bokeh python VS python xlrd

• Interactivity
  Bokeh provides interactive plots and dashboards that can enhance the user experience by allowing them to explore data by zooming, panning, and hovering.
• Web Integration
  It generates outputs that are readily usable in web applications. Bokeh plots can be embedded in web pages, making it suitable for creating dashboards and web-based data visualization applications.
• Versatility
  Bokeh supports a wide variety of plots and chart types, which allows users to create complex and informative visualizations.
• Pythonic Syntax
  The library has an API that is intuitive for Python users, making it easier to learn and integrate into Python-based projects.
• Server for Real-time Updates
  Bokeh server allows for the creation of interactive, real-time streaming web applications, which is useful for applications requiring live data updates.

Possible disadvantages

• Learning Curve
  Despite its intuitive syntax, Bokeh's extensive capabilities and features can present a steeper learning curve, particularly for beginners in data visualization.
• Rendering Performance
  For very large datasets, Bokeh might encounter performance issues, such as slower rendering times in the browser compared to other digital visualization technologies.
• Limited 3D Capabilities
  Unlike some other visualization libraries, Bokeh’s support for 3D plotting is limited, which might be a constraint for users needing advanced 3D plotting features.
• Complexity with Advanced Plots
  While Bokeh is great for basic plots, creating highly customized or advanced visualizations may require more effort, with users potentially needing to write custom JavaScript callbacks.
• Dependencies
  Bokeh’s reliance on JavaScript and other underlying libraries might pose challenges in environments where managing dependencies is complex.

• Simplicity
  xlrd provides a straightforward and easy-to-use API for reading Excel files, making it accessible for beginners and quick implementations.
• Widely Used
  xlrd has been a popular choice for handling Excel files in Python, which means there is a lot of available documentation and community support.
• Efficient Reading
  It is optimized for reading data from Excel files without loading entire data into memory, which is beneficial for handling large files.

Possible disadvantages

• No Write Support
  xlrd is designed solely for reading, and it does not support writing or modifying Excel files.
• Limited to Older Excel Formats
  With version 2.0 and above, xlrd only supports the older .xls Excel file format and does not support .xlsx files.
• Deprecated Features
  Due to changes in dependencies and updates to Excel formats, some features in xlrd have become deprecated or removed, which can limit its functionality.

Tracking bokeh python since Mar 2021.

• I need to read multiple excel files, extract a column from each and compose a new file

  So to get this out of the way first, xlrd has less features than openpyxl and in addition only works with the old '.xls' format, not the newer '.xlsx' format. Even on the xlrd's Github repo it says: 'Please use openpyxl where you can... '. Source: over 4 years ago

• Sending Bulk SMS using Africas Talking, Python and Excel

  There are few alternative libraries for reading and writing excel files: Pandas, Xlrd , openpyxl among others. In the end I settled for openpyxl as I had the most experience Using it and it had support for .xlsx files. - Source: dev.to / over 5 years ago