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.

With XY we rendered the entirety of OpenStreetMap — a 10,000,000,000 point dataset. See the example →

Important

XY is in alpha and is receiving frequent enhancements. ⭐️ Star the repo to follow the progress.

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

The base install includes the dependency-free native 2-D xy.pyplot implementation. For broader Matplotlib 3.11 Figure, Axes, Artist, layout, toolkit, widget, and animation semantics rendered by XY, install the optional compatibility extra:

pip install "xy[matplotlib]"

# or, with uv
uv add "xy[matplotlib]"

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:

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

xy.pyplot has two implementations. The configured default is the dependency-free, high-performance 2-D native shim, even when Matplotlib is already installed. Common pyplot workflows can change only the import:

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()

For broader Matplotlib 3.11 compatibility, select compat before creating a figure:

import xy.pyplot as plt

plt.set_mode("compat")
fig, axes = plt.subplots(1, 2, constrained_layout=True)

Compat mode returns genuine Matplotlib Figure, Axes, and Artist objects. Matplotlib supplies units, transforms, layout, toolkits, widgets, and animation semantics; the public module://xy.backends.backend_xy backend converts the resulting draw traversal to one XY display list. Browser, SVG, HTML, and native raster output consume that representation. Accepted gallery results may not fall back to Agg or another Matplotlib renderer. Three-dimensional axes are outside the integration and fail explicitly.

plt.set_mode("native"), plt.set_mode("compat"), and plt.set_mode("auto") are the valid calls. XY_PYPLOT_MODE provides the corresponding process-wide configuration. auto remains available as an explicit choice: it selects compat when supported Matplotlib 3.11 is installed and native otherwise. Explicit compat fails with an installation hint when the supported extra is unavailable. Switching modes requires all figures to be closed.

The permanent contract records the exact 507-source Matplotlib stable-gallery archive and excludes its 48 three-dimensional examples from XY's runnable corpus. Of the 459 checked-in sources (3.11.1 documentation sources evaluated against the pinned 3.11.0 oracle), 437 are pyplot import-swap cases (425 standard-profile and 12 extended-environment cases), while 22 backend/font/server/GUI sources are classified separately and never counted as pyplot successes or failures. The current compatibility report passes 425/425 standard and 12/12 extended cases with no execution, visual, behavioral, or fallback waivers. Compatibility requires the charts to agree in structure, labels, limits, layout, major geometry, and tolerant visual appearance—not exact pixels.

See the Matplotlib integration guide and compatibility specification for the current boundary and measured status.

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

Live interactive charts, 10k to 100M points. Every library gets every row and is driven through its own input path in a real browser. The clock stops only when the canvas is both correct (planted sentinel points verified lit) and stable (10 byte-identical frames), so progressive renderers are charged until their last chunk lands.

XY holds 0.071 s at 10k and 0.081 s at 100M, flat across four orders of magnitude, because above 200k rows it draws a screen-bounded density surface instead of one marker per row, and zoom drills back to exact rows. Every exact-marker path scales with N instead: Matplotlib crosses a second at ~3M and reaches 13.4 s at 50M; Plotly crosses at ~2.5M and reaches 9.8 s at 25M.

The pale line is XY with density=False: the same engine drawing one marker per row, no aggregation credit. It renders 100M exact markers in 1.34 s on 5.26 GiB.

Time until every point is on screen, in seconds. ✕ is a size the library did not render: Plotly never finishes constructing the figure at 50M, and Matplotlib draws at 100M but never resolves the zoom that follows.

Points	10k	100k	500k	1M	2.5M	5M	10M	25M	50M	100M
XY speedup	1×	2×	3×	4×	9×	16×	34×	89×	177×	—
XY	0.071	0.072	0.075	0.084	0.083	0.089	0.083	0.077	0.076	0.081
XY (density=False)	0.085	0.074	0.087	0.098	0.111	0.144	0.206	0.424	0.645	1.343
Matplotlib (WebAgg)	0.086	0.115	0.224	0.357	0.758	1.424	2.804	6.838	13.385	✕
Plotly (scattergl)	0.341	0.373	0.477	0.614	1.033	1.785	3.367	9.794	✕	✕

Peak Python-side resident memory, in GiB. Browser memory is tracked separately and excluded here, since a headless Chrome resides ~1 GiB before drawing anything.

Points	10k	100k	500k	1M	2.5M	5M	10M	25M	50M	100M
XY advantage	1.8×	1.7×	1.9×	2.1×	2.1×	2.4×	2.6×	2.9×	2.8×	—
XY	0.05	0.05	0.06	0.07	0.13	0.19	0.32	0.70	1.36	2.58
XY (density=False)	0.05	0.05	0.07	0.10	0.18	0.31	0.57	1.35	2.66	5.26
Matplotlib (WebAgg)	0.09	0.09	0.12	0.15	0.28	0.46	0.84	2.06	3.85	✕
Plotly (scattergl)	0.21	0.18	0.28	0.36	0.60	1.05	1.86	4.70	✕	✕

One machine (Apple M5 Pro), one run per cell; at the small end the timings carry roughly ±10 ms of run-to-run spread.

For the environment, methodology, per-size videos, and raw results, see the benchmark runbook and competitive benchmark specification.

Embed XY in a Reflex app

The Reflex integration bundled with xy turns any XY chart into a regular Reflex component, with no JavaScript, iframe, or separate chart service. Install the reflex extra to select a compatible framework version:

pip install "xy[reflex]"

# or, with uv
uv add "xy[reflex]"

The import namespace remains reflex_xy. Register the integration once:

# 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)

For state-driven charts, declare the chart in the page and supply columns from a @reflex_xy.data state method — the structure (channels, colormaps, axes) is validated when reflex run compiles the app, while the columns ride the app's own websocket as binary buffers, never through Reflex state:

from typing import TypedDict

import numpy as np
import reflex as rx
import reflex_xy


class CloudData(TypedDict):
    x: np.ndarray
    y: np.ndarray
    mag: np.ndarray


class Dash(rx.State):
    points: int = 200_000

    @reflex_xy.data
    def cloud(self) -> CloudData:
        rng = np.random.default_rng(7)
        x = rng.normal(size=self.points)
        y = x * 0.6 + rng.normal(scale=0.6, size=self.points)
        return {"x": x, "y": y, "mag": np.hypot(x, y)}


def dashboard() -> rx.Component:
    return reflex_xy.scatter_chart(
        data=Dash.cloud,
        x="x", y="y", color="mag", colormap="viridis",
        height="460px",
    )

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

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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

The declarative API and dependency-free native pyplot path prioritize broad 2-D coverage before geographic, 3-D, 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: xy.pie_chart(labels, values, hole=...) ships over unequal-width core polar bars, with Matplotlib-shaped helpers in xy.pyplot; nested donuts and variable-radius composition remain
• Candlestick / OHLC and finance overlays: SMA, VWAP, Bollinger, RSI, MACD; prototyped, awaiting a fresh landing
• Waterfall and funnel
• Treemap, sunburst, and icicle
• Gauge / indicator: build on the shipped polar axes and composable radial marks
• Slope, bump, and dumbbell
• 3-D 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.