Converting Notebooks to Scripts and Back

Notebooks and Scripts: Best of Both Worlds

Notebooks are great for exploration but terrible for version control (.ipynb files are JSON with embedded outputs), code review, and production pipelines. Tools like jupytext and papermill bridge the gap — letting you write in notebooks and deploy as scripts.

Definition:

jupytext — Notebooks as Plain Text

Jupytext synchronizes .ipynb notebooks with plain .py files:

pip install jupytext

# Convert notebook to script
jupytext --to py:percent notebook.ipynb

# Convert script back to notebook
jupytext --to notebook script.py

# Pair: auto-sync .ipynb and .py
jupytext --set-formats ipynb,py:percent notebook.ipynb

The percent format uses # %% markers to delimit cells:

# %% [markdown]
# # Title

# %%
import numpy as np
x = np.linspace(0, 1, 100)

Version-control the .py file (human-readable diffs) and .gitignore the .ipynb file.

Definition:

papermill — Parameterized Notebook Execution

Papermill runs notebooks with different parameters from the CLI:

pip install papermill

# Run with specific parameters
papermill template.ipynb output.ipynb \
    -p snr_db 15 \
    -p modulation QPSK \
    -p n_bits 100000

In the notebook, tag a cell as parameters:

# Parameters (tag this cell as "parameters")
snr_db = 10
modulation = "BPSK"
n_bits = 50000

Papermill injects new values before execution.

Definition:

nbconvert — Export Notebooks

nbconvert exports notebooks to HTML, PDF, LaTeX, or slides:

# To HTML
jupyter nbconvert --to html notebook.ipynb

# To PDF (requires LaTeX)
jupyter nbconvert --to pdf notebook.ipynb

# To Python script
jupyter nbconvert --to script notebook.ipynb

# Clear outputs (before committing)
jupyter nbconvert --clear-output --inplace notebook.ipynb

Theorem: When to Use Notebooks vs. Scripts

Use the right tool for the task:

Task Format
Exploratory analysis Notebook
Final figure generation Script
Library/module code Script (.py)
Teaching/tutorial Notebook
Production pipeline Script (+ CLI)
Parameter sweep papermill + notebook
Code review jupytext .py

The ideal workflow: explore in notebooks, extract reusable code to .py modules, keep notebooks as thin analysis wrappers.

Notebooks are for storytelling; scripts are for automation. Use both, not exclusively one or the other.

Example: Version-Controlled Notebook with jupytext

Set up a notebook that is automatically synced with a .py file for version control.

Solution
Setup
# Pair the notebook
jupytext --set-formats ipynb,py:percent analysis.ipynb

# Add to .gitignore
echo "*.ipynb" >> .gitignore

# Commit only the .py file
git add analysis.py
git commit -m "Add analysis notebook (jupytext)"
Workflow
# Edit in Jupyter (changes sync to .py)
jupyter lab analysis.ipynb

# Or edit in VS Code (changes sync to .ipynb)
code analysis.py

# On another machine: regenerate notebook
jupytext --to notebook analysis.py

Example: Parameter Sweep with papermill

Run the same analysis notebook for multiple SNR values and collect the results.

Solution
Implementation
import papermill as pm
import pandas as pd

results = []
for snr in [5, 10, 15, 20]:
    output = f'output/analysis_snr{snr}.ipynb'
    pm.execute_notebook(
        'template.ipynb', output,
        parameters={'snr_db': snr, 'n_bits': 100000}
    )
    # Extract results from output notebook
    nb = pm.read_notebook(output)
    results.append(nb.dataframe)

all_results = pd.concat(results)
all_results.to_csv('sweep_results.csv')

Development Workflow Comparison

Compare notebook-only, script-only, and hybrid workflows for scientific projects.

Parameters

Notebook-to-Production Pipeline

Watch the progression from exploratory notebook to production script, showing how code gets extracted and tested.

Parameters

Common Mistake: .ipynb Files in Git

Mistake:

Committing .ipynb files with outputs. Every execution changes cell execution counts and outputs, creating noisy diffs that are impossible to review.

Correction:

Use jupytext to pair with .py files and commit only the .py. Or use nbstripout as a git filter to strip outputs automatically:

pip install nbstripout
nbstripout --install

Key Takeaway

Use a hybrid workflow: explore in notebooks, deploy as scripts. Use jupytext for version control, papermill for parameterized execution, and nbconvert for exporting. Keep notebooks thin — extract reusable code into .py modules.

Quick Check

What problem does jupytext solve?

Making notebooks run faster

Converting notebooks to plain text files for version control

Adding GPU support to notebooks

Compiling notebooks to native code

Correction:
Converting notebooks to plain text files for version control

jupytext syncs .ipynb with .py files, enabling clean diffs and code review.

jupytext

A tool that converts Jupyter notebooks to/from plain text formats (Python scripts, Markdown) for version control.

papermill

A tool for parameterizing and executing Jupyter notebooks from the command line, enabling batch processing and parameter sweeps.

nbconvert

A Jupyter tool for converting notebooks to HTML, PDF, LaTeX, slides, or plain Python scripts.

Pandas for Tabular Experiment DataChapter Summary