🌌 A Python library package which provides accretive data structures - collections which can grow but never shrink.

• 📖 Accretive Dictionary: Like a regular dict, but entries cannot be modified or removed once added. Also has variants with defaults and validation.
• 🗃️ Accretive Namespace: Similar to SimpleNamespace, but attributes become immutable after assignment.
• 🧱 Additional Types: Classes (including abstract base classes), modules, and objects with accretive behavior.
• 🏗️ Flexible Initialization: Support for unprotected attributes during initialization; useful for compatibility with class decorators, such as dataclasses.
• 🔓 Flexible Mutability: Support for declaring specific attributes as mutable, enabling selective modification while maintaining immutability for other attributes.

Install via uv pip command:

uv pip install accretive

Or, install via pip:

pip install accretive

Enforcement of immutability is quite difficult in Python. While this library encourages immutability by default, it can be circumvented by anyone who has intermediate knowledge of Python machinery and who is determined to circumvent the immutability. Use the library in the spirit of making programs safer, but understand that it cannot truly prevent unwanted state tampering.

An accretive namespace, similar to types.SimpleNamespace, is available. This namespace can be initialized from multiple iterables and from keyword arguments. (Keyword arguments shown below; see documentation for additional forms of initialization.)

>>> from accretive import Namespace
>>> ns = Namespace( apples = 12, bananas = 6 )
>>> ns.cherries = 42  # ✅ Allows new attributes.
>>> ns.apples = 14    # ❌ Attempted reassignment raises error.
Traceback (most recent call last):
...
accretive.exceptions.AttributeImmutability: Could not assign or delete existing attribute 'apples'.
>>> del ns.apples     # ❌ Attempted deletion raises error.
Traceback (most recent call last):
...
accretive.exceptions.AttributeImmutability: Could not assign or delete existing attribute 'apples'.
>>> ns
accretive.namespaces.Namespace( apples = 12, bananas = 6, cherries = 42 )

An accretive dictionary, similar to dict, is available. This dictionary can be initialized from multiple iterables and from keyword arguments. (Keyword arguments shown below; see documentation for additional forms of initialization.)

>>> from accretive import Dictionary
>>> dct = Dictionary( apples = 12, bananas = 6 )
>>> dct[ 'cherries' ] = 42  # ✅ Allows new entries.
>>> dct.update( blueberries = 96, strawberries = 24 )
accretive.dictionaries.Dictionary( {'apples': 12, 'bananas': 6, 'cherries': 42, 'blueberries': 96, 'strawberries': 24} )
>>> dct[ 'bananas' ] = 11   # ❌ Attempted alteration raises error.
Traceback (most recent call last):
...
accretive.exceptions.EntryImmutability: Could not alter or remove existing entry for 'bananas'.
>>> del dct[ 'bananas' ]    # ❌ Attempted removal raises error.
Traceback (most recent call last):
...
accretive.exceptions.EntryImmutability: Could not alter or remove existing entry for 'bananas'.
>>> dct
accretive.dictionaries.Dictionary( {'apples': 12, 'bananas': 6, 'cherries': 42, 'blueberries': 96, 'strawberries': 24} )

The accretive decorator can be applied to any class to make its instances enforce attribute immutability after assignment.

>>> from accretive import with_standard_behaviors
>>> @with_standard_behaviors
... class Config:
...     def __init__( self, debug = False ):
...         self.debug = debug
...
>>> config = Config( debug = True )
>>> config.verbose = True  # ✅ Allows new attributes
>>> config.debug = False   # ❌ Attempted reassignment raises error
Traceback (most recent call last):
...
accretive.exceptions.AttributeImmutability: Could not assign or delete existing attribute 'debug'.

• 📝 Configuration Registries: Registries which can accumulate entries but never remove them, thereby guaranteeing sticky state.
• 🔌 Plugin Systems: Register extensions which are then guaranteed to be available from the time of registration to the end of the process.
• 🔒 Immutable Collections: Many scenarios requiring grow-only collections with immutability guarantees.

Contribution to this project is welcome! However, it must follow the code of conduct for the project.

Please file bug reports and feature requests in the issue tracker or submit pull requests to improve the source code or documentation.

For development guidance and standards, please see the development guide.

• python-absence (absence on PyPI)

  🕳️ A Python library package which provides a sentinel for absent values - a falsey, immutable singleton that represents the absence of a value in contexts where None or False may be valid values.

• python-classcore (classcore on PyPI)

  🏭 A Python library package which provides foundational class factories and decorators for providing classes with attributes immutability and concealment and other custom behaviors.

• python-detextive (detextive on PyPI)

  🕵️ A Python library which provides consolidated text detection capabilities for reliable content analysis. Offers MIME type detection, character set detection, and line separator processing.

• python-dynadoc (dynadoc on PyPI)

  📝 A Python library package which bridges the gap between rich annotations and automatic documentation generation with configurable renderers and support for reusable fragments.

• python-falsifier (falsifier on PyPI)

  🎭 A very simple Python library package which provides a base class for falsey objects - objects that evaluate to False in boolean contexts.

• python-frigid (frigid on PyPI)

  🔒 A Python library package which provides immutable data structures - collections which cannot be modified after creation.

• python-icecream-truck (icecream-truck on PyPI)

  🍦 Flavorful Debugging - A Python library which enhances the powerful and well-known icecream package with flavored traces, configuration hierarchies, customized outputs, ready-made recipes, and more.

• python-librovore (librovore on PyPI)

  🐲 Documentation Search Engine - An intelligent documentation search and extraction tool that provides both a command-line interface for humans and an MCP (Model Context Protocol) server for AI agents. Search across Sphinx and MkDocs sites with fuzzy matching, extract clean markdown content, and integrate seamlessly with AI development workflows.

• python-mimeogram (mimeogram on PyPI)

  📨 A command-line tool for exchanging collections of files with Large Language Models - bundle multiple files into a single clipboard-ready document while preserving directory structure and metadata... good for code reviews, project sharing, and LLM interactions.