dataclassish

Tools from dataclasses, extended to all of Python

Python's dataclasses provides tools for working with objects, but only compatible @dataclass objects. 😢
This repository is a superset of those tools and extends them to work on ANY Python object you want! 🎉
You can easily register in object-specific methods and use a unified interface for object manipulation. 🕶️

For example,

from dataclassish import replace  # New object, replacing select fields

d1 = {"a": 1, "b": 2.0, "c": "3"}
d2 = replace(d1, c=3 + 0j)
print(d2)
# {'a': 1, 'b': 2.0, 'c': (3+0j)}

Installation

pip install dataclassish

Documentation

WIP. But if you've worked with a dataclass then you basically already know everything you need to know.

Quick example

In this Example we'll show how dataclassish works exactly the same as dataclasses when working with a @dataclass object.

from dataclassish import replace
from dataclasses import dataclass


@dataclass
class Point:
    x: float
    y: float


p = Point(1.0, 2.0)
print(p)
# Point(x=1.0, y=2.0)

p2 = replace(p, x=3.0)
print(p2)
# Point(x=3.0, y=2.0)

Now we'll work with a dict object. Note that you cannot use tools from dataclasses with dict objects.

from dataclassish import replace

p = {"x": 1, "y": 2.0}
print(p)
# {'x': 1, 'y': 2.0}

p2 = replace(p, x=3.0)
print(p2)
# {'x': 3.0, 'y': 2.0}

# If we try to `replace` a value that isn't in the dict, we'll get an error
try:
    replace(p, z=None)
except ValueError as e:
    print(e)
# invalid keys {'z'}.

Registering in a custom type is very easy! Let's make a custom object and define how replace will operate on it.

from typing import Any
from plum import dispatch


class MyClass:
    def __init__(self, a, b, c):
        self.a = a
        self.b = b
        self.c = c

    def __repr__(self) -> str:
        return f"MyClass(a={self.a},b={self.b},c={self.c})"


@dispatch
def replace(obj: MyClass, **changes: Any) -> MyClass:
    current_args = {k: getattr(obj, k) for k in "abc"}
    updated_args = current_args | changes
    return MyClass(**updated_args)


obj = MyClass(1, 2, 3)
print(obj)
# MyClass(a=1,b=2,c=3)

obj2 = replace(obj, c=4.0)
print(obj2)
# MyClass(a=1,b=2,c=4.0)

replace can also accept a second positional argument which is a dictionary specifying a nested replacement. For example consider the following dict:

p = {"a": {"a1": 1, "a2": 2}, "b": {"b1": 3, "b2": 4}, "c": {"c1": 5, "c2": 6}}

With replace the sub-dicts can be updated via:

replace(p, {"a": {"a1": 1.5}, "b": {"b2": 4.5}, "c": {"c1": 5.5}})
# {'a': {'a1': 1.5, 'a2': 2}, 'b': {'b1': 3, 'b2': 4.5}, 'c': {'c1': 5.5, 'c2': 6}}

In contrast in pure Python this would be:

from copy import deepcopy

newp = deepcopy(p)
newp["a"]["a1"] = 1.5
newp["b"]["b2"] = 4.5
newp["c"]["c1"] = 5.5

And this is the simplest case, where the mutability of a dict allows us to copy the full object and update it after. Note that we had to use deepcopy to avoid mutating the sub-dicts. So what if the objects are immutable?

@dataclass(frozen=True)
class Object:
    x: float | dict
    y: float


@dataclass(frozen=True)
class Collection:
    a: Object
    b: Object


p = Collection(Object(1.0, 2.0), Object(3.0, 4.0))
print(p)
Collection(a=Object(x=1.0, y=2.0), b=Object(x=3.0, y=4.0))

replace(p, {"a": {"x": 5.0}, "b": {"y": 6.0}})
# Collection(a=Object(x=5.0, y=2.0), b=Object(x=3.0, y=6.0))

With replace this remains a one-liner. Replace pieces of any structure, regardless of nesting.

To disambiguate dictionary fields from nested structures, use the F marker.

from dataclassish import F

replace(p, {"a": {"x": F({"thing": 5.0})}})
# Collection(a=Object(x={'thing': 5.0}, y=2.0),
#            b=Object(x=3.0, y=4.0))

Citation

If you found this library to be useful in academic work, then please cite.

Development

We welcome contributions!