visualization

Original：🇺🇸 English

Translated

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

3installs

Sourceeyadsibai/ltk

Added on2026-02-16

NPX Install

npx skill4agent add eyadsibai/ltk visualization

SKILL.md Content

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Data Visualization

Python libraries for creating static and interactive visualizations.

Comparison

Library	Best For	Interactive	Learning Curve
Matplotlib	Publication, full control	No	Steep
Seaborn	Statistical, beautiful defaults	No	Easy
Plotly	Dashboards, web	Yes	Medium
Altair	Declarative, grammar of graphics	Yes	Easy

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 Type	Chart
Trends over time	Line chart
Distribution	Histogram, box plot, violin
Comparison	Bar chart, grouped bar
Relationship	Scatter, bubble
Composition	Pie, stacked bar
Correlation	Heatmap
Part-to-whole	Treemap, 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

Task	Recommendation
Publication figures	Matplotlib
Quick EDA	Seaborn
Statistical analysis	Seaborn
Interactive dashboards	Plotly
Web embedding	Plotly
Complex customization	Matplotlib

Resources

Matplotlib: https://matplotlib.org/stable/gallery/
Seaborn: https://seaborn.pydata.org/examples/
Plotly: https://plotly.com/python/

visualization

NPX Install

Tags

SKILL.md Content

Data Visualization

Comparison

Matplotlib

Seaborn

Plotly

Chart Type Selection

Design Principles

Decision Guide

Resources