest solution I could find was averaging all the metacritic scores and replacing the null values with that Average.
>Solution :
groupby + fillna
df.groupby('IMDB Score')['Meta Score'].apply(lambda x: x.fillna(x.mean()))
output:
0 86.0
1 90.0
2 89.0
3 91.0
4 66.0
5 44.0
Name: Meta Score, dtype: float64
make result to Meta Score column