🏁 Getting Started

Welcome to the Getting Started guide for the humbldata package. This guide will provide you with an overview of the package and its capabilities.

📦 What is humbldata?

The humbldata package is a comprehensive data analysis tool that provides a wide range of functionalities for working with financial data. It is designed to be user-friendly and efficient, making it a valuable tool for both beginners and experienced data analysts.

🛠 How was humbldata built?

The focus of this package was to be built on a hyper-modern python stack:

Features

• 🧑‍💻 Quick and reproducible development environments with VS Code's Dev Containers, PyCharm's Docker Compose interpreter, and GitHub Codespaces
• 🌈 Cross-platform support for Linux, macOS (Apple silicon and Intel), and Windows
• 📦 Packaging and dependency management with Poetry
• 🌍 Environment management with Micromamba
• 📖 Comprehensive documentation generation with MkDocs
• 🚚 Installing from and publishing to private package repositories and PyPI
• ⚡️ Task running with Poe the Poet
• ✍️ Code formatting with Ruff
• ✅ Code linting with Pre-commit, Mypy, and Ruff
• 🏷️ Optionally follows the Conventional Commits standard to automate Semantic Versioning and Keep A Changelog with Commitizen
• 💌 Verified commits with GPG
• ♻️ Continuous integration with GitHub Actions or GitLab CI/CD
• 🧪 Test coverage with Coverage.py
• 🏗 Scaffolding updates with Cookiecutter and Cruft
• 🧰 Dependency updates with Dependabot

📂 Project Organization

• .github/workflows: Contains GitHub Actions used for building, testing, and publishing.
• .devcontainer/Dockerfile: Contains Dockerfile to build a development container for VSCode with all the necessary extensions for Python development installed.
• .devcontainer/devcontainer.json: Contains the configuration for the development container for VSCode, including the Docker image to use, any additional VSCode extensions to install, and whether or not to mount the project directory into the container.
• .vscode/settings.json: Contains VSCode settings specific to the project, such as the Python interpreter to use and the maximum line length for auto-formatting.
• src: Place new source code here.
• tests: Contains Python-based test cases to validate source code.
• pyproject.toml: Contains metadata about the project and configurations for additional tools used to format, lint, type-check, and analyze Python code.
• .prompts/: Contains useful prompts to use during development for modifying and generating code and tests.