| name | tufte-viz |
| description | Use when designing, critiquing, or improving data visualizations -- applies Tufte's principles (data-ink, lie factor, chartjunk, small multiples) and implements them in seaborn |
Tufte Visualization Ideation
Apply Edward Tufte's principles to design clear, honest, high-density data visualizations. In this project, implement in seaborn (objects interface preferred) — never matplotlib-only, never bare pandas .plot().
When to Use
- Designing new charts, dashboards, or reports
- Critiquing or improving existing visualizations
- Reviewing notebooks or
ad_hoc/ plots for graphical integrity
- Deciding between visualization approaches (e.g. small multiples vs. overlay)
- Reducing chartjunk or improving data-ink ratio
Workflow
For new visualizations
-
Clarify the data story
- What comparisons matter? (Tufte: "compared to what?")
- What is the single key insight to communicate?
- Who is the audience and what decision does this inform?
-
Select approach using Tufte principles
- High comparison need → small multiples (
so.Plot().facet() or sns.FacetGrid)
- Dense data → tables, sparklines, or stripplot
- Time-series → line charts with minimal grid (
despine, no top/right spines)
- Part-to-whole → avoid pie charts; prefer ordered bar or table
- Distributions → strip/swarm/box, not 3D histograms
-
Design with data-ink in mind
- Start minimal, add only what earns its ink
- Default to grayscale (
palette="gray"); use color only to encode a variable
- Strip default seaborn theme cruft (
sns.despine(), remove gridlines, prune ticks)
-
Apply the Tufte test (see references/tufte-principles.md)
-
Implement in seaborn (see references/seaborn-patterns.md)
- Save plots to
data/ or ad_hoc/ — never leave only in notebook state
- Pure functions: pandas for transform, seaborn for render, no side effects in logic blocks
For critiquing visualizations
-
Check graphical integrity
- Compute lie factor if proportions look off (
visual_ratio / data_ratio, target ≈ 1.0)
- Verify baselines (bar charts must start at 0; line charts may not)
- Look for 3D distortion, dual axes, truncated scales
-
Identify chartjunk
- Decorative gradients, drop shadows, 3D bars
- Heavy gridlines competing with data
- Moiré patterns from default seaborn themes
- Redundant legends when direct labels would work
-
Evaluate data-ink ratio
- What can be erased without losing information?
- What is redundant (e.g. both color AND legend AND title saying the same thing)?
-
Suggest improvements with specific before/after seaborn snippets
References
Load only what the current task needs:
references/tufte-principles.md — core principles from The Visual Display of Quantitative Information: lie factor, data-ink, chartjunk, small multiples, integrity.references/analytical-design.md — extensions from Envisioning Information, Visual Explanations, Beautiful Evidence: the 6 principles of analytical design, sparklines, layering & separation, micro/macro, range-frames, causality, confections. Load when designing dashboards, dense displays, sparklines, or explanatory graphics.references/seaborn-patterns.md — concrete seaborn (objects interface) recipes for each Tufte principle: despine, small multiples via so.Plot().facet(), sparklines, range-frames, palette choices, theme. Load when actually writing the plotting code.
Quick Checklist
Before declaring a visualization done:
