koda 1.0.0

Type-safe functional tools for Python

Koda

Koda is a collection of practical type-safe tools for Python.

At its core are a number of datatypes that are common in functional programming.

Maybe

Maybe is similar to Python's Optional type. It has two variants: Nothing and Just, and they work in similar ways to what you may have seen in other languages.

from koda import Maybe, Just, nothing

a: Maybe[int] = Just(5)
b: Maybe[int] = nothing

To know if a Maybe is a Just or a Nothing, you'll need to inspect it.

from koda import Just, Maybe

maybe_str: Maybe[str] = function_returning_maybe_str()

# python 3.10 +
match maybe_str:
    case Just(val):
        print(val)
    case Nothing:
        print("No value!")

# python 3.9 and earlier
if isinstance(maybe_str, Just):
    print(maybe_str.val)
else:
    print("No value!")

Maybe has methods for conveniently stringing logic together.

Maybe.map

from koda import Just, nothing

def add_10(x: int) -> int:
    return x + 10


Just(5).map(add_10)  # Just(15)
nothing.map(add_10)  # nothing 
Just(5).map(add_10).map(lambda x: f"abc{x}")  # Just("abc15")

Maybe.flat_map

from koda import Maybe, Just, nothing


def safe_divide(dividend: int, divisor: int) -> Maybe[float]:
    if divisor != 0:
        return Just(dividend / divisor)
    else:
        return nothing

Just(5).flat_map(lambda x: safe_divide(10, x))  # Just(2)
Just(0).flat_map(lambda x: safe_divide(10, x))  # nothing
nothing.flat_map(lambda x: safe_divide(10, x))  # nothing

Result

Result provides a means of representing whether a computation succeeded or failed. To represent success, we can use OK; for failures we can use Err. Compared to Maybe, Result is perhaps most useful in that the "failure" case also returns data, whereas Nothing contains no data.

from koda import Ok, Err, Result 


def safe_divide_result(dividend: int, divisor: int) -> Result[float, str]:
    if divisor != 0:
        return Ok(dividend / divisor)
    else:
        return Err("cannot divide by zero!")


Ok(5).flat_map(lambda x: safe_divide_result(10, x))  # Ok(2)
Ok(0).flat_map(lambda x: safe_divide_result(10, x))  # Err("cannot divide by zero!") 
Err("some other error").map(lambda x: safe_divide_result(10, x))  # Err("some other error")

Result can be convenient with try/except logic.

from koda import Result, Ok, Err

def divide_by(dividend: int, divisor: int) -> Result[float, ZeroDivisionError]:
    try:
        return Ok(dividend / divisor)
    except ZeroDivisionError as exc:
        return Err(exc)


divided: Result[float, ZeroDivisionError] = divide_by(10, 0)  # Err(ZeroDivisionError("division by zero"))

Another way to perform the same computation would be to use safe_try:

from koda import Result, safe_try


# not safe on its own!
def divide(dividend: int, divisor: int) -> float:
    return dividend / divisor

# safe if used with `safe_try`
divided_ok: Result[float, Exception] = safe_try(divide, 10, 2)  # Ok(5)
divided_err: Result[float, Exception] = safe_try(divide, 10, 0)  # Err(ZeroDivisionError("division by zero"))

Conversion between Results, Maybes, and Optionals

Result and Maybe

Convert a Result to a Maybe type.

from koda import Just, nothing, Ok, Err

assert Ok(5).to_maybe == Just(5)
assert Err("any error").to_maybe == nothing 

Convert a Maybe to a Result type.

from koda import Just, nothing, Ok, Err

assert nothing.to_result("value if nothing") == Err("value if nothing")
assert Just(5).to_result("value if nothing") == Ok(5)

Maybe and Optional

Convert an Optional value to a Maybe.

from koda import to_maybe, Just, nothing

assert to_maybe(5) == Just(5)
assert to_maybe("abc") == Just("abc")
assert to_maybe(False) == Just(False)

assert to_maybe(None) == nothing

Convert a Maybe to an Optional.

from koda import Just, nothing

assert Just(5).to_optional == 5
assert nothing.to_optional is None

# note that `Maybe[None]` will always return None, 
# so `Maybe.get_or_else` would be preferable in this case
assert Just(None) is None

Result and Optional

Convert an Optional value to a Result.

from koda import to_result, Ok, Err 

assert to_result(5, "fallback") == Ok(5)
assert to_result("abc", "fallback") == Ok("abc")
assert to_result(False, "fallback") == Ok(False)

assert to_result(None, "fallback") == Err("fallback")

Convert a Result to an Optional.

from koda import Ok, Err

assert Ok(5).to_optional == 5
assert Err("some error").to_optional is None

# note that `Result[None, Any]` will always return None, 
# so `Result.get_or_else` would be preferable in this case
assert Ok(None) is None

More

There are many other functions and datatypes included. Some examples:

compose

Combine functions by sequencing.

from koda import compose
from typing import Callable

def int_to_str(val: int) -> str:
    return str(val)

def prepend_str_abc(val: str) -> str:
    return f"abc{val}"    

combined_func: Callable[[int], str] = compose(int_to_str, prepend_str_abc)
assert combined_func(10) == "abc10"

mapping_get

Try to get a value from a Mapping object, and return an unambiguous result.

from koda import mapping_get, Just, Maybe, nothing

example_dict: dict[str, Maybe[int]] = {"a": Just(1), "b": nothing}

assert mapping_get(example_dict, "a") == Just(Just(1))
assert mapping_get(example_dict, "b") == Just(nothing)
assert mapping_get(example_dict, "c") == nothing

As a comparison, note that dict.get can return ambiguous results:

from typing import Optional

example_dict: dict[str, Optional[int]] = {"a": 1, "b": None}

assert example_dict.get("b") is None
assert example_dict.get("c") is None

We can't tell from the resulting value whether the None was the value for a key, or whether the key was not present in the dict

load_once

Create a lazy function, which will only call the passed-in function the first time it is called. After it is called, the value is cached. The cached value is returned on each successive call.

from random import random
from koda import load_once

call_random_once = load_once(random)  # has not called random yet

retrieved_val: float = call_random_once()
assert retrieved_val == call_random_once()

Intent

Koda is intended to focus on a small set of practical data types and utility functions for Python. It will not grow to encompass every possible functional or typesafe concept. Similarly, the intent of this library is to avoid requiring extra plugins (beyond a type-checker like mypy or pyright) or specific typchecker settings. As such, it is unlikely that things like Higher Kinded Types emulation or extended type inference will be implemented in this library.