| name | python-viz |
| type | skill |
| description | Python plotting and statistical-modelling libraries (matplotlib, seaborn, statsmodels) for the analyst presentation and statistical-methodology layers. Use when producing publication-quality figures or fitting statistical models in Python. Library-specific HOW for the tech-agnostic principles in the aops-tools analyst skill. |
| category | instruction |
| triggers | ["matplotlib","seaborn","statsmodels","python plot","publication figure"] |
| modifies_files | true |
| needs_task | false |
| mode | execution |
| domain | ["academic","development"] |
| allowed-tools | Read,Grep,Glob,Edit,Write,Bash,Skill |
| version | 0.1.0 |
| permalink | skills-aops-extras-python-viz |
Python Visualisation & Statistical Modelling (academicOps)
This skill collects the Python library-specific references that support the
tech-agnostic analyst skill (aops-tools). The analyst skill owns the statistical
methodology and presentation principles; this skill owns the library how-to for
producing figures and fitting models in Python.
These libraries are swappable — the analyst statistical-methodology guidance (test
selection, assumptions, effect sizes, reporting standards) is library-neutral. Use this
skill when you have settled on the Python ecosystem.
Contents
- [[matplotlib]] — foundational plotting (pyplot + object-oriented Figure/Axes), styling,
and publication-quality static figures.
- [[seaborn]] — dataset-oriented statistical graphics, semantic mappings, and
multi-panel figures.
- [[statsmodels]] — statistical modelling: OLS/GLM, discrete choice, time series, and
hypothesis-testing diagnostics.
When to use
- You need to render a figure from PRE-COMPUTED data (presentation layer).
- You need to fit or diagnose a statistical model in Python (statistical-methodology
layer) — pair this with the analyst skill's
statistical-analysis reference for the
methodology that drives the choice of test/model.
