TypeError: fit_transform() takes 2 positional arguments but 3 were given

Question

I have pandas DataFrame df. I want to encode continuous and categorical features of df using different encoders. I find it very comfortable to use make_column_transformer, but the code shown below fails with LabelEncoder(), but works fine with OneHotEncoder(handle_unknown='ignore')). The error message is:

TypeError: fit_transform() takes 2 positional arguments but 3 were given

It's not clear to me how to fix this issue.

The code:

from sklearn.compose import make_column_transformer
from sklearn.preprocessing import RobustScaler, OneHotEncoder, LabelEncoder

continuous_features = ['COL1','COL2']       
categorical_features = ['COL3','COL4']

column_trans = make_column_transformer(
    (categorical_features,LabelEncoder()),
    (continuous_features, RobustScaler()))

X_enc = column_trans.fit_transform(df)

Answer 1

According to https://scikit-learn.org/stable/modules/generated/sklearn.compose.make_column_transformer.html.

make_column_transformer(
...     (StandardScaler(), ['numerical_column']),
...     (OneHotEncoder(), ['categorical_column']))

So for your case:

from sklearn.compose import make_column_transformer
from sklearn.preprocessing import RobustScaler, OneHotEncoder, LabelEncoder

continuous_features = ['COL1','COL2']       
categorical_features = ['COL3','COL4']

column_trans = make_column_transformer(
    (OneHotEncoder(), categorical_features),
    (RobustScaler(), continuous_features))

X_enc = column_trans.fit_transform(df)

If you want to use LabelEncoder(), you can only pass one column, not two!

Hope this helps.