visualization

v2026.09.24

Use when "data visualization", "plotting", "charts", "matplotlib", "plotly", "seaborn", "graphs", "figures", "heatmap", "scatter plot", "bar chart", "interactive plots"

GitHub
安装命令
npx skhub add eyadsibai/visualization
Markdown
SKILL.md

Data Visualization

Python libraries for creating static and interactive visualizations.

Comparison

LibraryBest ForInteractiveLearning Curve
MatplotlibPublication, full controlNoSteep
SeabornStatistical, beautiful defaultsNoEasy
PlotlyDashboards, webYesMedium
AltairDeclarative, grammar of graphicsYesEasy

Matplotlib

Foundation library - everything else builds on it.

Strengths: Complete control, publication quality, extensive customization Limitations: Verbose, dated API, learning curve

Key concepts:

  • Figure: The entire canvas
  • Axes: Individual plot area (a figure can have multiple)
  • Object-oriented API: fig, ax = plt.subplots() - preferred over pyplot

Seaborn

Statistical visualization with beautiful defaults.

Strengths: One-liners for complex plots, automatic aesthetics, works with pandas Limitations: Less control than matplotlib, limited customization

Key concepts:

  • Statistical plots: histplot, boxplot, violinplot, regplot
  • Categorical plots: boxplot, stripplot, swarmplot
  • Matrix plots: heatmap, clustermap
  • Built on matplotlib - use matplotlib for fine-tuning

Plotly

Interactive, web-ready visualizations.

Strengths: Interactivity (zoom, pan, hover), web embedding, Dash integration Limitations: Large bundle size, different mental model

Key concepts:

  • Express API: High-level, similar to seaborn (px.scatter())
  • Graph Objects: Low-level, full control (go.Figure())
  • Output as HTML or embedded in web apps

Chart Type Selection

Data TypeChart
Trends over timeLine chart
DistributionHistogram, box plot, violin
ComparisonBar chart, grouped bar
RelationshipScatter, bubble
CompositionPie, stacked bar
CorrelationHeatmap
Part-to-wholeTreemap, sunburst

Design Principles

  • Data-ink ratio: Maximize data, minimize decoration
  • Color: Use sparingly, consider colorblind users
  • Labels: Always label axes, include units
  • Legend: Only when necessary, prefer direct labeling
  • Aspect ratio: ~1.6:1 (golden ratio) for most plots

Decision Guide

TaskRecommendation
Publication figuresMatplotlib
Quick EDASeaborn
Statistical analysisSeaborn
Interactive dashboardsPlotly
Web embeddingPlotly
Complex customizationMatplotlib

Resources

发现
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版本
最新版本元数据

版本

v2026.09.24

发布时间

Sep 24, 2026

分类

未分类

许可证

未指定

源路径

plugins/ltk-data/skills/visualization

默认分支

master

最新提交

f8e8569

Tree SHA

8bcd589