| name | data-visualization |
| description | Create effective data visualizations with matplotlib, seaborn, and plotly. Use when building charts, dashboards, or communicating data insights visually. |
Data Visualization
Activate this skill when creating charts, plots, or visual data presentations.
When to Use
- Creating exploratory data analysis plots
- Building publication-quality figures
- Designing interactive dashboards
- Communicating model results visually
- Comparing distributions and relationships
Libraries
- matplotlib: Foundation, full control
- seaborn: Statistical visualization, clean defaults
- plotly: Interactive charts, dashboards
- altair: Declarative, grammar of graphics
Patterns
import matplotlib.pyplot as plt
import seaborn as sns
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
sns.histplot(data=df, x="value", hue="category", ax=axes[0])
sns.scatterplot(data=df, x="feature_1", y="target", ax=axes[1])
plt.tight_layout()
plt.savefig("analysis.png", dpi=150, bbox_inches="tight")
Rules
- Always label axes and add titles
- Use colorblind-friendly palettes
- Choose chart type based on data relationship
- Keep visualizations simple and focused
- Save figures at appropriate resolution (150+ DPI)
- Include units in axis labels