{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/4"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"}],"enrichment":{"capability":"hvPlot provides a high-level plotting API that wraps HoloViews, Bokeh, and other visualization backends, letting you create interactive plots from Pandas, Polars, XArray, Dask, and other data sources using a familiar `.plot()` syntax.","skillfed_tags":["data-visualization","interactive-plots","holoviz-ecosystem"],"use_cases":["Exploratory data analysis in Jupyter notebooks with interactive hover tooltips and zoom/pan controls.","Building interactive dashboards and data apps by combining hvplot with Panel widgets for filtering and parameter control.","Creating publication-ready plots that can be exported to static images or embedded in web applications.","Visualizing time-series data from Pandas DataFrames or XArray datasets with linked axes and cross-filtering.","Rapid prototyping of multi-backend visualizations (Bokeh, Matplotlib, Plotly) without rewriting plot code."],"what_it_does":"hvPlot is a high-level visualization library that sits on top of HoloViews and plotting backends like Bokeh, Matplotlib, and Plotly. It exposes a familiar `.hvplot()` API modeled after Pandas' `.plot()` method, so if you know how to plot with Pandas, you can immediately use hvPlot to create interactive visualizations. It works with multiple data sources\u2014Pandas DataFrames, Polars, XArray, Dask, and others\u2014and lets you switch between rendering backends without changing your code.\n\nThe library is designed for three main workflows: exploratory data analysis (interactive plots in notebooks), reporting (static or embedded visualizations), and building data apps (combining plots with interactive widgets via Panel). It abstracts away much of the complexity of configuring HoloViews and Bokeh directly, making interactive visualization accessible to developers who want a simple, Pandas-like interface.","worth_installing":"Yes. hvPlot is actively maintained, has no known vulnerabilities, and offers low install friction. It's well-suited if you want interactive plots without learning HoloViews or Bokeh directly. Choose it if you're already comfortable with Pandas' `.plot()` API and want to upgrade to interactivity; skip it if you need fine-grained control over plot styling or are committed to a single backend like pure Matplotlib."},"id":"hvplot","links":{"html":"https://skillfed.io/packages/hvplot","md":"https://skillfed.io/packages/hvplot.md","pypi":"https://pypi.org/project/hvplot/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-12-18","license_spdx":null,"license_treatment":"permissive","name":"hvplot","python_support":"supports_current","summary":"A high-level plotting API for the PyData ecosystem built on HoloViews."},"popularity":{"monthly_downloads":1412041,"position":3937,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.12.2"}