faif/python-patterns

Features

• Structural Patterns - Provides a comprehensive collection of structural design patterns including facades, adapters, proxies, and decorators.
• Behavioral Design Patterns - Provides a comprehensive collection of behavioral design patterns, including observers, strategies, commands, and state machines.
• Creational Design Patterns - Provides a comprehensive collection of creational design patterns including factories, builders, and prototypes.
• Design Pattern Collections - Serves as a comprehensive collection of diverse software design patterns implemented in Python.
• Proxies - Implements the proxy pattern to add logic like logging or caching without changing the resource interface.
• Factory Functions - Uses factory functions and callables to decouple client logic from the specific concrete classes being created.
• Python - Implements behavioral design patterns using Python-specific idioms like observers and state machines.
• Adapter Patterns - Implements the adapter pattern to allow objects with incompatible interfaces to collaborate.
• Anti-Patterns - Identifies common architectural pitfalls and provides structural alternatives to avoid ineffective design choices.
• State Patterns - Implements the state design pattern to manage object behavior through state transitions.
• Architectural Patterns - Provides reference implementations for 3-tier architecture, MVC, and blackboard models.
• Component Abstractions - Separates high-level interface definitions from concrete implementations to allow independent variation of components.
• Composite Patterns - Implements the composite pattern to represent part-whole hierarchies as tree structures.
• Factory Method Patterns - Implements factory method patterns to instantiate objects without specifying concrete classes.
• Prototype Patterns - Implements the prototype pattern for creating new objects by cloning existing instances.
• Decoupling Patterns - Implements decoupling patterns to separate high-level interfaces from their concrete implementations.
• Delegation Patterns - Implements delegation patterns to achieve object reuse through composition instead of inheritance.
• Facades - Implements the facade pattern to provide a single unified entry point to complex subsystems.
• Dependency Injection - Uses dependency injection to decouple components and improve the ease of writing unit tests.
• Object Builders - Implements the builder pattern to separate the specification of complex objects from their representation.
• Object Composition Patterns - Implements object composition strategies using adapters and decorators to extend behavior without inheritance.
• Observer Patterns - Implements the observer pattern to notify dependent objects automatically when a subject state changes.
• Separation of Concerns - Implements architectural patterns to divide systems into presentation, processing, and data layers.
• Service Providers - Provides shared functionality to unrelated classes through service providers to avoid rigid inheritance.
• Software Architecture Patterns - Applies standardized architectural design patterns to structure Python applications for maintainability.
• Object Oriented Design - Implements creational and structural design patterns in Python to manage object instantiation and composition.
• Software Design Patterns - Serves as a comprehensive reference for applying standard software design patterns in Python.
• State Patterns - Moves state-specific logic into separate classes to change object behavior at runtime without conditional blocks.
• Structural Design Patterns - Implements methods for composing objects using adapters, proxies, and decorators.
• Testability Optimizations - Uses dependency injection and decoupling techniques in Python to improve code isolation and unit testability.
• Code Refactoring - Identifies architectural anti-patterns and provides clean, idiomatic Python refactoring alternatives.
• Publish-Subscribe Systems - Provides a central provider to distribute messages from publishers to interested subscribers.
• Object Extensions - Implements techniques for dynamically extending objects with new features at runtime.
• Hierarchical State Composition - Manages complex states by nesting finite state machines where child states inherit or override parent behavior.
• Mediator Patterns - Implements a central mediator object to coordinate communication and reduce direct coupling between components.
• Blackboard Patterns - Provides a reference implementation of the blackboard architectural pattern.
• Callable Factories - Implements object instantiation via callables to achieve better decoupling.
• Chain of Responsibility Patterns - Implements the passing of requests through linked handlers until a processing condition is fulfilled.
• Class Registries - Tracks subclasses in a central data structure during definition to allow dynamic lookup of implementations.
• Command Patterns - Encapsulates requests as objects to decouple the triggering of a job from its internal execution logic.
• Decoupled Logic Layers - Implements architectural separation between data operations and presentation layers.
• Dispatch Table Patterns - Provides a mechanism to map parameters to specific methods via dictionaries to avoid complex if-else chains.
• Dispatch Tables - Maps input parameters to specific methods using lookup tables to replace complex conditional logic chains.
• Service Injection Patterns - Implements service injection patterns to provide dependencies via constructors or setters.
• Memento Patterns - Implements state capturing mechanisms to revert objects to previous internal conditions.
• Object Pooling - Provides an implementation of object pooling to recycle instances and reduce allocation overhead.
• Request Interception Middleware - Implements a single entry point to intercept and delegate incoming requests to specific handlers.
• Validation Chains - Implements sequences of validation rules using boolean operators to verify complex object requirements.
• Immutable State Patterns - Implements immutable state patterns using object pooling to share identical instances.
• Strategy Patterns - Provides interchangeable algorithm encapsulations that can be selected and swapped during application runtime.
• Template Method Patterns - Establishes base process structures that allow subclasses to define specific step implementations.
• Visitor Patterns - Implements the decoupling of operations from object structures to facilitate the addition of new behaviors.
• Algorithms and Patterns - Collection of common software design patterns.
• Design Patterns - Concise collection of design patterns and idioms for Python.
• Language Specific Resources - A collection of design patterns implemented in Python.
• Language Style Guides - Collection of common design patterns implemented in Python.
• Programming Language Patterns - Collection of design patterns implemented in Python.

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