Plotly

Website

plotly.com

Pricing URL

Official Plotly Pricing

Categories

#Application And Data #Developer Tools #App Development #App Deployment #Data Visualization #Low Code

Bokeh

Website

docs.bokeh.org

Pricing URL

-

Categories

#Charting Libraries #Data Visualization #Data Dashboard #Javascript UI Libraries

Plotly features and specs

• Interactivity
  Plotly offers highly interactive plots that allow users to pan, zoom, and hover over data points for more information. This enhances the user experience and provides deeper insights.
• High-quality visualizations
  It provides aesthetically pleasing and highly customizable charts, making it suitable for publication-quality visuals.
• Versatility
  Plotly supports multiple chart types including line charts, scatter plots, bar charts, and 3D plots, making it suitable for a wide range of applications.
• Python integration
  Plotly is well-integrated with Python and works seamlessly with other popular data science libraries like Pandas, NumPy, and Scikit-learn.
• Web-based
  The plots can be easily embedded in web applications or dashboards, making it ideal for sharing insights over the internet.
• Open-source
  Plotly offers an open-source version, which allows users to create and share visualizations without any cost.

Possible disadvantages of Plotly

• Performance
  Rendering very large datasets can sometimes be slow, which may not be suitable for real-time data visualization requirements.
• Learning curve
  Even though the library is well-documented, the extensive range of features can have a steep learning curve for beginners.
• Cost for advanced features
  While the basic functionality is free, more advanced features, such as export to certain formats and additional customizable options, require a paid subscription.
• Dependency management
  Plotly has a number of dependencies that need to be managed properly, which can sometimes complicate the setup process.
• Complexity
  For simple visualizations, Plotly might be overkill and simpler libraries like Matplotlib or Seaborn could be more appropriate.

Bokeh features and specs

• Interactive Visualizations
  Bokeh is designed specifically for creating interactive and highly customizable visualizations, making it suitable for engaging data exploration.
• Python Integration
  Bokeh integrates well with the Python ecosystem, allowing direct use of pandas, NumPy, and other Python libraries, facilitating seamless data manipulation and visualization.
• Web Compatibility
  Bokeh generates plots that are ready to be embedded into web applications, making it a powerful tool for creating dashboards and interactive reports.
• Server Functionality
  Bokeh provides a server component that allows users to build and deploy sophisticated interactive applications using just Python.
• Variety of Plotting Options
  Bokeh offers a wide range of plotting capabilities including charts, maps, and streamgraphs, enabling users to create complex visual stories.

Possible disadvantages of Bokeh

• Learning Curve
  Bokeh may have a steeper learning curve for users unfamiliar with JavaScript or those looking for a very simple or quick plotting tool.
• Performance Issues
  When dealing with very large datasets, Bokeh might suffer from performance issues, as it is primarily client-side rendering.
• Limited 3D Capabilities
  Bokeh's support for 3D plotting is limited compared to other visualization libraries like Plotly, potentially restricting its use for applications that require 3D visualizations.
• Documentation and Community Size
  While Bokeh has good documentation, its user community is smaller compared to more mature libraries like Matplotlib, which can mean fewer resources and third-party support options.

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