What chart types does Plotly support?

Plotly supports 40+ chart types including basic charts (scatter, line, bar, pie), statistical charts (histogram, box, violin), scientific charts (heatmap, contour, 3D scatter), financial charts (candlestick, OHLC, waterfall), maps (choropleth, density), and specialized charts (sunburst, treemap, sankey, parallel coordinates).

What's the difference between Plotly Express and Graph Objects?

Plotly Express (`px`) creates quick visualizations from pandas DataFrames in 1-5 lines with sensible defaults, while Graph Objects (`go`) provides precise control and supports chart types Express doesn't cover (like 3D mesh and isosurface). Since Express returns a `go.Figure`, you can combine both by calling `go` methods like `update_layout()` on a `px`-built figure.

How do I export Plotly visualizations?

You can export to standalone or CDN-linked interactive HTML using `fig.write_html()`, or to static PNG/PDF/SVG images using `fig.write_image()` with the optional `kaleido` package.

Can I embed Plotly figures in web applications?

Yes, Plotly integrates with Dash, Plotly's web app framework, by embedding a figure in a `dcc.Graph` component inside a Dash app layout.

What interactivity features does Plotly include?

Built-in interactivity includes hover tooltips, pan/zoom, legend toggling, box/lasso selection, time-series rangesliders, buttons/dropdowns, and animations via the `animation_frame` parameter.

Skill

Generate Interactive Data Visualizations with Plotly

Name: Generate Interactive Data Visualizations with Plotly
Availability: OnlineOnly
Author: Antigravity

Create interactive Python charts with Plotly Express or Graph Objects, then customize layouts and export HTML or static images.

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Works with pandas

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Spark score

out of 100

Updated 29 days ago

Source checked Oct 10, 2026

Version 19.2.0

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Why it matters

Create dynamic, publication-quality charts and dashboards using Python. This asset leverages Plotly's high-level (Plotly Express) and low-level (Graph Objects) APIs to generate over 40 chart types, enabling interactive exploration and presentation of data.

Outcomes

What it gets done

Generate scatter, line, bar, histogram, and other basic charts.

Create complex statistical, scientific, financial, and 3D visualizations.

Build interactive dashboards with subplots, custom layouts, and animations.

Export visualizations as interactive HTML or static image files (PNG, PDF, SVG).

Install

Add it to your toolbox

Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-plotly | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

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Overview

A Plotly skill covering Python chart creation, API selection, layouts, interactive controls, and export. It includes scientific, statistical, financial, and dashboard examples. Use it when charts need hover details, zoom, custom styling, or web embedding. Check exported data, CDN network needs, image-export dependencies, and deployment settings.

What it does

The Plotly skill guides Python visualization work that needs interactive charts, custom styling, or web embedding. It covers choosing an API, creating figures, arranging subplots, adding interactive controls, and exporting charts. Examples span scientific visualization, statistical analysis, time series, financial charts, and multi-plot dashboards.

Plotly Express is the high-level choice for common charts with pandas DataFrames, automatic color encoding, legends, and concise code. Graph Objects provides finer control for specialized figures, individual components, multiple traces, shapes, and annotations. The approaches can be combined: a Plotly Express result is a Graph Objects Figure that can be customized further.

The chart coverage includes scatter, line, bar, pie, area, and bubble charts; histograms, box plots, violins, distributions, and error bars; heatmaps, contours, and ternary charts; candlesticks, OHLC, waterfalls, funnels, and time series. Maps, three-dimensional surfaces and meshes, sunbursts, treemaps, Sankey diagrams, parallel coordinates, and gauges extend the available visualization choices.

When to use - and when NOT to

Use the skill for dashboards, exploratory notebooks, or presentations that benefit from hover details, panning, zooming, and other interaction. It is also useful when a visualization needs a choice between rapid standard chart construction and precise component control.

Inspect exported HTML before sharing it: the file contains plotted data, including hover fields. CDN-based export requires network access. Static image export requires Kaleido, and the guide warns that current Kaleido versions may also require an available Chrome installation. Dash debug mode belongs to local development and should be disabled for deployment.

The referenced detailed guides are upstream references rather than bundled files. Follow the official documentation for dependent details, validate behavior in the target environment, and clarify missing inputs, permissions, boundaries, or success criteria before proceeding.

Inputs and outputs

Inputs include DataFrames, matrices, coordinate arrays, time-series fields, and financial price columns. Figure construction maps these to traces, axes, colors, and chart-specific properties. Chart patterns include correlation heatmaps, distribution comparisons, three-dimensional surfaces, time-series range sliders, and candlestick figures.

Layouts support shared-axis subplots, coordinated templates, discrete and continuous color scales, fonts, tick marks, grids, legends, margins, sizing, shapes, and annotations. Interactive features include custom hover tooltips, legend toggling, box and lasso selection, range sliders, buttons, dropdowns, and animations.

Outputs include interactive figures, standalone HTML, smaller CDN-backed HTML, and static PNG, PDF, or SVG images. The quick-start installation example is:

uv pip install plotly

Integrations

The skill uses pandas with Plotly Express and combines Graph Objects with the subplot helper for custom layouts. Kaleido supports static image export. Dash provides the web application route, with a graph component displaying the figure within an application layout. These examples connect figure creation, presentation, and export while keeping data exposure and deployment settings visible.

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