Python Packages (Core)

Core Essential Tools for Professional Python Projects

These are the most commonly used external libraries across analytics, data, and AI projects. They are not part of the standard library — install them as needed using uv add <package> or pip install <package>.

Category	Package	Purpose
Core Management	pip, setuptools, wheel	Package installation, building, and distribution.
Documentation	mkdocs	Simple, fast static site generator for project documentation.
Logging	loguru	Modern, colorized logging with rotation and context.
HTTP & Environment	httpx, python-dotenv	Send modern HTTP/HTTPS requests (sync or async) and manage .env configuration.
Data Analysis	numpy, pandas, polars	Numeric computing, dataframes, and high-speed analysis.
Visualization	matplotlib, seaborn	Static and statistical visualizations.
Jupyter Ecosystem	jupyterlab, ipython, ipykernel	Notebook authoring, interactive shell, and VS Code kernel support.
Databases & Orchestration	duckdb	Lightweight database.
Machine Learning	scikit-learn, statsmodels	Classical ML, regression, and statistical modeling.
Natural Language Processing	spacy, nltk	Text preprocessing, tokenization, and linguistic analysis.

Notes:

1. httpx replaces requests as the modern, async-capable HTTP client for Python. Existing requests examples work with minor or no changes.

For the full curated reference (including Excel, orchestration, streaming, and alerts), see Essential External Tools in the documentation site.