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