One way would be to use transform: >>> df name value 0 A 1 1 A NaN 2 B NaN 3 B 2 4 B 3 5 B 1 6 C 3 7 C NaN 8 C 3 >>> df[“value”] = df.groupby(“name”).transform(lambda x: x.fillna(x.mean())) >>> df name value 0 A 1 1 A 1 2 … Read more
To use mean values for numeric columns and the most frequent value for non-numeric columns you could do something like this. You could further distinguish between integers and floats. I guess it might make sense to use the median for integer columns instead. import pandas as pd import numpy as np from sklearn.base import TransformerMixin … Read more
One way would be to use transform: >>> df name value 0 A 1 1 A NaN 2 B NaN 3 B 2 4 B 3 5 B 1 6 C 3 7 C NaN 8 C 3 >>> df[“value”] = df.groupby(“name”).transform(lambda x: x.fillna(x.mean())) >>> df name value 0 A 1 1 A 1 2 … Read more