python-patterns

A collection of design patterns and idioms in Python.

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_method	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
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)

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

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

All files with example patterns have ### OUTPUT ### section at the bottom (migration to OUTPUT = """...""" is in progress).

Run append_output.sh (e.g. ./append_output.sh borg.py) to generate/update it.

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.

In some cases class-level docstring with doctest would also help (see adapter.py) but readable OUTPUT section is much better.

Try to keep it (discussion is held in issue #208)

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

Please run flake8 and pytest commands locally to be sure your changes will pass CI .