How to implement Expectation Maximization Imputation method using python?

Question

From various resources, I came to know that Imputation using Expectation Maximization method is better than Mean Imputation for imputing missing data . But no source have explained how to implement it in python.

I looked into scikit-learn , fancyimpute packages, but they have not mentioned anything about Expectation Maximization method.

It would be very helpful , if you can provide link to documentation which explain implementation with example, or provide code to implement Expectation Maximization method for missing data.

Answer 1

import impyute as impy

data_missing = pd.DataFrame(np.array([[np.NaN, 0.6, np.NaN], [np.NaN, 0.25, 
0.3], [0.6, 0.7, np.NaN]]), columns=['a', 'b', 'c'])
em_imputed = impy.em(data_missing)

output:
   a     b    c
0  0.6  0.60  0.3
1  0.6  0.25  0.3
2  0.6  0.70  0.3

em function will return dataframe type