Bokeh vs ECharts

Overview

Bokeh

Stacks95

Followers183

Votes12

GitHub Stars20.2K

Forks4.2K

ECharts

Stacks172

Followers269

Votes30

Bokeh vs ECharts: What are the differences?

Interactivity: One key difference between Bokeh and ECharts is the level of interactivity offered by each. Bokeh provides a high level of interactivity with its easily customizable tools like hover, click, zoom, and pan, making it ideal for creating dynamic visualizations where users can explore the data. On the other hand, ECharts also offers interactivity features but may require more configuration and customization compared to Bokeh.
Rendering Performance: In terms of rendering performance, Bokeh and ECharts differ. Bokeh is built on top of the BokehJS JavaScript library, which uses the HTML5 Canvas for rendering, allowing for smooth and efficient presentation of large datasets. ECharts, on the other hand, uses the ZRender rendering engine, which may have slightly lower rendering performance compared to Bokeh when handling complex visualizations or large datasets.
Customization Options: Another notable difference between Bokeh and ECharts is the level of customization options available. Bokeh provides a wide range of customization features, including support for theming, layouts, styling, and interactions, giving users more control over the appearance and behavior of their visualizations. In comparison, while ECharts also offers customization options, it may have fewer built-in tools and features for advanced customization compared to Bokeh.
Community Support: Bokeh and ECharts differ in terms of community support and resources available. Bokeh has a strong and active community of developers, extensive documentation, and a variety of tutorials and examples, making it easier for users to find help and resources when working on projects. ECharts also has a supportive community, but it may not be as extensive or well-established as Bokeh's community, leading to comparatively fewer resources and support options for ECharts users.
Back-end Integration: When it comes to integrating with backend systems and data sources, Bokeh and ECharts have differing approaches. Bokeh provides seamless integration with popular back-end tools and frameworks like Django, Flask, and Jupyter, allowing users to easily connect their visualizations with backend data sources. In contrast, ECharts may require more complex configurations and setups for back-end integration, making it potentially more time-consuming and challenging for users.
Chart Types and Features: Bokeh and ECharts offer a wide range of chart types and features, but the specific types and features available in each library may differ. Bokeh excels in providing a variety of interactive charts, geographical maps, and statistical plots, making it well-suited for data exploration and analysis. ECharts, on the other hand, may have a different set of chart types and features, such as data zooming, data filtering, and graphic transition effects, catering to different visualization requirements and preferences.

In Summary, the key differences between Bokeh and ECharts lie in the level of interactivity, rendering performance, customization options, community support, back-end integration, and available chart types and features, offering users distinct advantages based on their specific visualization needs.

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Advice on Bokeh, ECharts

Ayaskant

SSE-II at Akamai

Oct 25, 2019

Needs advice

I want to get suggestions on these 2 open source js libraries (D3.js & echarts) that help in creating charts or graphs on the UI. Which one will be better for bar graphs. Which is easy to learn and start with? Which provides better features and community support?

My requirements are 1 - Plot data in X-Y axis graph where x-axis will present time till seconds level and Y-Axis will present the data corresponding to that time.

2 - Zoom-in and zoom out feature.

56k views56k

Comments

Detailed Comparison

Bokeh	ECharts
Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets.	It is an open source visualization library implemented in JavaScript, runs smoothly on PCs and mobile devices, and is compatible with most current browsers.
interactive visualization library ; versatile graphics ; open source; https://github.com/bokeh/bokeh	Line graph; Bar graph; Scatter plot; Multidimensional visualization
Statistics
GitHub Stars 20.2K	GitHub Stars -
GitHub Forks 4.2K	GitHub Forks -
Stacks 95	Stacks 172
Followers 183	Followers 269
Votes 12	Votes 30
Pros & Cons
Pros 12 Beautiful Interactive charts in seconds	Pros 7 East to implement 6 Smaller learning curve 5 Free to use 4 Vue Compatible 3 Angular compatible Cons 2 Support is in chinese
Integrations
Bootstrap Flask NGINX React Django Python Jupyter Tornado Streamlit	Google Chrome JavaScript Firefox

What are some alternatives to Bokeh, ECharts?

D3.js

It is a JavaScript library for manipulating documents based on data. Emphasises on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework.

Highcharts

Highcharts currently supports line, spline, area, areaspline, column, bar, pie, scatter, angular gauges, arearange, areasplinerange, columnrange, bubble, box plot, error bars, funnel, waterfall and polar chart types.

Plotly.js

It is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as Plotly.py and Plotly.R). It can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more.

Chart.js

Visualize your data in 6 different ways. Each of them animated, with a load of customisation options and interactivity extensions.

Recharts

Quickly build your charts with decoupled, reusable React components. Built on top of SVG elements with a lightweight dependency on D3 submodules.

ZingChart

The most feature-rich, fully customizable JavaScript charting library available used by start-ups and the Fortune 100 alike.

amCharts

amCharts is an advanced charting library that will suit any data visualization need. Our charting solution include Column, Bar, Line, Area, Step, Step without risers, Smoothed line, Candlestick, OHLC, Pie/Donut, Radar/ Polar, XY/Scatter/Bubble, Bullet, Funnel/Pyramid charts as well as Gauges.

CanvasJS

Lightweight, Beautiful & Responsive Charts that make your dashboards fly even with millions of data points! Self-Hosted, Secure & Scalable charts that render across devices.

AnyChart

AnyChart is a flexible JavaScript (HTML5) based solution that allows you to create interactive and great looking charts. It is a cross-browser and cross-platform charting solution intended for everybody who deals with creation of dashboard, reporting, analytics, statistical, financial or any other data visualization solutions.

ApexCharts

A modern JavaScript charting library to build interactive charts and visualizations with simple API.

Related Comparisons

Bokeh vs ECharts: What are the differences?

Interactivity: One key difference between Bokeh and ECharts is the level of interactivity offered by each. Bokeh provides a high level of interactivity with its easily customizable tools like hover, click, zoom, and pan, making it ideal for creating dynamic visualizations where users can explore the data. On the other hand, ECharts also offers interactivity features but may require more configuration and customization compared to Bokeh.
Rendering Performance: In terms of rendering performance, Bokeh and ECharts differ. Bokeh is built on top of the BokehJS JavaScript library, which uses the HTML5 Canvas for rendering, allowing for smooth and efficient presentation of large datasets. ECharts, on the other hand, uses the ZRender rendering engine, which may have slightly lower rendering performance compared to Bokeh when handling complex visualizations or large datasets.
Customization Options: Another notable difference between Bokeh and ECharts is the level of customization options available. Bokeh provides a wide range of customization features, including support for theming, layouts, styling, and interactions, giving users more control over the appearance and behavior of their visualizations. In comparison, while ECharts also offers customization options, it may have fewer built-in tools and features for advanced customization compared to Bokeh.
Community Support: Bokeh and ECharts differ in terms of community support and resources available. Bokeh has a strong and active community of developers, extensive documentation, and a variety of tutorials and examples, making it easier for users to find help and resources when working on projects. ECharts also has a supportive community, but it may not be as extensive or well-established as Bokeh's community, leading to comparatively fewer resources and support options for ECharts users.
Back-end Integration: When it comes to integrating with backend systems and data sources, Bokeh and ECharts have differing approaches. Bokeh provides seamless integration with popular back-end tools and frameworks like Django, Flask, and Jupyter, allowing users to easily connect their visualizations with backend data sources. In contrast, ECharts may require more complex configurations and setups for back-end integration, making it potentially more time-consuming and challenging for users.
Chart Types and Features: Bokeh and ECharts offer a wide range of chart types and features, but the specific types and features available in each library may differ. Bokeh excels in providing a variety of interactive charts, geographical maps, and statistical plots, making it well-suited for data exploration and analysis. ECharts, on the other hand, may have a different set of chart types and features, such as data zooming, data filtering, and graphic transition effects, catering to different visualization requirements and preferences.

Bokeh vs ECharts

Overview