Beautiful Data Viz

name: beautiful-data-viz description: Create publication-quality, aesthetically refined charts in Python/Jupyter (matplotlib/seaborn): readable axes, tight whitespace, and curated palettes (categorical/sequential/diverging). argument-hint: "[medium=notebook|paper|slides] [background=light|dark]"

Beautiful Data Viz

Use this skill whenever the user asks to create, restyle, or “make nicer” a data visualization in Python / Jupyter notebooks.

Ethos

Toyplot’s ethos is a good North Star for high-quality plots:

Always look your best. Share your things. Play well with others. Never tell a lie.

(Reference: https://toyplot.readthedocs.io/en/stable/ethos.html)

Defaults (when the user doesn’t specify)

• Medium: notebook
• Background: light
• Library: matplotlib-first, optionally use seaborn for theme/palettes
• Output: one crisp figure per cell; include a savefig line when useful

Workflow (follow in order)

1. Clarify intent (or assume):

   • What is the single takeaway?
   • Audience + medium (notebook, paper, slides) and whether background is light/dark
   • Categorical vs continuous variables; number of categories

2. Pick the simplest correct chart

   • Prefer: line (time), scatter (relationship), bar/dot (ranking), histogram/density (distribution), heatmap (matrix)
   • Avoid: 3D effects, gratuitous decoration, “rainbow” gradients

3. Choose palette type before choosing colors

   • Qualitative → categorical (hue-based)
   • Sequential → numeric intensity (luminance ramp)
   • Diverging → numeric with a meaningful midpoint (neutral center)
   • (See: https://seaborn.pydata.org/tutorial/color_palettes.html)

4. Apply a base style, then customize

   • Use the helper in assets/beautiful_style.py (recommended).
   • If you can’t import it, copy/paste the functions into the notebook.

5. Build the plot with “readability first” mechanics

   • Use human-friendly units, tick formatting, and concise labels.
   • Limit tick count and prevent overlap.
   • Put the legend where it doesn’t create dead space; prefer direct labels when possible.

6. Polish for maximum clarity + minimal whitespace

   • Titles: short title; optional subtitle/caption in smaller text
   • Axes: label with units; use sensible limits and a small margin
   • Spines: remove top/right; lighten remaining
   • Grid: subtle, usually y-grid only; never compete with data
   • Whitespace: use constrained_layout=True or fig.tight_layout(); export with tight bounding box

7. Validate like a designer

   • At target size: do labels remain readable?
   • In grayscale / colorblind-safe mode: is meaning preserved?
   • Are you encoding something with color that should be encoded with position/shape instead?

8. Export cleanly

   • Notebooks: ensure high-resolution (retina) rendering when possible
   • Files: bbox_inches="tight" and small pad_inches for minimal outer whitespace

Color rules (high-aesthetic, high-clarity)

Categorical palettes

• Prefer visually equidistant colors so categories are easy to distinguish and map to a legend/key.
• If you need a custom palette, choose very different endpoints (e.g., warm vs cool) so intermediate colors are distinct.
• If using a brand color, adjust saturation/brightness as needed; hue is what people recognize most.
• Tools:
  • LearnUI “Data Visualization Color Picker” (generates equidistant palettes): https://www.learnui.design/tools/data-color-picker.html
  • Seaborn: colorblind, ColorBrewer palettes (e.g., "Set2"), or husl for many categories: https://seaborn.pydata.org/tutorial/color_palettes.html

Sequential palettes

• Use perceptually uniform sequential maps for numeric magnitude.
• Seaborn includes "rocket", "mako", "flare", "crest" and supports matplotlib maps like "viridis" / "magma".
• For lines/points on light backgrounds, avoid maps whose extremes approach white (hard to see).
• Reference: https://seaborn.pydata.org/tutorial/color_palettes.html

Diverging palettes

• Use when values diverge around a meaningful midpoint (0, baseline, target).
• Prefer a neutral midpoint; avoid muddy mid-tones.
• Tool: LearnUI Divergent Scale generator: https://www.learnui.design/tools/data-color-picker.html

Implementation: quick start

If you can, import and apply the helper:

import sys
from pathlib import Path

# Add the skill directory to sys.path if needed:
# sys.path.append(str(Path("path/to/beautiful-data-viz").resolve()))

from assets.beautiful_style import set_beautiful_style, finalize_axes

set_beautiful_style(medium="notebook", background="light")

Then create plots normally (matplotlib or seaborn), and finish with:

finalize_axes(ax, title="...", subtitle="...", tight=True)

Internal references (load only when needed)

• Design & QA checklist: references/checklist.md
• Palette selection + examples: references/palettes.md
• Copy/paste style helpers: assets/beautiful_style.py
• Plot recipes (line/bar/scatter/heatmap): examples/recipes.md

External inspiration / guidance

• Information is Beautiful (visual language inspiration): https://informationisbeautiful.net/
• LearnUI Data Viz Color Picker: https://www.learnui.design/tools/data-color-picker.html
• Berkeley Library design guide (reference): https://guides.lib.berkeley.edu/data-visualization/design
• Toyplot ethos: https://toyplot.readthedocs.io/en/stable/ethos.html
• Seaborn aesthetics + palettes: https://seaborn.pydata.org/