choose-python-library

Choose Python Library

Human-facing prose

Details: setup-workspace references/human_facing_prose.md. Present library options as a data-science choice for this job. Do not name skill ids or the wrapper CLI in the question.

1. State the job and constraints.
2. Run python -m skore_skills env stack. A job listed under competing is a genuine choice; anything the policy already fixes (mandatory, stage, export) is settled — use it rather than reopening the choice. Do not read the packaged JSON file directly; it lives inside the installed package.
3. For a genuine choice, present the smallest useful option set and ask the user. Do not pick silently.
4. After the user chooses, load add-python-package when status.skills reports it installed. If it is not installed, name the package and stop. Do not call env add from this skill. Typical callers: explore-ml-data (pandas vs polars), later extras (tuning). Not setup-workspace. Plotting jobs: load plot-ml-figure if installed; do not present matplotlib vs seaborn vs plotly.
5. Confirm symbols with python -m skore_skills api get <dotted>... before writing calls. Several symbols go in one call. Scope for one package is python -m skore_skills env route <pkg>, not a guess.

Stop conditions

• Do not install every candidate.
• Do not use popularity alone as a technical decision.
• Do not run pip directly in a managed project.
• Do not substitute black/isort for Ruff or sklearn Pipeline for skrub DataOps; those choices are already fixed.
• Do not present matplotlib vs seaborn vs plotly; that job is plot-ml-figure.