{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/4"}],"enrichment":{"capability":"pyecharts wraps Apache ECharts to generate interactive data visualizations from Python, rendering charts as HTML or images with support for 30+ chart types and 400+ map files.","skillfed_tags":["data-visualization","jupyter-friendly","web-integration"],"use_cases":["Build interactive sales dashboards or business intelligence reports in Jupyter notebooks for exploratory data analysis","Generate standalone HTML visualizations for embedding in web applications or sharing via email","Create geographic heatmaps or choropleth maps using the 400+ map files for location-based data analysis","Render time-series or multi-series charts with custom styling and configuration for presentations or reports","Integrate real-time or batch-generated charts into Flask or Django web applications for live dashboards"],"what_it_does":"pyecharts is a Python binding for Apache ECharts, a data visualization library originally developed by Baidu. It lets you build interactive charts and maps in Python and export them as standalone HTML files or images. The library supports chain-style method calls for fluent API design, making it natural to build visualizations incrementally. It includes over 30 common chart types (bar, line, scatter, pie, heatmap, and others) and extensive map support for geographic data.\n\nThe package integrates with Jupyter Notebook, JupyterLab, and marimo for inline visualization, and can be embedded into Flask, Sanic, Django, and other web frameworks. It depends on jinja2 for template rendering, prettytable for table formatting, and simplejson for JSON serialization. Version 2 is based on ECharts 5.4.1+ and requires Python 3.7 or later.","worth_installing":"Yes. pyecharts is actively maintained, has low install friction, carries no known vulnerabilities, and offers a permissive MIT license. It is well-suited for anyone needing interactive charts in Python, especially for Jupyter workflows or web integration. The large ecosystem of chart types and map support makes it a practical choice for data visualization across multiple contexts."},"id":"pyecharts","links":{"html":"https://skillfed.io/packages/pyecharts","md":"https://skillfed.io/packages/pyecharts.md","pypi":"https://pypi.org/project/pyecharts/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-02-10","license_spdx":null,"license_treatment":"permissive","name":"pyecharts","python_support":"unspecified","summary":"Python options, make charting easier"},"popularity":{"monthly_downloads":1139358,"position":4310,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.1.0"}