dataclasses
A backport of the dataclasses module for Python 3.6
Decision gist · record as of 2026-08-14
Only if you are actively running Python 3.6 and need dataclass syntax. For any Python 3.7 or later, use the built-in dataclasses module instead. The package is stable and has no known vulnerabilities, but it is unmaintained and serves a shrinking audience. Do not install it for new projects targeting modern Python versions.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.6 exactly (>=3.6, <3.7); not compatible with Python 3.7 or later, which have dataclasses built-in.
- Low install friction with no runtime dependencies.
- Dormant maintenance since 2020-11-13 (over 2100 days); the repo is not archived but receives no active development.
License · maintenance · safety
Apache (permissive) — Apache License (permissive). No restrictions on use, modification, or distribution in commercial or private projects.
last release 2020-11-13 (2100 days) · last repo commit 2024-07-11 · 593 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 9,670,338 downloads/mo, #1,507 on PyPI
Alternatives
Verify before relying
pip install dataclasses
from dataclasses import dataclass
@dataclass
class InventoryItem:
name: str
unit_price: float
quantity_on_hand: int = 0
item = InventoryItem('hammers', 10.49, 12)- Whether dict insertion-order guarantee holds reliably across all Python 3.6 implementations beyond CPython.
What it is and what it does
This package is a backport of the dataclasses module from PEP 557 to Python 3.6. It allows you to define classes using the `@dataclass` decorator, which automatically generates common methods like `__init__`, `__repr__`, `__eq__`, and others based on type-annotated class attributes. This eliminates boilerplate code for simple data-holding classes.
The package is now historical: Python 3.7 and later include dataclasses in the standard library, so this backport is only needed if you are stuck on Python 3.6. The repository is no longer maintained, and the package has received no updates since 2020. If you are on Python 3.7 or later, use the built-in module instead.
Use it for
- Define simple data classes on Python 3.6 without writing verbose `__init__` and `__repr__` methods.
- Migrate legacy Python 3.6 codebases to use dataclass syntax before upgrading to Python 3.7+.
- Prototype or maintain applications that must run on Python 3.6 and need structured data types.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Only if you are actively running Python 3.6 and need dataclass syntax.
For any Python 3.7 or later, use the built-in dataclasses module instead. The package is stable and has no known vulnerabilities, but it is unmaintained and serves a shrinking audience. Do not install it for new projects targeting modern Python versions.
Install
dataclasses on PyPI
Before you install
Low install friction with no runtime dependencies. Dormant maintenance since 2020-11-13 (over 2100 days); the repo is not archived but receives no active development. This is expected—the package exists solely to backport a feature that became standard in Python 3.7.
Requires Python 3.6 exactly (>=3.6, <3.7); not compatible with Python 3.7 or later, which have dataclasses built-in.
License in practice
Apache License (permissive). No restrictions on use, modification, or distribution in commercial or private projects.
Quickstart
pip install dataclasses
from dataclasses import dataclass
@dataclass
class InventoryItem:
name: str
unit_price: float
quantity_on_hand: int = 0
item = InventoryItem('hammers', 10.49, 12)Verify before relying
- Whether dict insertion-order guarantee holds reliably across all Python 3.6 implementations beyond CPython.
Package facts
| License | Apache permissive |
| Python support | Capped below the current Python release >=3.6, <3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Dormant 2,100 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 9,670,338 / month, #1,507 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3.6Topic :: Software Development :: Libraries :: Python Modules |
Evidence: dataclasses-0.8-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “python 3.6 dataclasses backport”
- dataclassesProvides the PEP 557 dataclasses module for Python 3.6, enabling…
- zipfile36Provides a backport of Python 3.6's zipfile module for use on earlier…
- contextvarsProvides the PEP 567 contextvars module for Python 3.6, enabling…
Give your agent the search over MCP, or paste the wish link into any chat.
More Python Modules packages
Converts domain names between Unicode and ASCII-compatible encoding (Punycode) according to IDNA 2008 and Unicode Technical Standard 46, with security validation and broader script coverage than the standard library.
Install it if you work with internationalized domain names, need to validate domains, or use HTTP clients that depend on it transitively.
Setuptools is a Python build backend and package management tool that handles building, distributing, and installing Python packages, including support for C/C++ extension modules.
PyYAML parses and emits YAML 1.1 data format, enabling serialization and deserialization of configuration files and Python objects to and from human-readable YAML text.
Pydantic validates Python data structures against type hints, coercing and checking input at runtime to ensure it matches a declared schema.
Provides reusable metadata objects for use with PEP-593 `typing.Annotated` to express common constraints like bounds, collection sizes, and predicates on types.
Install it if you use or build libraries that need to express type constraints in a standardized, inspectable way—or if you want to annotate your own types with…
Provides runtime tools to inspect and introspect Python type annotations, enabling programmatic examination of type hints at execution time.
See also djangorestframework-dataclasses · dacite · typed-json-dataclass · argparse-dataclass · dataclasses-jsonschema · dataclass-csv · datafiles · dataclasses-json · dataclass-wizard · types-dataclasses