.groupby & .fillna With Median

# Create a groupby object: by_sex_class by_sex_class = titanic.groupby(['sex','pclass']).count() # Write a function that imputes median def impute_median(series): return serie

Solution 1:

I will recommend using this

df['age'].fillna(df.groupby(["sex","pclass"])['age'].transform('median'),inplace=True)

Solution 2:

The new values are matched to the original dataframe by the index (when you group, you still keep the original index).

df['age'] = df.groupby(["sex","pclass"])['age'].transform(lambda x: x.fillna(x.median()))