Pipeline.transform(population_table, peripheral_tables=None, df_name='', table_name='')[source]

Translates new data into the trained features.

Transforms the data provided in population_table and peripheral_tables into features, which can be used to drive machine learning models. In addition to returning them as numerical array, this method is also able to return a or write the results in a data base called table_name.

population_table (

Main table corresponding to the population Placeholder instance variable. Its target variable(s) will be ignored.

peripheral_tables (List[]):

Additional tables corresponding to the peripheral Placeholder instance variable. They have to be provided in the exact same order as their corresponding placeholders. A single DataFrame will be wrapped into a list internally.

df_name (str, optional):

If not an empty string, the resulting features will be written into a newly created DataFrame.

table_name (str, optional):

If not an empty string, the resulting features will be written into the database of the same name. See Unified import interface for further information.

IOError: If the pipeline could not be found on the engine or

the pipeline could not be fitted.

TypeError: If any input argument is not of proper type. KeyError: If an unsupported instance variable is


TypeError: If any instance variable is of wrong type. ValueError: If any instance variable does not match its

possible choices (string) or is out of the expected bounds (numerical).


Resulting features provided in an array of the (number of rows in population_table, number of selected features).


A DataFrame containing the resulting features.


By default, transform returns a numpy.ndarray: .. code-block:: python

my_features_array = pipe.transform()

You can also export your features as by providing the df_name argument: .. code-block:: python

my_features_df = pipe.transform(df_name=”my_features”)


Only fitted pipelines (fit()) can transform data into features.