| name | seaborn |
| description | Seaborn statistical plots for exploratory analysis via agent_generated_code. Use for quick relational/distribution/categorical charts. Do NOT use for submission-grade Nature figures (nature_figure) or low-level artists control (matplotlib). |
| license | Unknown |
| metadata | {"version":"1.2","skill-author":"VenusFactory2"} |
Seaborn Statistical Visualization
Overview
High-level statistical viz on top of matplotlib. In VenusFactory, plots run through agent_generated_code / python_repl. Publication figures must load nature_figure first.
VenusFactory execution
- No
seaborn_* @tool. Optional helpers: src/tools/visualize/matplotlib/plot_template.py, style_configurator.py.
- Deep API:
read_skill("seaborn", relative_path="references/function_reference.md") or references/legacy_guide.md.
When to use / NOT
| Use seaborn | Prefer instead |
|---|
| EDA bar/box/violin/heatmap | — |
| Publication multi-panel Nature style | nature_figure |
| Pixel-perfect artists / custom projections | matplotlib |
Quick start
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="whitegrid")
ax = sns.barplot(data=df, x="mutation", y="score")
fig = ax.get_figure()
fig.savefig(out_png, dpi=300, bbox_inches="tight")
Plot selection (index)
- Relational:
scatterplot, lineplot, relplot
- Distribution:
histplot, kdeplot, ecdfplot
- Categorical:
boxplot, violinplot, barplot, stripplot
- Matrix:
heatmap, clustermap
- Objects API:
seaborn.objects — see references/objects_interface.md if present / legacy guide
Common mistakes
- Skipping
nature_figure for manuscript figures
- Not saving PNG under session
output_dir at dpi≥300
- Passing wide data without melting for categorical plots
References (progressive disclosure)
Trust order: SKILL.md (hub tools & envelopes) → topic refs → references/legacy_guide.md (archived; may be outdated).
read_skill(skill_id="seaborn", relative_path="references/legacy_guide.md")
Load legacy only after the hub workflow in this file is insufficient.
