python-project-structure

name python-project-structure description Python project organization, module architecture, and public API design. Use when setting up new projects, organizing modules, defining public interfaces with __all__, or planning directory layouts. Python Project Structure & Module Architecture Design well-organized Python projects with clear module boundaries, explicit public interfaces, and maintainable directory structures. Good organization makes code discoverable and changes predictable. When to Use This Skill Starting a new Python project from scratch Reorganizing an existing codebase for clarity Defining module public APIs with __all__ Deciding between flat and nested directory structures Determining test file placement strategies Creating reusable library packages Core Concepts 1. Module Cohesion Group related code that changes together. A module should have a single, clear purpose. 2. Explicit Interfaces Define what's public with __all__ . Everything not listed is an internal implementation detail. 3. Flat Hierarchies Prefer shallow directory structures. Add depth only for genuine sub-domains. 4. Consistent Conventions Apply naming and organization patterns uniformly across the project. Quick Start myproject/ ├── src/ │ └── myproject/ │ ├── __init__.py │ ├── services/ │ ├── models/ │ └── api/ ├── tests/ ├── pyproject.toml └── README.md Fundamental Patterns Pattern 1: One Concept Per File Each file should focus on a single concept or closely related set of functions. Consider splitting when a file: Handles multiple unrelated responsibilities Grows beyond 300-500 lines (varies by complexity) Contains classes that change for different reasons # Good: Focused files # user_service.py - User business logic # user_repository.py - User data access # user_models.py - User data structures # Avoid: Kitchen sink files # user.py - Contains service, repository, models, utilities... Pattern 2: Explicit Public APIs with __all__ Define the public interface for every module. Unlisted members are internal implementation details. # mypackage/services/__init__.py from .user_service import UserService from .order_service import OrderService from .exceptions import ServiceError, ValidationError __all__ = [ "UserService" , "OrderService" , "ServiceError" , "ValidationError" , ] # Internal helpers remain private by omission # from .internal_helpers import _validate_input # Not exported Pattern 3: Flat Directory Structure Prefer minimal nesting. Deep hierarchies make imports verbose and navigation difficult. # Preferred: Flat structure project/ ├── api/ │ ├── routes.py │ └── middleware.py ├── services/ │ ├── user_service.py │ └── order_service.py ├── models/ │ ├── user.py │ └── order.py └── utils/ └── validation.py # Avoid: Deep nesting project/core/internal/services/impl/user/ Add sub-packages only when there's a genuine sub-domain requiring isolation. Pattern 4: Test File Organization Choose one approach and apply it consistently throughout the project. Option A: Colocated Tests src/ ├── user_service.py ├── test_user_service.py ├── order_service.py └── test_order_service.py Benefits: Tests live next to the code they verify. Easy to see coverage gaps. Option B: Parallel Test Directory src/ ├── services/ │ ├── user_service.py │ └── order_service.py tests/ ├── services/ │ ├── test_user_service.py │ └── test_order_service.py Benefits: Clean separation between production and test code. Standard for larger projects. Advanced Patterns Pattern 5: Package Initialization Use __init__.py to provide a clean public interface for package consumers. # mypackage/__init__.py """MyPackage - A library for doing useful things.""" from .core import MainClass, HelperClass from .exceptions import PackageError, ConfigError from .config import Settings __all__ = [ "MainClass" , "HelperClass" , "PackageError" , "ConfigError" , "Settings" , ] __version__ = "1.0.0" Consumers can then import directly from the package: from mypackage import MainClass, Settings Pattern 6: Layered Architecture Organize code by architectural layer for clear separation of concerns. myapp/ ├── api/ # HTTP handlers, request/response │ ├── routes/ │ └── middleware/ ├── services/ # Business logic ├── repositories/ # Data access ├── models/ # Domain entities ├── schemas/ # API schemas (Pydantic) └── config/ # Configuration Each layer should only depend on layers below it, never above. Pattern 7: Domain-Driven Structure For complex applications, organize by business domain rather than technical layer. ecommerce/ ├── users/ │ ├── models.py │ ├── services.py │ ├── repository.py │ └── api.py ├── orders/ │ ├── models.py │ ├── services.py │ ├── repository.py │ └── api.py └── shared/ ├── database.py └── exceptions.py File and Module Naming Conventions Use snake_case for all file and module names: user_repository.py Avoid abbreviations that obscure meaning: user_repository.py not usr_repo.py Match class names to file names: UserService in user_service.py Import Style Use absolute imports for clarity and reliability: # Preferred: Absolute imports from myproject.services import UserService from myproject.models import User # Avoid: Relative imports from ..services import UserService from . import models Relative imports can break when modules are moved or reorganized. Best Practices Summary Keep files focused - One concept per file, consider splitting at 300-500 lines (varies by complexity) Define __all__ explicitly - Make public interfaces clear Prefer flat structures - Add depth only for genuine sub-domains Use absolute imports - More reliable and clearer Be consistent - Apply patterns uniformly across the project Match names to content - File names should describe their purpose Separate concerns - Keep layers distinct and dependencies flowing one direction Document your structure - Include a README explaining the organization