A newly open-sourced Python charting library, called XY, from Reflex offers an advantage few other charting tools provide: massive scalability.
The library rethinks how to render a set of points on the computer screen, and opens new possibilities for using charts in daily workflows.
Most Python charting libraries today, such as Plotly, Bokeh, or Matplotlib, take a similar approach to creating charts: they serialize every data point and send it to the browser, where it is usually parsed by JavaScript. GPU acceleration can help, though portraying thousands to millions of rows can hog memory, slow interactions and frequently lead to crashes.
The XY library shifts all this heavy data processing to speedy Rust-built libraries, where the rendering job can be matched to the pixel/density surface of the screen. The core then only sends to the screen what the screen can physically display.
The library keeps the chart values in a ColumnStore format. The Rust-compiled libraries compute the level of detail, and transfers the results out as typed binary buffers.
A New Kind of Charting
This approach brings untold benefits, not the least of which is the memory saved by not sending data the browser can’t use.
The time it takes to render a chart stays about the same—around 80 milliseconds—regardless of whether you are plotting 10,000 or 10 million points.
While reducing the number of points, the software preserves the peaks, troughs and overall shape of the data. Even outliers and tail behavior is captured.
Another benefit is that the rendered charts are dynamic. Should the user want to zoom into a region of a chart, XY will recalculate the new area of interest. The user can drill all the way down to individual data points.
Reflex built XY to be a drop-in replacement for Matplotlib workflows. Existing charts in the pyplot format can be rerendered with XY.
The developers admit that XY doesn’t cover some charts rendered by other libraries. This initial release of XY only handles two-dimensional charts. It doesn’t draw polar or 3D charts. Overall, Plotly, Bokeh, and Matplotlib cover more chart types.
A New Horizon for Charts
The ability to quickly render large charts opens a new range of uses previously considered impracticable.
Entire datasets can be rendered without additional sampling. Dashboards can now show live data. The chart.append() method updates an existing chart, meaning dashboards can connect to a live data source without requiring a rebuild.
The library also sets the stage for sharing charts as stand-alone interactive files. A 10-million-point interactive scatter chart exports to 258 KiB of HTML, which is small enough to send as an e-mail attachment. The Plotly equivalent would be 259 MiB, Reflex calculated.
Time series data – Telemetry, market tick data, and log-derived metrics – can also be plotted. The library “renders the full series in one pass with every spike and drop intact, and zoom resolves them without a second query,” the documentation notes.
Installing XY into your Python environment is simple: either `pip install XY` or `uv add XY`, depending on which packager you are using. Import the library in your code and then the numbers can be added through a Python container. NumPy may or may not be used for this task.
Of course, XY can be integrated into Reflex, the company’s open source Python-based Web framework. Any XY chart can be turned into a Reflex component. No JavaScript, iframe, or separate chart service is required.
