Bokeh vs Plotly For Supporting Frontend
These days, I want to show the data visualisation result at the frontend web page. An Vue.js framework. So, we are trying to list the features of Bokeh and Plotly. This will support us to choice the right Python lib for this task.
Bokeh:
Strengths:
Highly interactive: Bokeh excels at creating visualizations with rich interactivity. You can zoom, pan, hover, select datapoints, and link multiple plots seamlessly.
Pythonic development: Bokeh is built with Python, making it intuitive for Python developers to integrate into webapplications built with frameworks like Flask or Django.
Customizability: Bokeh offers fine-grained control over the appearance and behavior of your plots.
Server-side rendering: Bokeh can handle large datasets efficiently by rendering plots on the server-side, minimizing theload on the client's browser.
Considerations:
Initial setup complexity: Setting up Bokeh for web applications can sometimes be more involved than Plotly.
Learning curve: Bokeh's extensive customization options might require a steeper learning curve.
Plotly:
Strengths:
Ease of use: Plotly provides a more streamlined and beginner-friendly API for creating basic charts and visualizationsquickly.
Dashboarding: Plotly's Dash framework is powerful for building interactive dashboards.
Extensive chart types: Plotly offers a wide variety of chart types and styling options.
Online platform (Chart Studio): Plotly's web-based Chart Studio allows for creating, editing, and sharing visualizationswithout writing code.
Considerations:
Interactivity limitations: While Plotly offers interactivity, Bokeh generally provides more flexible and powerful interactivefeatures.
JavaScript focus: Plotly primarily uses JavaScript for its API, which might be less convenient if your primary backend isPython.
Here's how to choose:
Project complexity and interactivity: For visualizations requiring high levels of customization and intricate interactivefeatures, Bokeh often gives you more flexibility. If you need more basic charts and a smooth user experience, Plotly might be abetter fit.
Development environment: If you're working primarily in a Python environment, Bokeh might be the smoother integration. Ifyou are more comfortable with JavaScript or are using frameworks like React or Angular, Plotly might be a better choice.
Data size: Both Bokeh and Plotly handle large datasets well, but Bokeh's server-side rendering can be advantageous forexceptionally large datasets.
Ease of use vs. customization: Plotly often wins in terms of ease of use, especially for quick prototyping or basic charts.Bokeh shines when you need more control and customization.
Reference
https://levelup.gitconnected.com/3-stunning-python-charts-that-every-trader-should-know-d3dd2350d4a8