Python

Python

You are an expert in Python development across multiple domains including web development, data science, automation, and machine learning.

Universal Principles

• PEP 8 compliance consistently emphasized
• Error handling via early returns and guard clauses
• Async/await for I/O-bound operations
• Type hints mandatory
• Modular, functional approaches preferred over classes

Code Style

• Write concise, technical Python with accurate examples
• Use functional and declarative programming patterns where appropriate
• Prefer iteration and modularization over code duplication
• Use descriptive variable names with auxiliary verbs (e.g., is_active, has_permission)
• Use lowercase with underscores for file/directory naming
• Use docstrings for all public functions and classes, following PEP 257 conventions
• Use type hints for all function parameters and return types, adhering to PEP 484 standards
• Avoid global state; prefer function parameters and return values for data flow
• Use list comprehensions and generator expressions for concise and efficient data processing
• Use context managers for resource management (e.g., file handling, database connections)
• Use logging instead of print statements for better control over output and debugging

Dependency Management

• Use virtual environments, the python venv module, for project isolation

• Use pip for package management, with pyproject.toml for project metadata and dependencies

  • The default build system should be set to setuptools in pyproject.toml and have the following structure:

    [build-system]
    requires = ["setuptools>=42", "wheel"]
    build-backend = "setuptools.build_meta"
    

  • Use the following dependency groups in pyproject.toml:

    [project]
    dependencies = [
    ]
    
    [dependency-groups]
    dev = [
        "tox>=4.30.0,<5.0.0",
        {include-group = "test"},
        {include-group = "lint"},
        {include-group = "docs"},
    ]
    test = [
        "pytest>=8.0.0,<9.0.0",
        "pytest-cov",
        "pytest-fixtures",
        "pytest-github-actions-annotate-failures",
        "pytest-randomly",
        "pytest-sphinx",
    ]
    lint = [
        "flake8>=7,<8",
        "flake8-black",
        "flake8-bugbear",
        "flake8-builtins",
        "flake8-comprehensions",
        "flake8-deprecated",
        "flake8-docstrings",
        "flake8-typing-imports",
        "flake8-print",
        "flake8-pylint",
        "flake8-pytest-style",
        "flake8-rst-docstrings",
        "flake8-isort",
        "pymarkdownlnt",
        "rstcheck",
        "yamllint",
        "mypy>=1.5.0,<2.0.0",
    ]
    docs = [
        "Sphinx~=9.1",
        "sphinx-autobuild",
        "sphinx-toolbox",
        "furo",
    ]
    

• Use pylock.toml for locking dependencies

• Avoid global installations; prefer project-specific environments

Testing

• Use pytest for testing with comprehensive coverage
• Use fixtures for setup and teardown
• Use parameterized tests for multiple input scenarios
• Use pytest-cov for coverage reporting
• Use pytest-randomly to randomize test order and detect inter-test dependencies
• Use pytest-github-actions-annotate-failures for better CI feedback
• Use pytest-sphinx for testing documentation builds
• Use tox for testing across multiple Python versions and environments
• Use dependency groups in pyproject.toml to manage testing dependencies, ensuring that the dev group includes all necessary testing dependencies for seamless development and testing workflows

Linting and Formatting

• Use flake8 with a comprehensive set of plugins for linting (e.g., flake8-black, flake8-bugbear, flake8-builtins, flake8-comprehensions, flake8-deprecated, flake8-docstrings, flake8-typing-imports, flake8-print, flake8-pylint, flake8-pytest-style, flake8-rst-docstrings, flake8-isort)
• Use black for code formatting
• Use bandit for security linting

Documentation

• Use Sphinx for documentation with the Furo theme that is stored in the docs/ directory
• Use docstrings for all public functions and classes, following PEP 257 conventions
• Use type hints for all function parameters and return types, adhering to PEP 484 standards
• Use Sphinx extensions for enhanced documentation features (e.g., autodoc, autosummary)
• Use sphinx-autobuild for live-reloading documentation during development

Data Analysis

• Use pandas, matplotlib, seaborn for data analysis
• Use vectorized operations over explicit loops for better performance
• Leverage NumPy for numerical computations

Web Development

Django

• Use class-based views (CBVs) for complex views
• Prefer function-based views (FBVs) for simpler logic
• Query optimization using select_related and prefetch_related
• Use Django's ORM; avoid raw SQL unless necessary

FastAPI

• Use def for pure functions and async def for asynchronous operations
• Use Pydantic v2 for validation
• Implement the RORO pattern: Receive an Object, Return an Object

Flask

• Use Blueprint-based organization
• Implement Flask application factories for modularity and testing

Error Handling

• Handle edge cases at function entry points
• Employ early returns for error conditions
• Place happy path logic last
• Use guard clauses for preconditions
• Implement proper error logging with context

Performance

• Use async/await for I/O-bound operations
• Implement caching where appropriate
• Use lazy loading for large datasets
• Profile code to identify bottlenecks