Jupyter

Jupyter is the de facto standard for interactive data science. v7 (2025) of the Notebook is built on JupyterLab components, offering a modern, extensible experience.

When to Use

• Exploratory Data Analysis (EDA): Plotting data inline (matplotlib).
• Education: Teaching code with markdown explanations.
• Prototyping: Testing snippets before moving to a script.

Core Concepts

Kernels

The computation engine (IPython, IJulia).

Cells

Code cells (executed) vs Markdown cells (documentation).

Magic Commands

%timeit, !pip install.

Best Practices (2025)

Do:

• Use JupyterLab: The richer, multi-tab interface is standard.
• Use nbdev: If you want to build libraries from notebooks.
• Use Version Control: Use jupytext to pair notebooks with .py files for git diffs.

Don’t:

• Don’t store secrets: Clear output before committing.

References

• Jupyter Documentation