Python Knowledge Base

Best practices, templates, and documentation for Python applications.

Contents

Shared

Framework-agnostic tooling and standards that apply to all Python projects: dependency management (uv, pyproject.toml), CI/CD (GitHub Actions), code quality (ruff, type checking, pre-commit), logging & observability (Azure Monitor), and Docker best practices.

FastAPI

Best practices for building FastAPI web APIs. Covers project structure, configuration, testing, and framework-specific deployment details.

Flask

Best practices for building Flask web applications with server-rendered HTML (Jinja2 templates, forms, sessions). Covers the application factory, blueprints, WTForms, security headers, testing, and framework-specific deployment details.

ETL Pipelines

Best practices for building data pipelines that extract data from external sources, transform it, and load it into a target system (API, database, file store). Covers project structure, configuration, data types, error handling, testing, CLI design, deployment, and more.

Data Analysis

Best practices for building reproducible, well-structured Jupyter notebook workflows for data analysis, statistical calibration, and geospatial modeling. Covers notebook structure, conda environments, lazy data loading, memory management, visualization, code reuse, and more.