Imputation of missing values for categories in pandas

Question

The question is how to fill NaNs with most frequent levels for category column in pandas dataframe?

In R randomForest package there is na.roughfix option : A

Answer 1

In more recent versions of scikit-learn up you can use SimpleImputer to impute both numerics and categoricals:

import pandas as pd
from sklearn.impute import SimpleImputer
arr = [[1., 'x'], [np.nan, 'y'], [7., 'z'], [7., 'y'], [4., np.nan]]
df1 = pd.DataFrame({'x1': [x[0] for x in arr],
                    'x2': [x[1] for x in arr]},
                  index=[l for l in 'abcde'])
imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent')
print(pd.DataFrame(imp.fit_transform(df1),
                   columns=df1.columns,
                   index=df1.index))
#   x1 x2
# a  1  x
# b  7  y
# c  7  z
# d  7  y
# e  4  y