Importing Python Modules and Objects into Transformations

Importing Python modules into transformations

note

Transformations support the pandas and numpy modules, only. These modules can only be used in Pandas transformations.

Python modules must be imported inside the transformation function.

Avoid using aliases for imports (e.g. use import pandas instead of import pandas as pd).

Any modules used for type annotations in function signatures must be imported outside the function.

In the following example, the pandas module is imported in two places:

• Inside of the transformation function, because the function uses the pandas module
• Outside of the transformation function, because pandas type annotations are used in the function signature (my_transformation(request: pandas.DataFrame) -> pandas.DataFrame:)

from tecton import transformation
import pandas  # required for type hints on my_transformation.


@transformation(mode="pandas")
def my_transformation(request: pandas.DataFrame) -> pandas.DataFrame:
    import pandas  # required for pandas.DataFrame() below.

    df = pandas.DataFrame()
    df["amount_is_high"] = (request["amount"] >= 10000).astype("int64")
    return df

Importing Python objects into transformation functions

Object imports must be done outside of the transformation definition.

The following imports of objects into transformation functions are allowed:

• Functions
• Constants

The following imports of objects into transformation functions are not allowed:

• Classes
• Class instances
• Enums

In the following example, my_func, my_int_const, my_string_const, my_dict_const are imported from my_local_module. The import takes place outside of the transformation function.

from tecton import transformation
import pandas  # required for type hints on my_transformation.
from my_local_module import my_func, my_int_const, my_string_const, my_dict_const


@transformation(mode="pandas")
def my_transformation(request: pandas.DataFrame) -> pandas.DataFrame:
    import pandas  # required for pandas.DataFrame() below.

    df = pandas.DataFrame()
    df[my_dict_const["resultval"]] = my_func(request[my_string_const] >= my_int_const)
    return df