Bokeh vs Dash

Overview

Dash

Stacks314

Followers408

Votes63

Bokeh

Stacks95

Followers183

Votes12

GitHub Stars20.2K

Forks4.2K

Bokeh vs Dash: What are the differences?

Introduction: Bokeh and Dash are both popular libraries for creating interactive data visualizations in Python. Understanding the key differences between the two can help choose the best tool for specific project requirements.

Programming Paradigm: Bokeh follows a declarative approach where users describe what they want to see, while Dash adopts an imperative approach where users control the flow of the application through callbacks.
Flexibility: Bokeh provides more flexibility in terms of customization and styling of visualizations, offering a wide range of features for advanced users. On the other hand, Dash focuses on simplicity and ease of use, making it suitable for quick prototyping and deployment.
Scalability: Bokeh is more suitable for handling large datasets and complex visualizations due to its efficient server-side processing capabilities and support for streaming data. Dash may face performance issues with very large datasets or real-time applications.
Community Support: Bokeh has a larger and more established community with a wide range of tutorials, examples, and extensions available. Dash, while growing rapidly, may have a smaller community in comparison, resulting in fewer resources and community-driven plugins.
Integration with Other Tools: Bokeh integrates well with other visualization libraries like Matplotlib and Seaborn, allowing users to combine different tools for enhanced visualizations. Dash, on the other hand, focuses primarily on integration with Plotly for its visualization capabilities.
Learning Curve: Bokeh can have a steeper learning curve for beginners due to its complex architecture and various customization options. In contrast, Dash's straightforward API and documentation make it easier for new users to quickly get started with building interactive web visualizations.

In Summary, understanding the key differences between Bokeh and Dash, such as programming paradigm, flexibility, scalability, community support, integration with other tools, and learning curve, can help choose the right library for specific visualization requirements.

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CLI (Node.js)

Manual

Detailed Comparison

Dash	Bokeh
Dash is an API Documentation Browser and Code Snippet Manager. Dash stores snippets of code and instantly searches offline documentation sets for 150+ APIs. You can even generate your own docsets or request docsets to be included.	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.
150+ offline docsets;Instant, fuzzy search;Great integration with other apps;Easily download docsets;Easily generate docsets:;Supports AppleDoc docsets;Supports Doxygen docsets;Supports CocoaDocs docsets;Supports Python / Sphinx docsets;Supports Ruby / RDoc docsets;Supports Javadoc docsets;Supports Scaladoc docsets;Supports Any HTML docsets;Easily switch between docsets:;Smart search profiles;Docset keywords;Documentation bookmarks;Convenient, filterable table of contents;Highlighted in-page search	interactive visualization library ; versatile graphics ; open source; https://github.com/bokeh/bokeh
Statistics
GitHub Stars -	GitHub Stars 20.2K
GitHub Forks -	GitHub Forks 4.2K
Stacks 314	Stacks 95
Followers 408	Followers 183
Votes 63	Votes 12
Pros & Cons
Pros 17 Dozens of API docs and Cheat-Sheets 12 Great for offline use 8 Excellent documentation 8 Quick API search 8 Works with Alfred	Pros 12 Beautiful Interactive charts in seconds
Integrations
No integrations available	Bootstrap Flask NGINX React Django Python Jupyter Tornado Streamlit

What are some alternatives to Dash, Bokeh?

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.

ECharts

It is an open source visualization library implemented in JavaScript, runs smoothly on PCs and mobile devices, and is compatible with most current browsers.

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.

Related Comparisons

Bokeh vs Dash: What are the differences?

Programming Paradigm: Bokeh follows a declarative approach where users describe what they want to see, while Dash adopts an imperative approach where users control the flow of the application through callbacks.
Flexibility: Bokeh provides more flexibility in terms of customization and styling of visualizations, offering a wide range of features for advanced users. On the other hand, Dash focuses on simplicity and ease of use, making it suitable for quick prototyping and deployment.
Scalability: Bokeh is more suitable for handling large datasets and complex visualizations due to its efficient server-side processing capabilities and support for streaming data. Dash may face performance issues with very large datasets or real-time applications.
Community Support: Bokeh has a larger and more established community with a wide range of tutorials, examples, and extensions available. Dash, while growing rapidly, may have a smaller community in comparison, resulting in fewer resources and community-driven plugins.
Integration with Other Tools: Bokeh integrates well with other visualization libraries like Matplotlib and Seaborn, allowing users to combine different tools for enhanced visualizations. Dash, on the other hand, focuses primarily on integration with Plotly for its visualization capabilities.
Learning Curve: Bokeh can have a steeper learning curve for beginners due to its complex architecture and various customization options. In contrast, Dash's straightforward API and documentation make it easier for new users to quickly get started with building interactive web visualizations.

Bokeh vs Dash

Overview