nicsuzor/python-viz
aops-extras/skills/python-viz/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add nicsuzor/academicops python-vizInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- 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
- modifies_files:
- true
- needs_task:
- false
- mode:
- execution
- 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-analysisreference for the methodology that drives the choice of test/model.
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