python-patterns

A collection of design patterns and idioms in Python.

Remember that each pattern has its own trade-offs. And you need to pay attention more to why you’re choosing a certain pattern than to how to implement it.

Current Patterns

Creational Patterns:

Pattern	Description
abstract_factory	use a generic function with specific factories
borg	a singleton with shared-state among instances
builder	instead of using multiple constructors, builder object receives parameters and returns constructed objects
factory	delegate a specialized function/method to create instances
lazy_evaluation	lazily-evaluated property pattern in Python
pool	preinstantiate and maintain a group of instances of the same type
prototype	use a factory and clones of a prototype for new instances (if instantiation is expensive)

Structural Patterns:

Pattern	Description
3-tier	data<->business logic<->presentation separation (strict relationships)
adapter	adapt one interface to another using a white-list
bridge	a client-provider middleman to soften interface changes
composite	lets clients treat individual objects and compositions uniformly
decorator	wrap functionality with other functionality in order to affect outputs
facade	use one class as an API to a number of others
flyweight	transparently reuse existing instances of objects with similar/identical state
front_controller	single handler requests coming to the application
mvc	model<->view<->controller (non-strict relationships)
proxy	an object funnels operations to something else

Behavioral Patterns:

Pattern	Description
chain_of_responsibility	apply a chain of successive handlers to try and process the data
catalog	general methods will call different specialized methods based on construction parameter
chaining_method	continue callback next object method
command	bundle a command and arguments to call later
iterator	traverse a container and access the container’s elements
iterator (alt. impl.)	traverse a container and access the container’s elements
mediator	an object that knows how to connect other objects and act as a proxy
memento	generate an opaque token that can be used to go back to a previous state
observer	provide a callback for notification of events/changes to data
publish_subscribe	a source syndicates events/data to 0+ registered listeners
registry	keep track of all subclasses of a given class
servant	provide common functionality to a group of classes without using inheritance
specification	business rules can be recombined by chaining the business rules together using boolean logic
state	logic is organized into a discrete number of potential states and the next state that can be transitioned to
strategy	selectable operations over the same data
template	an object imposes a structure but takes pluggable components
visitor	invoke a callback for all items of a collection

Design for Testability Patterns:

Pattern	Description
dependency_injection	3 variants of dependency injection

Fundamental Patterns:

Pattern	Description
delegation_pattern	an object handles a request by delegating to a second object (the delegate)

Others:

Pattern	Description
blackboard	architectural model, assemble different sub-system knowledge to build a solution, AI approach - non gang of four pattern
graph_search	graphing algorithms - non gang of four pattern
hsm	hierarchical state machine - non gang of four pattern

Videos

Design Patterns in Python by Peter Ullrich

Sebastian Buczyński - Why you don’t need design patterns in Python?

You Don’t Need That!

Pluggable Libs Through Design Patterns

Contributing

When an implementation is added or modified, please review the following guidelines:

Docstrings

Add module level description in form of a docstring with links to corresponding references or other useful information.

Add “Examples in Python ecosystem” section if you know some. It shows how patterns could be applied to real-world problems.

facade.py has a good example of detailed description, but sometimes the shorter one as in template.py would suffice.

Python 2 compatibility

To see Python 2 compatible versions of some patterns please check-out the legacy tag.

Update README

When everything else is done - update corresponding part of README.

Travis CI

Please run the following before submitting a patch

• black . This lints your code.

Then either:

• tox or tox -e ci37 This runs unit tests. see tox.ini for further details.
• If you have a bash compatible shell use ./lint.sh This script will lint and test your code. This script mirrors the CI pipeline actions.

You can also run flake8 or pytest commands manually. Examples can be found in tox.ini.

Contributing via issue triage

You can triage issues and pull requests which may include reproducing bug reports or asking for vital information, such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to subscribe to python-patterns on CodeTriage.

🚫 Anti-Patterns

This section lists some common design patterns that are not recommended in Python and explains why.

🧱 Singleton

Why not:

• Python modules are already singletons — every module is imported only once.
• Explicit singleton classes add unnecessary complexity.
• Better alternatives: use module-level variables or dependency injection.

🌀 God Object

Why not:

• Centralizes too much logic in a single class.
• Makes code harder to test and maintain.
• Better alternative: split functionality into smaller, cohesive classes.

🔁 Inheritance overuse

Why not:

• Deep inheritance trees make code brittle.
• Prefer composition and delegation.
• “Favor composition over inheritance.”