Charting: Python Static Visualizations

Select the optimal Python charting library and produce clean, publication-quality output.

Library Selection Framework

Choose the library based on what the visualization represents, not habit.

Seaborn — DEFAULT for statistical/analytical charts

Seaborn wraps matplotlib with better defaults, tighter pandas integration, and fewer lines of code. Reach for seaborn first when the data lives in a DataFrame and the goal is analytical.

Use for: distributions (histograms, KDEs, violin plots, ECDFs), categorical comparisons (box plots, swarm plots, strip plots, bar plots), correlation (heatmaps, pair plots, regression plots), grouped/faceted views (FacetGrid, catplot, relplot).

Why: Automatic axis labeling from column names, coherent color palettes, built-in aggregation with confidence intervals, and hue/col/row faceting with minimal code.

Practical rule: If the code would call plt.bar(), plt.hist(), plt.scatter(), or build a heatmap with plt.imshow() — use the seaborn equivalent instead. It will look better with less effort.

Matplotlib — fine-grained control and non-standard layouts

Drop to raw matplotlib only when seaborn doesn't support the chart type or when pixel-level layout control is required.

Use for: custom multi-panel figures mixing chart types, unusual annotations (arrows, shaded regions, custom legends), non-standard axes (polar, broken axes, insets), animations, image overlays, or any layout where the default seaborn API is insufficient.

Combine with seaborn: Seaborn plots return matplotlib Axes objects. Apply matplotlib customization on top of seaborn output rather than rebuilding from scratch.

Graphviz — graph/network structures

Graphviz operates in a fundamentally different domain: nodes and edges, not x/y data.

Use for: dependency trees, flowcharts, state machines, org charts, entity-relationship diagrams, DAGs, call graphs, any directed or undirected graph structure.

Python interface: Use the graphviz Python package (installed). Create graphviz.Digraph() or graphviz.Graph(), add nodes/edges, render to PNG/SVG/PDF.

import graphviz
g = graphviz.Digraph(format='png')
g.node('A', 'Start')
g.node('B', 'Process')
g.edge('A', 'B')
g.render('/home/claude/output', cleanup=True)

Layout engines: dot (hierarchical, default), neato (spring model), fdp (force-directed), circo (circular), twopi (radial). Set via g.engine = 'neato'.

Vega-Lite — interactive browser charts

When the user wants interactive, browser-rendered visualizations (tooltips, zoom, selection, filtering) or uploads data for exploratory charting, defer to the charting-vega-lite skill. That skill handles React artifact generation with inline data islands.

Decision shortcut: Static image file → this skill. Interactive artifact → charting-vega-lite.

Quick Reference: Chart Type → Library

Need	Library	Function
Histogram / KDE	seaborn	sns.histplot(), sns.kdeplot()
Box / Violin / Swarm	seaborn	sns.boxplot(), sns.violinplot()
Bar (categorical)	seaborn	sns.barplot(), sns.countplot()
Correlation heatmap	seaborn	sns.heatmap()
Scatter + regression	seaborn	sns.scatterplot(), sns.regplot()
Pair plot (multi-var)	seaborn	sns.pairplot()
Faceted grid	seaborn	sns.FacetGrid, catplot, relplot
Time series line	seaborn	sns.lineplot() (handles CI bands)
Custom multi-panel	matplotlib	fig, axes = plt.subplots()
Polar / radar	matplotlib	projection='polar'
Annotated diagrams	matplotlib	ax.annotate(), arrows, patches
Dependency tree	graphviz	Digraph
Flowchart / FSM	graphviz	Digraph with shape attrs
ER diagram	graphviz	Graph with record shapes
Network graph	graphviz	Graph with layout engine

Production Defaults

Apply these defaults to produce clean output without per-chart fiddling.

Seaborn Setup

import seaborn as sns
import matplotlib.pyplot as plt

sns.set_theme(style="whitegrid", palette="muted", font_scale=1.1)

Style options: whitegrid (default, good for most), white (cleaner for publications), darkgrid (data-dense plots), ticks (minimal).

Figure Sizing and DPI

fig, ax = plt.subplots(figsize=(10, 6))
# Or for seaborn figure-level functions:
g = sns.catplot(..., height=6, aspect=1.5)

# Save at publication quality
plt.savefig('/home/claude/chart.png', dpi=150, bbox_inches='tight', facecolor='white')

Use dpi=150 for screen/web output, dpi=300 for print. Always use bbox_inches='tight' to avoid clipped labels.

Color Guidance

• Categorical: "muted", "Set2", "tab10" — distinct, accessible
• Sequential: "viridis", "YlOrRd", "Blues" — ordered magnitude
• Diverging: "RdBu", "coolwarm" — centered on zero/midpoint
• Avoid: "jet", "rainbow" — perceptually non-uniform, colorblind-hostile

Common Refinements

# Rotate x-labels if overlapping
plt.xticks(rotation=45, ha='right')

# Remove top/right spines for cleaner look
sns.despine()

# Thousands separator for large numbers
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{x:,.0f}'))

Output Workflow

1. Create chart in /home/claude/
2. Save as PNG (default) or SVG (if user needs vector)
3. Copy to /mnt/user-data/outputs/
4. Present via present_files

Always plt.close() after saving to free memory.