python-guidelines

# Python Guidelines ## What I Do Provide universal Python development guidelines that apply across different Python projects and domains. ## Universal Python Best Practices ### Project Structure ```text # Universal Python project structure project/ ├── src/ # Main source code │ └── package/ # Importable package ├── tests/ # Test suite ├── docs/ # Documentation ├── scripts/ # Utility scripts ├── pyproject.toml # Project configuration ├── README.md # Project overview └── .gitignore # Version control ignore ``` ### Dependency Management ```bash # Universal Python dependency management # Use uv for all package operations uv add package-name # Add production dependency uv add package-name --dev # Add development dependency uv remove package-name # Remove dependency uv sync --all-extras -U # Update all dependencies ``` ### Type Hints and Annotations ```python # Universal type hint patterns from typing import List, Dict, Optional, Union # Function with complete type annotations def process_data( input_data: List[Dict[str, Union[int, str]]], config: Optional[Dict[str, str]] = None ) -> Dict[str, List[float]]: """Process data with type-safe operations""" # Implementation with type-checked operations return processed_results ``` ## When to Use Me Use this skill when: - Setting up new Python projects - Standardizing Python development across teams - Creating reusable Python patterns - Implementing maintainable Python code ## Universal Python Examples ### Import Organization ```python # Universal import structure # 1. Standard library imports import os import sys from pathlib import Path # 2. Third-party imports import numpy as np import pandas as pd # 3. Local application imports from .utils import helpers from .core import processors ``` ### Error Handling Patterns ```python # Universal Python error handling class DataValidationError(Exception): """Custom exception for data validation issues""" pass def validate_input(data: dict) -> None: """Validate input data with specific error messages""" if not data: raise DataValidationError("Input data cannot be empty") if "required_field" not in data: raise DataValidationError("Missing required field: required_field") ``` ### Testing Patterns ```python # Universal Python testing structure import pytest from hypothesis import given, strategies as st class TestDataProcessor: """Test suite for data processor""" @pytest.fixture def sample_data(self): """Provide sample data for testing""" return {"input": [1, 2, 3], "expected": [2, 4, 6]} def test_process_data(self, sample_data): """Test data processing with sample input""" result = process_data(sample_data["input"]) assert result == sample_data["expected"] @given(st.lists(st.integers())) def test_process_data_properties(self, input_list): """Property-based testing for data processor""" result = process_data(input_list) assert len(result) == len(input_list) assert all(isinstance(x, int) for x in result) ``` ## Best Practices 1. **Consistency**: Apply same patterns across all Python projects 2. **Type Safety**: Use complete type annotations 3. **Testing**: Implement comprehensive test coverage 4. **Documentation**: Use Google-style docstrings ## Compatibility Works with: - Python 3.8+ projects - Any Python application type - Cross-project standardization - Organizational Python guidelines