Chapter Summary

Chapter Summary

Key Points

• 1.

  JupyterLab is the standard for interactive scientific computing. Install essential extensions (ipywidgets, autoreload, git). Use SSH tunnels for remote access. Always "Restart Kernel and Run All" before sharing a notebook.

• 2.

  Follow the analysis notebook pattern. Title + imports + config + data loading + analysis + conclusions. Keep cells short (<30 lines). Use Markdown for narrative. Enable %autoreload 2 when developing modules alongside notebooks.

• 3.

  Pandas is the tool for tabular experiment data. Store simulation results as tidy DataFrames (one row per observation). Use groupby + agg for summaries, pivot_table for matrices, merge for joining tables. Always vectorize — never use iterrows.

• 4.

  Use jupytext for version control. Pair .ipynb with .py:percent files. Commit only the .py — clean diffs, easy review. Use nbstripout as a fallback.

• 5.

  Use papermill for parameterized execution. Run the same notebook with different parameters from the CLI or a script. This enables batch processing and parameter sweeps without duplicating notebooks.