iterable-extensions 0.2.0

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iterable-extensions

Collection of useful extension methods to Iterables to enable functional programming. It is heavily inspired by C#'s LINQ.

The extension methods are implemented using https://pypi.org/project/extensionmethods/. Under the hood they rely strongly on itertools and are basically syntactic sugar to write code in a more functional style.

Type-checking is supported, see Type-checking.

Important notes:

• Whereas itertools generally returns iterators that are exhausted after consuming once, iterable-extensions generally returns iterables that may be consumed repeatedly.
• Similarly to itertools, iterable-extensions aims to evaluate lazily so that not the entire input iterable is loaded into memory. However, there are some notable exceptions, including:
  • order_by and order_by_descending
  • group_by

This package is under development. Only a number of extension methods are currently implemented.

For the full API reference and the currently implemented methods, see https://iterable-extensions.readthedocs.io/.

Example usage

To filter elements in an iterable based on some predicate:

from iterable_extensions import where, to_list

source = [1, 2, 3, 4, 5]

filtered = source | where[int](lambda x: x > 3) # Only numbers greater than 3
lst = filtered | to_list() # Materialize into list

print(lst)
# [4, 5]

lst2 = filtered | to_list() # Iterables can be consumed multiple times
print(lst2)
# [4, 5]

To transform elements according to some function:

from iterable_extensions import select, to_list

source = [1, 2, 3, 4, 5]

transformed = source | select[int, str](lambda x: str(2 * x))  # Transform each element
lst = transformed | to_list()  # Materialize into list

print(lst)
# ['2', '4', '6', '8', '10']

To group elements based on a key:

from dataclasses import dataclass

from iterable_extensions.iterable_extensions import group_by


@dataclass
class Person:
    age: int
    name: str


source = [
    Person(21, Gender.MALE, "Arthur"),
    Person(37, Gender.FEMALE, "Becky"),
    Person(12, Gender.MALE, "Chris"),
    Person(48, Gender.MALE, "Dave"),
    Person(88, Gender.MALE, "Eduardo"),
    Person(56, Gender.FEMALE, "Felice"),
]

grouped = source | group_by[Person, int](lambda p: p.age) # Group by age
lst = grouped | to_list() # Materialize into list

print(lst)
# [
#   10: [Person(age=10, name='Arthur'), Person(age=10, name='Becky')],
#   20: [Person(age=20, name='Chris')],
#   30: [Person(age=30, name='Dave'), Person(age=30, name='Eduardo'), Person(age=30, name='Felice')]
# ]

You can chain these methods into functional-style code. For instance, in the below example, to get the full name of the oldest male and female:

from dataclasses import dataclass
from enum import IntEnum

from iterable_extensions.iterable_extensions import (
    Grouping,
    first,
    group_by,
    order_by_descending,
    select,
    to_list,
)


class Gender(IntEnum):
    MALE = 1
    FEMALE = 2


@dataclass
class Person:
    age: int
    gender: Gender
    first_name: str
    last_name: str


data = [
    Person(21, Gender.MALE, "Arthur", "Johnson"),
    Person(56, Gender.FEMALE, "Becky", "de Vries"),
    Person(12, Gender.MALE, "Chris", "Lamarck"),
    Person(48, Gender.MALE, "Dave", "Stevens"),
    Person(88, Gender.MALE, "Eduardo", "Doe"),
    Person(37, Gender.FEMALE, "Felice", "van Halen"),
]

grouped = (
    data
    | group_by[Person, Gender](lambda p: p.gender) # Group by gender
    | select[Grouping[Person, Gender], Person]( # Within each group
        lambda g: (
            g 
            # Order by age, descending
            | order_by_descending[Person, int](lambda p: p.age) 
            | first() # Take the first entry
    )
    # For each gender, aggregate first and last name
    | select[Person, str](lambda p: f"{p.first_name} {p.last_name}") 
    | to_list() # Materialize into list
)

print(grouped)
# ['Eduardo Doe', 'Becky de Vries']

Type-checking

The iterable extensions are fully type-annotated and support type inference with linters as much as possible. However, due to limitations in current type checkers, inference doesn't propage through the | operator. For example:

source: list[int] = [1, 2, 3, 4, 5]

filtered = source | where(lambda x: x > 3)

Will give an error like Operator ">" not supported for types "T@where" and "Literal[3]" on the lambda body, even though the type of x is fully specified through source.

To circumvent this, you can expicitly specify its type: where[int](lambda x: ...). This also gives you autocompletion on x in the lambda body.

Alternatively, you can explicitly define the function instead of writing a lambda:

def func(x: int) -> bool:
    return x > 3


filtered = source | where(func)

Although this hampers the readability of the functional style that the iterable-extensions package aims to provide.

Note that the type annotations are only for static checkers. You can ignore these errors and the code will still run fine.

How to read Extension[TIn, **P, TOut]

In the API reference, you'll notice that all extension methods inherit from Extension[TIn, **P, TOut]. This class is the core of the extensionsmethods package (https://pypi.org/project/extensionmethods/). It provides the basic |-operator functionality.

The Extension class has two type parameters and a paramspec:

• TIn: The type that the extension is defined to operate on.
• **P: Arbitrary number of arguments that the extension method may take.
• TOut: The type of the return value of the extension method.

For example, looking at the signature of select:

class select[TIn, TOut](
    Extension[
        Iterable[TIn],
        [Callable[[TIn], TOut]],
        Iterable[TOut]
    ]
): ...

we see that:

• TIn = Iterable[TIn]. select is defined to operate on iterables of an arbitrary input type.
• **P = [Callable[[TIn], TOut]]. select requires a mapping function as a parameter.
• TOut = Iterable[TOut]. select returns an iterable of an arbitrary output type.

For example:

source: list[int] = [1, 2, 3, 4]

# Allowed. Inputs ints, outputs strings.
source | select[int, str](lambda: str(x)) 

# Not allowed. Expects strings as inputs, but ints are given.
source | select[str, str](lambda: str(x)) 

# Not allowed. Excepts ints as output, but the lambda returns strings.
source | select[int, int](lambda: str(x)) 

Installation

Using pip:

pip install iterable-extensions

Using uv:

uv add iterable-extensions

License

This project is licensed under the MIT License. See the LICENSE file for details.