fc4917ae8a7320fc9a258b50d82a177ed2124a91,category_encoders/basen.py,BaseNEncoder,fit_base_n_encoding,#BaseNEncoder#Any#,148

Before Change

                values = values[values > 0]

            digits = self.calc_required_digits(X, col)
            X_unique = pd.DataFrame(index=values)

            X_unique_to_cols = X_unique.index.map(lambda x: self.col_transform(x, digits))

            for dig in range(digits):
                X_unique[str(col) + "_%d" % (dig,)] = X_unique_to_cols.map(
                    lambda r: int(r[dig]) if r is not None else None)

            if self.handle_unknown == "return_nan":
                X_unique.loc[-1] = np.nan
            elif self.handle_unknown == "value":

After Change

                values = values[values > 0]

            digits = self.calc_required_digits(values)
            X_unique = pd.DataFrame(index=values,
                                    columns=[str(col) + "_%d" % x for x in range(digits)],
                                    data=np.array([self.col_transform(x, digits) for x in range(1, len(values) + 1)]))

            if self.handle_unknown == "return_nan":
                X_unique.loc[-1] = np.nan
            elif self.handle_unknown == "value":

Instances