dataclasses

library0.8pypypi✓ verified 31d ago

The `dataclasses` library is a backport of the standard library `dataclasses` module, designed specifically for Python 3.6. It provides the `@dataclass` decorator and related utilities, enabling simpler creation of data-holding classes without boilerplate. Its current version is 0.8. As it targets a specific, older Python version, its release cadence is stable and infrequent, with no new feature development anticipated.

serialization

pip install dataclasses

github ↗package ↗

INSTALL

IMPORT

SIG · DATACLASSES

dataclasses

serializationpythonv0.8

Install

1.5s avg

Import

27ms

Disk

16MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.6 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.028s · 17.8MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 1.5s · import 0.026s · 18MB

16MB installed

● package 16MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

dataclass

✓ from dataclasses import dataclass

field

✓ from dataclasses import field

Define a simple data class using the `@dataclass` decorator. Note the `from __future__ import annotations` which is highly recommended for type hints in Python 3.6 to handle forward references and complex types gracefully. Mutable default values for fields should use `default_factory`.

from __future__ import annotations # Recommended for Python 3.6 type hints from dataclasses import dataclass, field @dataclass class User: id: int name: str = "Guest" email: str | None = None # Using Union syntax (PEP 604) or typing.Optional tags: list[str] = field(default_factory=list) # Example usage user1 = User(id=1, name="Alice") user2 = User(id=2, email="bob@example.com") print(user1) print(user2) assert user1.name == "Alice" assert user2.tags == []

Debug

Known issues

breakingThis library is exclusively for Python 3.6. Installing it on Python 3.7 or newer is redundant and can cause subtle issues if you rely on features specific to this backport or expect it to override the standard library module. Python 3.7+ includes `dataclasses` as a built-in module.

fix

Remove `dataclasses` from your project's dependencies when upgrading to Python 3.7 or newer. The standard library module will be used automatically.

affects: Python >=3.7

gotchaWhen using type hints in Python 3.6 with `dataclasses`, `from __future__ import annotations` is highly recommended. Without it, you may encounter `NameError` for forward references or issues with complex type hints (e.g., `list[str]`, `dict[str, int]`). Alternatively, string literal type hints (e.g., `field: 'MyClass'`) can be used.

fix

Add `from __future__ import annotations` at the top of your module, or use string literal type hints for any forward references or complex generic types.

affects: Python 3.6

gotchaThis backport provides the functionality of `dataclasses` as it existed in Python 3.7. It does not include features introduced in later Python versions, such as `kw_only` or `slots=True` (Python 3.10+).

fix

If these newer features are required, consider upgrading your Python environment to 3.10 or newer. There is no workaround to enable these features in the 3.6 backport.

affects: All versions of this backport on Python 3.6

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'dataclasses'

The `dataclasses` module is built into Python from version 3.7 onwards; for Python 3.6, it needs to be installed as a backport library.

fix

Install the backport library using pip: `pip install dataclasses`

TypeError: non-default argument follows default argument

Python (and thus dataclasses) requires that all arguments with default values must appear after all arguments without default values in a class definition's `__init__` method, which dataclasses automatically generates. This is often encountered with inheritance where field order is implicitly merged.

fix

Rearrange your dataclass fields, or fields in parent classes, so that all fields without default values are defined before any fields with default values.

TypeError: mutable default <class 'list'> for field 'my_list' is not allowed

Dataclasses prevent using mutable types (like lists, dictionaries, or sets) directly as default values because all instances would share the same mutable object, leading to unexpected side effects.

fix

Use `dataclasses.field(default_factory=...)` to provide a zero-argument callable that returns a new mutable object for each instance. For example, `my_list: List[int] = field(default_factory=list)`.

AttributeError: 'MyClass' object has no attribute 'my_field'

This can occur when `field(default_factory=...)` is used in a dataclass where `init=False` is set for the dataclass itself or for the specific field, and no custom `__init__` or `__post_init__` method explicitly initializes the field. When `init=False`, the automatically generated `__init__` does not include the field, thus the `default_factory` is not called.

fix

Either ensure `init=True` (the default) for the dataclass or the field, or if `init=False` is necessary, explicitly initialize the field in a custom `__init__` or `__post_init__` method, calling the `default_factory` manually.

Upgrade

Version history

0.8latest on PyPI · released Nov 13, 2020

Audit

Dependencies

No dependency data recorded yet.

Agent activity

47 hits · last 30 days

node

Resources

githubhttps://github.com/ericvsmith/dataclasses ↗

packagehttps://pypi.org/project/dataclasses/ ↗