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from sklearn_pandas import DataFrameMapper, gen_features

mean_columns_2d = [[f] for f in mean_columns]
bool_columns_2d = [[f] for f in bool_cols]

feature_def1 = gen_features(columns=mean_columns_2d, classes=[{'class': SimpleImputer, 'strategy':'mean'},StandardScaler])
feature_def2 = gen_features(columns=bool_columns_2d, classes=[{'class': SimpleImputer, 'strategy':'most_frequent'}])

feature_def_all = feature_def1 + feature_def2
mapper = DataFrameMapper(feature_def_all)


x_train = mapper.fit_transform(df_train).astype('float32')
x_val = mapper.transform(df_val).astype('float32')

In the original code, mean_columns and bool_cols are assumed to be lists of column names. By using a list comprehension [[f] for f in mean_columns], each column name is enclosed within a separate inner list. This is done to match the expected format for feature definitions in sklearn_pandas and DataFrameMapper, which expects a 2D list where each inner list represents a column name or a group of column names.

For example, if mean_columns contains ['column1', 'column2'], the list comprehension [[f] for f in mean_columns] will create [['column1'], ['column2']]. Now, each column name is represented as a separate inner list, making it compatible with the expected format in the feature definitions.

Similarly, the bool_columns_2d variable is created in the same way, ensuring that each column name is enclosed within a separate inner list.

This 2D list format allows you to define feature transformations for each column individually or groups of columns collectively in the subsequent steps of the code.