| name | d3blocks |
| description | Use this skill when users want to create data visualizations using D3Blocks from a pandas DataFrame. This skill helps users create scatterplots, sankey diagrams, network graphs, chord diagrams, heatmaps, treemaps, circle packing, tree diagrams, moving bubbles, time series, violin plots, particles, maps, and |
D3Blocks
D3Blocks is a Python library for creating interactive visualizations
using D3.js. It provides high-level Python APIs for visualizing
networks, scatter plots, heatmaps, treemaps, timelines, and other
data structures.
When to use D3Blocks
Use D3Blocks when the user wants to:
- Create an interactive visualization from Python data.
- Explore relationships between data points.
- Visualize networks or graphs.
- Create interactive scatter plots.
- Create heatmaps or clustered visualizations.
- Export or embed D3 visualizations.
- Customize an existing D3Blocks visualization.
- Add interactivity to a visualization.
Choosing a visualization
When the user asks about networks or relationships between entities, prefer d3graph.
When the user asks about networks or relationships but entities are hierarchical, prefer radialgraph.
When the user asks about automatic clustering of networks or relationships between entities, prefer elasticgraph.
When the user wants to understand the relationship between two continuous variables, prefer scatter.
When the user wants to compare values across two categorical dimensions, prefer heatmap.
When the user wants to visualize relationships between multiple categories in a circular layout, prefer chord.
When the user wants to visualize hierarchical data as nested circles, prefer circlepacking.
When the user wants to interactively compare or browse multiple images, prefer imageslider.
When the user wants to visualize data geographically or on a map, prefer maps.
When the user wants to visualize relationships or values between rows and columns as a matrix, prefer matrix.
When the user wants to visualize changing values or entities as animated bubbles over time, prefer movingbubbles.
When the user wants to visualize particles, movement, or dynamic particle-based patterns, prefer particles.
When the user wants to visualize flows between different entities, stages, or categories, prefer sankey.
When the user wants to visualize how one or more variables change over time, prefer timeseries.
When the user wants to visualize hierarchical data using nested rectangles sized by value, prefer treemap.
When the user wants to compare the distributions of continuous variables across groups, prefer violin.
Use matrix primarily when the user wants to inspect pairwise relationships, similarities, distances, or connections between rows and columns.
Use circlepacking when the hierarchical structure and relative containment of groups are important.
Use treemap when comparing the relative size of hierarchical categories is the primary goal.
Installation
pip install d3blocks
References
For detailed information about individual visualizations:
- Scatter plots:
references/scatter.md
- radialgraph:
references/radialgraph.md
- sankey:
references/sankey.md
- d3graph:
references/d3graph.md
- chord:
references/chord.md
- circlepacking:
references/circlepacking.md
- elasticgraph:
references/elasticgraph.md
- heatmap:
references/heatmap.md
- imageslider:
references/imageslider.md
- maps:
references/maps.md
- matrix:
references/matrix.md
- movingbubbles:
references/movingbubbles.md
- particles:
references/particles.md
- timeseries:
references/timeseries.md
- tree:
references/tree.md
- treemap:
references/treemap.md
- violin:
references/violin.md
