How to use the kaggler.preprocessing.const.NAN_INT function in Kaggler

Args:
            X (pandas.DataFrame): categorical columns to encode

        Returns:
            (pandas.DataFrame): label encoded columns
        """

        self.label_encoders = [None] * X.shape[1]
        self.label_maxes = [None] * X.shape[1]

        for i, col in enumerate(X.columns):
            self.label_encoders[i], self.label_maxes[i] = \
                self._get_label_encoder_and_max(X[col])

            X.loc[:, col] = (X[col].fillna(NAN_INT)
                             .map(self.label_encoders[i])
                             .fillna(0))

        return X

Args:
            X (pandas.DataFrame): categorical columns to encode

        Returns:
            (pandas.DataFrame): encoded columns
        """
        for i, col in enumerate(X.columns):
            if self.cv is None:
                X.loc[:, col] = (X[col].fillna(NAN_INT)
                                       .map(self.target_encoders[i])
                                       .fillna(self.target_mean))
            else:
                for i_enc, target_encoder in enumerate(self.target_encoders[i], 1):
                    if i_enc == 1:
                        x = X[col].fillna(NAN_INT).map(target_encoder).fillna(self.target_mean)
                    else:
                        x += X[col].fillna(NAN_INT).map(target_encoder).fillna(self.target_mean)

                X.loc[:, col] = x / i_enc

        return X.astype(float)