Learn BEAKERX with Real Code Examples

Mix Python and Java code in a single notebook

Create interactive tables for data exploration

Use advanced plotting for scientific visualization

Develop educational notebooks with interactive widgets

Prototype multi-language algorithms in one environment

Ensure correct kernel is selected for each cell

Verify Java runtime installation for JVM languages

Check library dependencies in Python or JVM environment

Restart kernel if cell execution fails

Consult BeakerX documentation for widget setup issues

Run cells and verify outputs for correctness

Test polyglot integration with multi-language cells

Check interactive widgets for functionality

Validate data visualizations for accuracy

Ensure kernels are installed and functioning properly

Not designed for production deployment

Use for teaching, experimentation, and prototyping

Share notebooks via GitHub or JupyterHub

Export to HTML or PDF for presentation

Integrate with cloud-hosted Jupyter services if needed

BeakerX kernels for Python, Java, Kotlin, Groovy, Scala

Interactive plotting libraries

Table widgets for data manipulation

Jupyter Notebook or Lab interface

Markdown and rich text support

Jupyter Notebook and JupyterLab

Python and JVM libraries

Matplotlib, Bokeh, Plotly for visualization

Data analysis libraries like pandas or Apache Spark

Version control via Git for notebooks

Organize notebooks with clear markdown sections

Use widgets for interactive demonstrations

Combine languages thoughtfully to avoid confusion

Test kernel outputs and visualizations frequently

Share notebooks via GitHub or JupyterHub

Kernel setup can be complex for multiple languages

Requires Java runtime for JVM languages

Interactive widgets may require configuration

Performance may vary by machine or server

Limited cloud-hosted support compared to Colab