Bokeh

Bokeh is a Python data visualization library and interactive plotting framework used to create high-performance graphics and data dashboards that render in web browsers. It serves as a tool for generating standalone HTML documents, embedded components for digital notebooks, and full-stack web applications powered by a Python backend.

The project distinguishes itself through its ability to handle large or streaming datasets while maintaining smooth interactivity. It enables linked brushing across multiple views, allowing data selected in one plot to automatically highlight corresponding data in others.

The system covers broad capability areas including the composition of complex data analysis dashboards and the customization of visual appearances through themes and styling. It provides a high-level programming interface for rendering interactive charts and exporting visualizations as static images or HTML files.

Features

Plotting and Visualization - Provides a high-level Python programming interface to generate interactive plots and data applications that render in web browsers.
Python Visualization - Serves as a primary Python library for creating high-performance interactive plots and data dashboards that render in web browsers.
High-Performance Visualizers - Maintains high performance when rendering large or streaming datasets using optimized web graphics.
Data Visualization Dashboards - Enables the composition of interactive plots and controls into comprehensive visual applications for complex data analysis.
Interactive Data Charting - Enables the creation of high-performance, interactive charts and graphs in a web browser using Python.
Client Side Rendering - Uses a dedicated JavaScript library to render interactive graphics directly in the browser using HTML5 Canvas or WebGL.
Interactive Plotting Frameworks - Provides a framework for rendering high-performance graphics and streaming datasets in browsers via a Python backend.
Server-Side Interaction Callbacks - Triggers Python functions based on browser-side UI interactions by mapping JavaScript events to server-side handlers.
JSON Serializers - Serializes plot configurations into JSON objects to synchronize state between the Python backend and the JavaScript frontend.
Columnar Formats - Organizes datasets into named arrays using a columnar format to optimize data transfer from Python to the browser.
Data Science Notebooks - Embeds interactive plots and standalone HTML components into data science notebooks for sharing and reporting.
Data Brushing Tools - Connects multiple plots so that selecting data in one view automatically highlights corresponding data in other views.
Visualization Exporters - Generates plots as static image files, standalone HTML documents, or embedded components for digital notebooks.
Hybrid Rendering Controls - Creates data visualizations in a web browser using scripts that support server-side control or independent execution.
Dashboard Layout Engines - Provides a layout engine for arranging visual components into complex grids and hierarchical dashboard containers.
Visual Styling Frameworks - Allows customizing the visual appearance of charts through style sheets and predefined themes.
Backend Servers - Supports running a backend server to power complex, real-time interactive data applications with dynamic updates.
Data App Frameworks - Facilitates the development of full-stack web applications with a Python backend to power dynamic data visualizations.
WebSocket Communication - Implements a persistent WebSocket connection to synchronize data changes and user interactions between the Python server and the browser.
Data Visualization - Interactive visualization library for modern web browsers.
Visualization and Analysis - Interactive visualization library for modern web browsers.
Data Analysis and Processing - Interactive browser-based visualization.
Data Visualization - Interactive web-based plotting for Python.
Vizualizarea datelor și analiză - Vizualizare interactivă a datelor pentru browsere web.
Python Projects - Listed in the “Python Projects” section of the Awesome For Beginners awesome list.
Visualization - Interactive visualization library for modern web browsers.

Istoric stele

bokehbokeh

Name: bokeh/bokeh
Author: bokeh

View on GitHub

20,403 stele4,259 fork-uriTypeScriptBSD-3-Clause10 vizualizăribokeh.org

Bokeh

Features

Plotting and Visualization - Provides a high-level Python programming interface to generate interactive plots and data applications that render in web browsers.
Python Visualization - Serves as a primary Python library for creating high-performance interactive plots and data dashboards that render in web browsers.
High-Performance Visualizers - Maintains high performance when rendering large or streaming datasets using optimized web graphics.
Data Visualization Dashboards - Enables the composition of interactive plots and controls into comprehensive visual applications for complex data analysis.
Interactive Data Charting - Enables the creation of high-performance, interactive charts and graphs in a web browser using Python.
Client Side Rendering - Uses a dedicated JavaScript library to render interactive graphics directly in the browser using HTML5 Canvas or WebGL.
Interactive Plotting Frameworks - Provides a framework for rendering high-performance graphics and streaming datasets in browsers via a Python backend.
Server-Side Interaction Callbacks - Triggers Python functions based on browser-side UI interactions by mapping JavaScript events to server-side handlers.
JSON Serializers - Serializes plot configurations into JSON objects to synchronize state between the Python backend and the JavaScript frontend.
Columnar Formats - Organizes datasets into named arrays using a columnar format to optimize data transfer from Python to the browser.
Data Science Notebooks - Embeds interactive plots and standalone HTML components into data science notebooks for sharing and reporting.
Data Brushing Tools - Connects multiple plots so that selecting data in one view automatically highlights corresponding data in other views.
Visualization Exporters - Generates plots as static image files, standalone HTML documents, or embedded components for digital notebooks.
Hybrid Rendering Controls - Creates data visualizations in a web browser using scripts that support server-side control or independent execution.
Dashboard Layout Engines - Provides a layout engine for arranging visual components into complex grids and hierarchical dashboard containers.
Visual Styling Frameworks - Allows customizing the visual appearance of charts through style sheets and predefined themes.
Backend Servers - Supports running a backend server to power complex, real-time interactive data applications with dynamic updates.
Data App Frameworks - Facilitates the development of full-stack web applications with a Python backend to power dynamic data visualizations.
WebSocket Communication - Implements a persistent WebSocket connection to synchronize data changes and user interactions between the Python server and the browser.
Data Visualization - Interactive visualization library for modern web browsers.
Visualization and Analysis - Interactive visualization library for modern web browsers.
Data Analysis and Processing - Interactive browser-based visualization.
Data Visualization - Interactive web-based plotting for Python.
Vizualizarea datelor și analiză - Vizualizare interactivă a datelor pentru browsere web.
Python Projects - Listed in the “Python Projects” section of the Awesome For Beginners awesome list.
Visualization - Interactive visualization library for modern web browsers.

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Întrebări frecvente

Ce face bokeh/bokeh?

Care sunt principalele funcționalități ale bokeh/bokeh?

Principalele funcționalități ale bokeh/bokeh sunt: Plotting and Visualization, Python Visualization, High-Performance Visualizers, Data Visualization Dashboards, Interactive Data Charting, Client Side Rendering, Interactive Plotting Frameworks, Server-Side Interaction Callbacks.

Care sunt câteva alternative open-source pentru bokeh/bokeh?

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