plotly-resampler

What it is and what it does

plotly-resampler wraps Plotly figures to add scalable visualization of large time-series data. Instead of rendering millions of points at once, it dynamically aggregates data based on the current view—when you pan or zoom, the library resamples the underlying dataset to show only the most relevant points, keeping the interface responsive. It uses the MinMaxLTTB algorithm from tsdownsample to select approximately 1000 representative points for display.

The package integrates with Plotly's standard figure construction, so you can use it with plotly.graph_objects or plotly.express with minimal code changes. It supports two modes: FigureResampler for Dash web applications with server-side callbacks, and FigureWidgetResampler for Jupyter notebooks using widget events. A convenience function, register_plotly_resampler, can wrap all figures automatically in a notebook.

Use it for:

• Visualize financial time series with millions of OHLC candles or tick data without freezing the browser.
• Explore sensor or IoT data streams in Jupyter notebooks with smooth panning and zooming interactions.
• Build interactive dashboards displaying high-frequency data (e.g., stock ticks, network metrics) with Dash callbacks.
• Analyze scientific time-series data (e.g., seismic, astronomical) where raw datasets exceed typical rendering limits.
• Prototype data exploration tools that need responsive performance on large datasets before optimization.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Adds dynamic data aggregation to Plotly figures, enabling responsive visualization of large time-series datasets by resampling data in real time as users pan and zoom.

Yes, if you need to visualize large time-series datasets interactively. The package solves a real scalability problem in Plotly and integrates cleanly with existing code. Install friction is low, maintenance is stable (aging but not abandoned), and the MIT license carries no restrictions. The main gotcha is that dynamic aggregation requires Jupyter or Dash—static HTML exports won't work—so confirm your deployment model first.

Install

plotly-resampler on PyPI

pip install plotly-resampler

uv add plotly-resampler

poetry add plotly-resampler

Installing plotly-resampler

Before you install

Low friction installation with a pure-Python wheel. Maintenance status is aging—last release was 350 days ago—but the repository remains active with recent commits and no archived status.

License in practice

MIT license permits unrestricted use, modification, and distribution in both open-source and proprietary projects, with only attribution required.

Quickstart

pip install plotly-resampler

import plotly.graph_objects as go
import numpy as np
from plotly_resampler import register_plotly_resampler

register_plotly_resampler(mode='auto')
x = np.arange(1_000_000)
y = np.sin(x / 200)
fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=y))
fig

Dynamic aggregation requires either IPython/Jupyter environment (FigureWidgetResampler) or Dash web app (FigureResampler with show_dash); static .show() calls do not enable resampling.

Verify before relying

• Performance characteristics and memory overhead when handling datasets larger than the example's 110,000,000 points.
• Compatibility with all Plotly trace types beyond Scatter and Scattergl.
• Behavior and responsiveness of dynamic aggregation on slower network connections or with high-latency Dash deployments.

Package facts

Evidence: plotly_resampler-0.11.0-py3-none-any.whl

Keywords: time-series, visualization, resampling, plotly, plotly-dash

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