Plotly vs Bokeh

Professional comparison and analysis to help you choose the right software solution for your needs. Compare features, pricing, pros & cons, and make an informed decision.

Plotly icon
Plotly
Bokeh icon
Bokeh

Expert Analysis & Comparison

Struggling to choose between Plotly and Bokeh? Both products offer unique advantages, making it a tough decision.

Plotly is a Data Visualization solution with tags like python, r, javascript, excel, data-analysis, data-visualization, interactive, charts, graphs, dashboards.

It boasts features such as Interactive data visualization, Support for Python, R, JavaScript, Excel, 2D and 3D plotting, Statistical charts, Dashboards, Collaboration tools, Exporting and sharing and pros including User-friendly, High-quality visualizations, Cross-platform compatibility, Open source and free, Large gallery of examples, Active community support.

On the other hand, Bokeh is a Development product tagged with python, data-visualization, interactive, graphics, web-browser.

Its standout features include Interactive data visualization, Supports streaming data, Python library, Targets modern web browsers, Elegant and concise graphics, High-performance interactivity, Can handle large datasets, and it shines with pros like Very flexible and customizable visualizations, Integrates well with other Python data tools like NumPy and Pandas, Open source and free, Good performance even with large datasets, Nice web-based interface for sharing visualizations.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

Why Compare Plotly and Bokeh?

When evaluating Plotly versus Bokeh, both solutions serve different needs within the data visualization ecosystem. This comparison helps determine which solution aligns with your specific requirements and technical approach.

Market Position & Industry Recognition

Plotly and Bokeh have established themselves in the data visualization market. Key areas include python, r, javascript.

Technical Architecture & Implementation

The architectural differences between Plotly and Bokeh significantly impact implementation and maintenance approaches. Related technologies include python, r, javascript, excel.

Integration & Ecosystem

Both solutions integrate with various tools and platforms. Common integration points include python, r and python, data-visualization.

Decision Framework

Consider your technical requirements, team expertise, and integration needs when choosing between Plotly and Bokeh. You might also explore python, r, javascript for alternative approaches.

Feature Plotly Bokeh
Overall Score N/A N/A
Primary Category Data Visualization Development
Target Users Developers, QA Engineers QA Teams, Non-technical Users
Deployment Self-hosted, Cloud Cloud-based, SaaS
Learning Curve Moderate to Steep Easy to Moderate

Product Overview

Plotly
Plotly

Description: Plotly is an open-source graphing library for Python, R, JavaScript, and Excel. It allows users to create interactive, publication-quality graphs, charts, and dashboards that can be embedded in websites and apps. Plotly is useful for data analysis and visualization.

Type: Open Source Test Automation Framework

Founded: 2011

Primary Use: Mobile app testing automation

Supported Platforms: iOS, Android, Windows

Bokeh
Bokeh

Description: Bokeh is an interactive data visualization library for Python that targets modern web browsers for presentation. It offers elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets.

Type: Cloud-based Test Automation Platform

Founded: 2015

Primary Use: Web, mobile, and API testing

Supported Platforms: Web, iOS, Android, API

Key Features Comparison

Plotly
Plotly Features
  • Interactive data visualization
  • Support for Python, R, JavaScript, Excel
  • 2D and 3D plotting
  • Statistical charts
  • Dashboards
  • Collaboration tools
  • Exporting and sharing
Bokeh
Bokeh Features
  • Interactive data visualization
  • Supports streaming data
  • Python library
  • Targets modern web browsers
  • Elegant and concise graphics
  • High-performance interactivity
  • Can handle large datasets

Pros & Cons Analysis

Plotly
Plotly
Pros
  • User-friendly
  • High-quality visualizations
  • Cross-platform compatibility
  • Open source and free
  • Large gallery of examples
  • Active community support
Cons
  • Steep learning curve
  • Limited customization compared to matplotlib
  • Online dependency for full functionality
  • Freemium pricing model limits features
Bokeh
Bokeh
Pros
  • Very flexible and customizable visualizations
  • Integrates well with other Python data tools like NumPy and Pandas
  • Open source and free
  • Good performance even with large datasets
  • Nice web-based interface for sharing visualizations
Cons
  • Steeper learning curve than some visualization libraries
  • Visualizations can be more complex to build
  • Limited built-in statistical analysis features
  • Requires knowledge of Python and web development
  • Not as simple as drag-and-drop visualization builders

Pricing Comparison

Plotly
Plotly
  • Freemium
  • Subscription-based
Bokeh
Bokeh
  • Open Source

Get More Information

Plotly
Plotly
Plotly
Learn More About Plotly
Bokeh
Bokeh
Bokeh
Learn More About Bokeh

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