Impute categorical missing values in scikit-learn

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

class DataFrameImputer(TransformerMixin):

    def __init__(self):
        """Impute missing values.

        Columns of dtype object are imputed with the most frequent value 
        in column.

        Columns of other types are imputed with mean of column.

        """
    def fit(self, X, y=None):

        self.fill = pd.Series([X[c].value_counts().index[0]
            if X[c].dtype == np.dtype('O') else X[c].mean() for c in X],
            index=X.columns)

        return self

    def transform(self, X, y=None):
        return X.fillna(self.fill)

data = [
    ['a', 1, 2],
    ['b', 1, 1],
    ['b', 2, 2],
    [np.nan, np.nan, np.nan]
]

X = pd.DataFrame(data)
xt = DataFrameImputer().fit_transform(X)

print('before...')
print(X)
print('after...')
print(xt)

which prints,

before...
     0   1   2
0    a   1   2
1    b   1   1
2    b   2   2
3  NaN NaN NaN
after...
   0         1         2
0  a  1.000000  2.000000
1  b  1.000000  1.000000
2  b  2.000000  2.000000
3  b  1.333333  1.666667

You can use sklearn_pandas.CategoricalImputer for the categorical columns. Details:

First, (from the book Hands-On Machine Learning with Scikit-Learn and TensorFlow) you can have subpipelines for numerical and string/categorical features, where each subpipeline's first transformer is a selector that takes a list of column names (and the full_pipeline.fit_transform() takes a pandas DataFrame):

class DataFrameSelector(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names = attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names].values

You can then combine these sub pipelines with sklearn.pipeline.FeatureUnion, for example:

full_pipeline = FeatureUnion(transformer_list=[
    ("num_pipeline", num_pipeline),
    ("cat_pipeline", cat_pipeline)
])

Now, in the num_pipeline you can simply use sklearn.preprocessing.Imputer(), but in the cat_pipline, you can use CategoricalImputer() from the sklearn_pandas package.

note: sklearn-pandas package can be installed with pip install sklearn-pandas, but it is imported as import sklearn_pandas