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data_without_missing_values = original_data.dropna(axis=1)

# other option
cols_with_missing = [col for col in original_data.columns 
                                 if original_data[col].isnull().any()]
reduced_original_data = original_data.drop(cols_with_missing, axis=1)
reduced_test_data = test_data.drop(cols_with_missing, axis=1)

from sklearn.impute import SimpleImputer
my_imputer = SimpleImputer()
data_with_imputed_values = my_imputer.fit_transform(original_data)

# make copy to avoid changing original data (when Imputing)
new_data = original_data.copy()

# make new columns indicating what will be imputed
cols_with_missing = (col for col in new_data.columns 
                                 if new_data[col].isnull().any())
for col in cols_with_missing:
    new_data[col + '_was_missing'] = new_data[col].isnull()

# Imputation
my_imputer = SimpleImputer()
new_data = pd.DataFrame(my_imputer.fit_transform(new_data))
new_data.columns = original_data.columns