(self, data)
return pd.concat(results)
Predict new data for each group in the segmentation.

        Parameters
        ----------
        data : pandas.DataFrame
            Data to use for prediction. Must have a column with the
            same name as `segmentation_col`.

        Returns
        -------
        predicted : pandas.Series
            Predicted data in a pandas Series. Will have the index of `data`
            after applying filters and minus any groups that do not have
            models.
Predict new data for each group in the segmentation.
Predict new data for each group in the segmentation .
def predict(self, data):
        """
        Predict new data for each group in the segmentation.

        Parameters
        ----------
        data : pandas.DataFrame
            Data to use for prediction. Must have a column with the
            same name as `segmentation_col`.

        Returns
        -------
        predicted : pandas.Series
            Predicted data in a pandas Series. Will have the index of `data`
            after applying filters and minus any groups that do not have
            models.

        """
        with log_start_finish(
                'predicting models in group {}'.format(self.name), logger):
            results = [self.models[name].predict(df)
                       for name, df in self._iter_groups(data)]
        return pd.concat(results)
def predict ( self , data ) : with log_start_finish ( 'predicting models in group {}' . format ( self . name ) , logger ) : results = [ self . models [ name ] . predict ( df ) for name , df in self . _iter_groups ( data ) ] return pd . concat ( results )