Visualization
by eyadsibai
Use when "data visualization", "plotting", "charts", "matplotlib", "plotly", "seaborn", "graphs", "figures", "heatmap", "scatter plot", "bar chart", "interactive plots
Skill Details
Repository Files
1 file in this skill directory
name: visualization description: Use when "data visualization", "plotting", "charts", "matplotlib", "plotly", "seaborn", "graphs", "figures", "heatmap", "scatter plot", "bar chart", "interactive plots" version: 1.0.0
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/
Related Skills
Xlsx
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Clickhouse Io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Clickhouse Io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Analyzing Financial Statements
This skill calculates key financial ratios and metrics from financial statement data for investment analysis
Data Storytelling
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
Team Composition Analysis
This skill should be used when the user asks to "plan team structure", "determine hiring needs", "design org chart", "calculate compensation", "plan equity allocation", or requests organizational design and headcount planning for a startup.
Startup Financial Modeling
This skill should be used when the user asks to "create financial projections", "build a financial model", "forecast revenue", "calculate burn rate", "estimate runway", "model cash flow", or requests 3-5 year financial planning for a startup.
Kpi Dashboard Design
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.
Dbt Transformation Patterns
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
Startup Metrics Framework
This skill should be used when the user asks about "key startup metrics", "SaaS metrics", "CAC and LTV", "unit economics", "burn multiple", "rule of 40", "marketplace metrics", or requests guidance on tracking and optimizing business performance metrics.