3526a6d7d5005d0cb126a39c894ad7f46b441260,category_encoders/basen.py,BaseNEncoder,transform,#BaseNEncoder#Any#Any#,150

Before Change

        // impute missing values only in the generated columns
        generated_cols = util.get_generated_cols(X, X_out, self.cols)
        X_out[generated_cols] = X_out[generated_cols].fillna(value=0.0)

        if self.return_df or override_return_df:
            return X_out
        else:

After Change

        

        if self.handle_missing == "error":
            if X[self.cols].isnull().any().bool():
                raise ValueError("Columns to be encoded can not contain null")

        if self._dim is None:
            raise ValueError("Must train encoder before it can be used to transform data.")

        // first check the type

Instances