zedr/clean-code-python
Clean Code Python
This project is a Python clean code guide and refactoring resource. It provides a collection of software craftsmanship principles and examples designed to improve the readability and maintainability of Python source code.
The resource focuses on optimizing variable naming to remove mental mapping and breaking functions into single-purpose units. It provides guidelines for reducing code duplication and organizing logic to ensure components remain easy to extend and maintain.
The content covers software architecture and design quality, including the implementation of SOLID principles and industry-standard design patterns. It also addresses the management of side effects and the use of automated test workflows within virtual environments.
Features
- Clean Code Guides - Offers a comprehensive guide to software craftsmanship and clean code principles specifically for Python developers.
- Clean Coding Standards - Provides a comprehensive collection of craftsmanship principles and examples for writing clean, maintainable Python code.
- Interface-Driven Abstraction - Guides the definition of stable boundaries between high-level modules and low-level implementations to decouple dependencies.
- Design Patterns - Uses industry-standard design patterns to solve recurring software design problems and improve maintainability.
- Python Patterns - Guides the structuring of Python applications using Pythonic architectural patterns to reduce technical debt.
- Pattern-Based Organization - Demonstrates how to organize source code using industry-standard design patterns to ensure maintainability.
- Code Duplication Reduction - Provides techniques for eliminating redundant logic through the creation of shared abstractions.
- Code Refactoring Guidelines - Provides engineering guidelines for restructuring Python code to optimize naming, reduce duplication, and improve function modularity.
- Modular Function Design - Demonstrates how to break complex functions into smaller, independent, and reusable units.
- Naming Schemas - Provides specific rules for variable and function naming to eliminate mental mapping and increase clarity.
- Python Coding Standards - Provides technical references and guidelines for refactoring Python code using clean code standards.
- SOLID Principles - Implements the five core SOLID design principles to create extensible and maintainable software.
- Single Responsibility Principles - Provides guidance on decomposing logic into single-purpose units based on the single responsibility principle.
- Naming Conventions - Offers guidelines for choosing intention-revealing names for variables and functions to improve readability.
- Variable Naming Conventions - Choose pronounceable and searchable names for variables to increase clarity and remove the need for mental mapping.
- Abstraction Level Management - Explains how to group function calls by complexity levels to keep logic flow linear and readable.
- Side Effect Management - Teaches how to centralize operations that modify global state to prevent unpredictable system behavior.
- Clean Code and Principles - Collection of clean code principles applied to Python.
- Python Programming Resources - Clean code concepts adapted for Python.
Star history
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