I heard that Mean, Median isn't the best way to impute the missing values, why would that be?
In my scenario, I have data like this
Brand|Value A|2, A|NaN, A|4, B|8, B|NaN, B|10, C|9, C|11
if using mean imputation the data would be
Brand|Value A|2, A|7.3, A|4, B|8, B|7.3, B|10, C|9, C|11
which does make sense for brand B to be 7.3 but doesn't make sense if brand A 7.3 because the value of Brand A has its tendency somewhere around 2 and 8 is there any other way to fill the missing values based on the Brand?
This is an example of data with only 2 features, with 1 feature that may has pattern for missing values, what if there are like 20 features, and there would be more than one features that may have pattern to better define the missing values.
How to apply this in Python?