Skip to content


Repository files navigation


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
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)


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


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


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


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

Python 2 compatibility

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


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 ./ 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 Open Source Helpers

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.