{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/2"}],"enrichment":{"capability":"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.","skillfed_tags":["time-series-visualization","plotly-extension","interactive-dashboards"],"use_cases":["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."],"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\u2014when 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.\n\nThe 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.","worth_installing":"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\u2014static HTML exports won't work\u2014so confirm your deployment model first."},"id":"plotly-resampler","links":{"html":"https://skillfed.io/packages/plotly-resampler","md":"https://skillfed.io/packages/plotly-resampler.md","pypi":"https://pypi.org/project/plotly-resampler/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-08-29","license_spdx":null,"license_treatment":"permissive","name":"plotly-resampler","python_support":"supports_current","summary":"Visualizing large time series with plotly"},"popularity":{"monthly_downloads":571042,"position":5952,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.11.0"}