data-science-python-stack

To set up a Python stack for data science, this Skill provides an opinionated collection of libraries covering preprocessing, modeling, and evaluation, ensuring compatibility and a streamlined workflow for your project lifecycle.

Managing competing libraries in a data science stack is handled by organizing dependencies into mandatory, user choice, and optional categories, allowing you to select the most appropriate tool for tasks like deep learning.

Polars and PyArrow are part of the curated Python stack for tabular data tasks, but they are categorized as user-choice dependencies, meaning you can select them based on your specific data preprocessing needs.

Yes, scikit-learn and skrub are included in the mandatory libraries to ensure compatibility, covering data preprocessing and modeling while maintaining a consistent and efficient workflow throughout the project.

Automated installation of required ML libraries is managed by the Skill, which handles dependency resolution to ensure compatibility and ease of use when setting up your data science environment.

Yes, ruff and pytest are included in the Python data science stack to provide code linting and testing capabilities, ensuring your project maintains quality and reliability throughout its lifecycle.