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 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) 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: Output 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. 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. In some cases class-level docstring with doctest would also help (see adapter.py) but readable OUTPUT section is much better. 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 tox or tox -e ci37 before submitting a patch to be sure your changes will pass CI. 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.