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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.
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.
  2. 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.