databackend

The databackend package allows you to register a subclass, without
needing to import the subclass itself. This is useful for implementing
actions over optional dependencies.

Example

For this example, we’ll implement a function, fill_na(), that fills in
missing values in a DataFrame. It works with DataFrame objects from two
popular libraries: pandas and polars. Importantly, neither library
needs to be installed.

Setup

The code below defines “abstract” parent classes for each of the
DataFrame classes in the two libraries.

from databackend import AbstractBackend

class AbstractPandasFrame(AbstractBackend):
    _backends = [("pandas", "DataFrame")]


class AbstractPolarsFrame(AbstractBackend):
    _backends = [("polars", "DataFrame")]

Note that the abstract classes can be used as stand-ins for the real
thing in issubclass() and isinstance.

from pandas import DataFrame

issubclass(DataFrame, AbstractPandasFrame)
isinstance(DataFrame(), AbstractPandasFrame)

True

📝 Note that you can use
AbstractPandasFrame.register_backend("pandas", "DataFrame"), as an
alternative way to register backends.

Simple fill_na: isinstance to switch behavior

The fill_na() function below uses custom handling for pandas and
polars.

def fill_na(data, x):
    if isinstance(data, AbstractPolarsFrame):
        return data.fill_nan(x)
    elif isinstance(data, AbstractPandasFrame):
        return data.fillna(x)
    else:
        raise NotImplementedError()

Notice that neither pandas nor polars need to be imported when
defining fill_na().

Here is an example of calling fill_na() on both kinds of DataFrames.

# test polars ----

import polars as pl

df = pl.DataFrame({"x": [1, 2, None]})
fill_na(df, 3)


# test pandas ----

import pandas as pd

df = pd.DataFrame({"x": [1, 2, None]})
fill_na(df, 3)

The key here is that a user could have only pandas, or only polars,
installed. Importantly, doing the isinstance checks do not import any
libraries!

Advanced fill_na: generic function dispatch

databackend shines when combined with generic function
dispatch.
This is a programming approach where you declare a function
(e.g. fill_na()), and then register each backend specific
implementation on the function.

Python has a built-in function implementing this called
functools.singledispatch.

Here is an example of the previous fill_na() function written using
it.

from functools import singledispatch

@singledispatch
def fill_na2(data, x):
    raise NotImplementedError(f"No support for class: {type(data)}")


# handle polars ----

@fill_na2.register
def _(data: AbstractPolarsFrame, x):
    return data.fill_nan(x)


# handle pandas ----

@fill_na2.register
def _(data: AbstractPandasFrame, x):
    return data.fillna(x)

Note two important decorators:

@singledispatch defines a default function. This gets called if no
specific implementations are found.
@fill_na2.register defines specific versions of the function.

Here’s an example of it in action.

# example ----

import pandas as pd
import polars as pl

df = pl.DataFrame({"x": [1, 2, None]})
fill_na2(df, 3)

df = pd.DataFrame({"x": [1, 2, None]})
fill_na2(df, 3)

How it works

Under the hood, AbstractBackend behaves similarly to python’s builtin
abc.ABC class.

from abc import ABC

class MyABC(ABC):
    pass

from io import StringIO

MyABC.register(StringIO)


# StringIO is a "virtual subclass" of MyABC
isinstance(StringIO("abc"), MyABC)

True

The key difference is that you can specify the virtual subclass using
the tuple ("<mod_name>", "<class_name>").

When issubclass(SomeClass, AbstractBackend) runs, then…

The standard ABC caching mechanism is checked, and potentially
returns the answer immediately.
Otherwise, a subclass hook cycles through registered backends.
The hook runs the subclass check for any backends that are imported
(e.g. are in sys.modules).

Technically, AbstractBackend inherits all the useful metaclass things
from abc.ABCMeta, so these can be used also.

Dependencies of databackend (2).
Dependency	Optional
pip-tools	true
pytest	true

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databackend

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