What is xy?

XY is a Python charting library for interactive 2D visualizations that stay smooth at millions of points and take your design system seriously. Two ideas shape the library:

• Fast, even with lots of data. XY draws the detail you can actually see instead of every row at once, so pan, zoom, and hover stay responsive as your data grows.
• Styled by your CSS, not ours. Titles, axes, legends, tooltips, and controls are addressable with plain CSS or Tailwind through 23 stable slots, and your design tokens reach the marks themselves — in the browser, in SVG, and in native PNG. What each mechanism reaches is published per renderer in the Capability Matrix, including where it stops.

All four interactive charts are live — drag to pan, scroll to zoom, and hover to inspect exact values. Together they render more than a million points from a single probability field across four chart families. View the customizable Python source.

• Browse the visual gallery to see the chart families available today.
• Install XY and build your first chart.
• Learn the composition model behind every chart.
• Read the benchmark snapshot with its output contracts and measurement caveats.
• Follow the styling overview for CSS, Tailwind, theme tokens, and rendered-mark styles.

Python teams usually face a trade-off: charting libraries that hit an interactivity ceiling as data grows, or browser-first tools that give up design control. XY is built for the workflows where that trade-off bites. Compose marks, axes, legends, and controls in Python; brand them with CSS, Tailwind, and theme tokens; and ship the same chart to notebooks, applications, and standalone HTML, PNG, or SVG exports.

Performance is part of the architecture, not an option flag. Native Rust kernels aggregate data before display, binary transport keeps numbers out of JSON, and the WebGL2 client bounds browser work by what the screen can show, while exact source data stays in Python for hover and selection.

The numbers back this up. In the recorded live interactive sweep, XY reached a correct, stable canvas in 0.071 seconds at 10,000 points and 0.081 seconds at 100 million. Matplotlib reached 13.4 seconds at 50 million points, while Plotly reached 9.8 seconds at 25 million.

Above 200,000 rows, XY's default path switches to a density view while keeping exact source rows available for deeper zooms. With density disabled, the same engine still rendered 100 million individual markers in 1.34 seconds. The benchmark publishes both paths so the effect of aggregation stays visible. Inspect the benchmark evidence or browse the chart gallery.

Install it and see for yourself:

Browse the chart gallery or jump straight to your first chart.