xy · PyPI

XY-shaped probability field shown as a binned scatter chart.

XY is an extremely fast, interactive, customizable Python charting library for the web, notebooks, and static exports.

Charts are composed declaratively or through matplotlib conventions. You can fully customize them with Python, CSS, or Tailwind.

With small charts, every point is sent to the browser. For large charts, the Rust core computes only what the screen needs to display, based on its resolution. Pan, zoom, hover, and selection can show full details by running the same process for the new range, and a selection returns the original rows.

XY is early alpha, and is receiving frequent enhancements. Any contributions are appreciated!

Is XY right for me?

XY is for Python users who want one flexible charting library for everything from everyday plots to custom application visuals and large datasets. Build a chart once, then use it in notebooks and web apps or export it as HTML, PNG, SVG, or PDF.

Installation

pip install xy

# or, with uv
uv add xy

Getting started

A chart is a container plus the marks inside it. Any sequence works; NumPy is optional.

import xy

chart = xy.line_chart(xy.line([1, 2, 3, 4, 5], [120, 180, 165, 240, 310]))
# chart.to_html("chart.html")
# chart.to_png("chart.png")
# chart.to_svg("chart.svg")
chart  # notebooks render it

The same API scales to a hundred million points as a density surface:

A hundred-million-point spiral rendered as a density surface, then zoomed until the surface resolves into individual points.

import numpy as np

import xy

rng = np.random.default_rng(7)
n = 100_000_000

r = 6.0 * rng.beta(1.2, 3.0, n)
theta = 2.9 * np.log1p(r) + rng.integers(0, 4, n) * (np.pi / 2) + rng.normal(0, 0.045 + 0.016 * r, n)

chart = xy.scatter_chart(
    xy.scatter(
        r * np.cos(theta),
        r * np.sin(theta),
        color=np.exp(-r / 2.2),
        colormap="magma_r",
        density=True,
        opacity=0.85,
        # Grow and solidify markers once a view drills through to real rows.
        size=2.5,
        zoom_size_factor=2.6,
        zoom_opacity=0.95,
    ),
    xy.theme(
        background="#ffffff", plot_background="#ffffff", grid_color="#e6e6e1",
        axis_color="#c3c2b7", text_color="#0b0b0b",
    ),
    title="100 million points",
)
chart

Coming from matplotlib

For common pyplot workflows, change the import and keep the plotting code:

import numpy as np
import xy.pyplot as plt

x = np.linspace(0, 10, 200)
fig, ax = plt.subplots()
ax.plot(x, np.sin(x), "r--", label="signal")
ax.legend()
plt.show()

See the compatibility guide; not all charts and functionality are supported yet.

Customize every layer

Use Python to control the chart, from marks and axes to interactions and layout.

Marks: Control color, size, opacity, symbols, gradients, strokes, curves, and colormaps.
Guides: Customize axes, ticks, grids, annotations, legends, colorbars, and tooltips.
Interaction: Add pan, zoom, hover, selections, crosshairs, callbacks, and linked charts.
Layout: Create layers and facets, set responsive dimensions, and apply themes.

chart = xy.line_chart(
    xy.line(x, y, color="#7c3aed", width=3),
    class_name="rounded-xl bg-white",
    class_names={"tooltip": "rounded-lg bg-zinc-900 text-white"},
)

See the styling guide for examples. For a detailed breakdown of what can be customized, see the capability matrix.

Benchmarks

Cold-render time for a 10-million-point chart in XY, Matplotlib, and Plotly. Lower is better.

In the recorded 10-million-point launch baseline, XY wrote a static PNG in 0.018 s against 2.7 s for Matplotlib and 9.6 s for Plotly, and reached first interactive render 16–18× sooner. The baseline uses identical seeded data, a 900×420 output, and three isolated cold runs.

For the environment, methodology, and raw results, see the launch report, benchmark runbook, and competitive benchmark specification.

Embed XY in a Reflex app

The reflex-xy adapter turns any XY chart into a regular Reflex component, with no JavaScript, iframe, or separate chart service. It ships as its own package and pulls in xy and reflex:

pip install reflex-xy

# or, with uv
uv add reflex-xy

# rxconfig.py
import reflex as rx
import reflex_xy

config = rx.Config(
    app_name="dashboard",
    plugins=[reflex_xy.XYPlugin()],
)

Then add a chart anywhere in the component tree:

import reflex as rx
import reflex_xy
import xy

signups = xy.line_chart(
    xy.line([1, 2, 3, 4, 5], [120, 180, 165, 240, 310]),
    title="Weekly signups",
)


def index() -> rx.Component:
    return rx.card(
        rx.heading("Growth"),
        reflex_xy.chart(signups, height="320px"),
        width="100%",
    )


app = rx.App()
app.add_page(index)

Hover, pan, and zoom keep working. For charts driven by Reflex state, events, or live streams, see the Reflex integration guide and the runnable example app.

Examples

Each notebook fetches its rows from the linked public source; no raw datasets are stored in this repository. Counts describe the featured chart, and the notebooks scale further. See the example guide for sources, workload controls, and setup.


Gaia DR3 · HR diagram _{250,000 plotted stars} Open notebook	gnomAD v4.1 · allele frequency _{164,000 plotted variants} Open notebook	Pan-UKBB · Manhattan plot _{814,294 plotted variants} Open notebook
Dukascopy · EUR/USD ticks _{101,427 plotted ticks} Open notebook	LIGO · GW150914 strain _{16,777,216 raw · 3,441 shown} Open notebook	NYC TLC · taxi pickup density _{300,000 pickup records} Open notebook

How it works

Most chart stacks serialize every value as JSON and ask the browser to draw every mark. XY keeps exact values in a ColumnStore, computes a level of detail in Rust, and transfers typed binary buffers. Decimated and density views are bounded by the visible result.

flowchart TB
    API["Python API<br/>Build the chart"]
    STORE["ColumnStore<br/>Keep canonical f64 columns"]
    CORE["Native Rust compute<br/>Direct · decimated · density"]
    PAYLOAD["Compact payload<br/>Data-less JSON spec + typed binary buffers"]
    RENDER["Browser or notebook<br/>WebGL2 marks · Canvas axes · DOM interface"]

    API --> STORE --> CORE --> PAYLOAD --> RENDER

So a dense overview can aggregate while a narrow view returns exact points. With a live host, pan and zoom request a refined payload. Canonical f64 data stays in Python, so hover and selection still return original rows.

For the full design, see the design dossier.

Roadmap

Broad 2D coverage first, then geographic, 3D, and volume visualization. Queued next, no dates implied:

Categorical distributions: strip, swarm, beeswarm, boxen, rug
Regression diagnostics: trendline, residual, QQ, PP
Scatter matrix and joint plots: SPLOM, pair grid, marginal histograms
Pie / donut: in xy.pyplot today, promoting to xy.pie_chart(xy.pie(...))
Candlestick / OHLC and finance overlays: SMA, VWAP, Bollinger, RSI, MACD; prototyped, awaiting a fresh landing
Waterfall and funnel
Treemap, sunburst, and icicle
Radar / polar and gauge: needs polar axes first
Slope, bump, and dumbbell
3D and volume: scatter, surfaces, meshes, isosurfaces, and volumetric views

The full ranked backlog is in the chart roadmap. Want a chart or feature that isn't listed? Open an issue.

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Aug 1, 2026

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Jul 28, 2026

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Jul 25, 2026

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Jul 17, 2026

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