Jupyter Notebook Patterns

When to Use

Notebook vs Script Decision Table

Scenario	Notebook	Script	Hybrid
Exploratory analysis, one-off	Yes
Production data pipeline		Yes
Report with inline visuals	Yes
Shared team utility		Yes
Parameterized batch runs			Papermill
ML experiment tracking	Yes
CI/CD artifact generation			nbconvert
Code review required		Yes	nbstripout + notebook

Clean Diffs with nbstripout

Notebook JSON includes execution counts, cell outputs, and metadata noise. Strip it.

Git Filter Setup

bash

# Install
pip install nbstripout

# Configure for repo (writes to .gitattributes + .git/config)
nbstripout --install

# Or configure globally
nbstripout --install --global

# Verify
cat .gitattributes
# *.ipynb filter=nbstripout

Manual .gitattributes (when you need custom config)

gitattributes

*.ipynb filter=nbstripout
*.ipynb diff=ipynb

Pre-commit hook alternative

yaml

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/kynan/nbstripout
    rev: 0.7.1
    hooks:
      - id: nbstripout
        args: [--extra-keys, "metadata.kernelspec metadata.language_info"]

The --extra-keys flag strips kernel metadata that causes spurious diffs across environments.

Parameterized Execution with Papermill

Basic Parameterized Notebook

Tag a cell with parameters in notebook metadata. Papermill injects a new cell after it.

python

# Cell tagged "parameters"
input_path = "data/default.csv"
n_samples = 1000
model_type = "xgboost"
output_dir = "results/"

Execute Programmatically

python

import papermill as pm

pm.execute_notebook(
    "template.ipynb",
    "output/run_2024_01.ipynb",
    parameters={
        "input_path": "data/january.csv",
        "n_samples": 5000,
        "model_type": "lightgbm",
        "output_dir": "results/january/",
    },
    kernel_name="python3",
    cwd=".",  # working directory for execution
)

Batch Execution Pattern

python

from pathlib import Path
import papermill as pm

configs = [
    {"input_path": f"data/{m}.csv", "month": m}
    for m in ["jan", "feb", "mar"]
]

for cfg in configs:
    output = Path(f"output/{cfg['month']}_report.ipynb")
    output.parent.mkdir(parents=True, exist_ok=True)
    pm.execute_notebook(
        "template.ipynb",
        str(output),
        parameters=cfg,
        request_save_on_cell_execute=True,  # checkpoint after each cell
    )

Notebook-to-Script Conversion

nbconvert CLI

bash

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

# To HTML report (execute first)
jupyter nbconvert --to html --execute notebook.ipynb --no-input

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

Programmatic Conversion

python

from nbconvert import PythonExporter
import nbformat

nb = nbformat.read("analysis.ipynb", as_version=4)
exporter = PythonExporter()
script, _ = exporter.from_notebook_node(nb)

# Strip "# In[...]" markers
import re
script = re.sub(r"# In\[.*?\]:\n", "", script)

Path("analysis.py").write_text(script)

jupytext for Round-Trip Sync

bash

# Pair notebook with .py percent format
jupytext --set-formats ipynb,py:percent notebook.ipynb

# Sync after editing either file
jupytext --sync notebook.ipynb

Testing Notebooks

conftest.py for Notebook Execution Tests

python

# tests/conftest.py
import subprocess
import pytest
from pathlib import Path

NOTEBOOK_DIR = Path("notebooks")

def collect_notebooks():
    return sorted(NOTEBOOK_DIR.glob("*.ipynb"))

@pytest.fixture(params=collect_notebooks(), ids=lambda p: p.stem)
def notebook_path(request):
    return request.param

def execute_notebook(path, timeout=300):
    """Execute notebook and return exit code."""
    result = subprocess.run(
        [
            "jupyter", "nbconvert",
            "--to", "notebook",
            "--execute",
            "--ExecutePreprocessor.timeout=" + str(timeout),
            "--output", "/dev/null",
            str(path),
        ],
        capture_output=True,
        text=True,
    )
    return result

Test File

python

# tests/test_notebooks.py
def test_notebook_executes(notebook_path):
    result = execute_notebook(notebook_path)
    assert result.returncode == 0, (
        f"Notebook {notebook_path.name} failed:\n{result.stderr[-500:]}"
    )

Targeted Cell Testing with testbook

python

from testbook import testbook

@testbook("notebooks/preprocessing.ipynb", execute=["setup", "clean_data"])
def test_cleaning_removes_nulls(tb):
    df = tb.ref("df_clean")
    assert df.isnull().sum().sum() == 0

Gotchas and Anti-Patterns

Hidden State Bugs

•Problem: Running cells out of order creates unreproducible state.
•Fix: Kernel > Restart & Run All before any commit or share. Automate with --execute in CI.

Out-of-Order Execution

•Problem: Cell 5 depends on cell 10's output because you ran them manually out of order.
•Fix: Structure notebooks linearly. Use nbval or --execute in CI to catch this.

Large Notebooks in Git

•Problem: Output cells with images/dataframes bloat repo history.
•Fix: nbstripout (above). For intentional output storage, use nbconvert --to html and store HTML artifacts instead.

Kernel Dependency Drift

•Problem: \!pip install inside notebooks creates unreproducible environments.
•Fix: Pin deps in requirements.txt or pyproject.toml. Use %pip install (not \!pip) if you must install in-notebook -- it targets the correct kernel.

Import Spaghetti

•Problem: Imports scattered across cells, hard to extract to scripts.
•Fix: First code cell = all imports. Tag it for Papermill to skip if needed.