jupyter-notebook-guide

name: jupyter-notebook-guidedescription: "Best practices for computational research notebooks with reproducible workflows"metadata:   openclaw:     emoji: "📓"    category: "tools"    subcategory: "code-exec"    keywords: ["sandbox execution", "code runner", "Jupyter notebook", "computational notebook", "reproducible research"]    source: "wentor"

Jupyter Notebook Guide

A skill for using Jupyter notebooks effectively in research contexts. Covers notebook organization, reproducibility best practices, collaboration workflows, and integration with research computing infrastructure.

Notebook Organization

Recommended Structure

Every research notebook should follow a consistent structure:

01_data_collection.ipynb     # Data acquisition and initial storage
02_data_cleaning.ipynb       # Preprocessing, validation, transformations
03_exploratory_analysis.ipynb # EDA, descriptive statistics, initial plots
04_modeling.ipynb             # Model training, evaluation, selection
05_results_visualization.ipynb # Publication-quality figures
06_supplementary.ipynb       # Additional analyses, robustness checks

Cell Organization Within a Notebook

# === CELL 1: Header and metadata ===
"""
# Analysis: Effect of Treatment on Outcome Variable
Author: [Name]
Date: 2026-03-09
Data: experiment_results_v2.csv
Dependencies: pandas>=2.0, scipy>=1.11, matplotlib>=3.8
"""

# === CELL 2: Imports and configuration ===
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats

# Reproducibility
np.random.seed(42)
pd.set_option('display.max_columns', 50)
plt.rcParams.update({
    'figure.figsize': (10, 6),
    'figure.dpi': 150,
    'font.size': 12,
    'axes.titlesize': 14,
    'savefig.dpi': 300,
    'savefig.bbox': 'tight'
})

# === CELL 3: Data loading ===
DATA_PATH = '../data/raw/experiment_results_v2.csv'
df = pd.read_csv(DATA_PATH)
print(f"Loaded {len(df)} rows, {len(df.columns)} columns")
df.head()

Reproducibility Best Practices

Environment Management

Always pin your dependencies:

# Create environment from scratch
conda create -n research python=3.11
conda activate research

# Install and pin
pip install pandas==2.1.4 scipy==1.11.4 matplotlib==3.8.2 jupyterlab==4.0.9

# Export for reproducibility
pip freeze > requirements.txt

# Or use conda
conda env export --no-builds > environment.yml

Kernel and Execution Order

# Add this cell at the top of every notebook to catch execution order issues
import IPython
print(f"Python: {IPython.sys.version}")
print(f"IPython: {IPython.__version__}")
print(f"Working directory: {os.getcwd()}")

# Run all cells from top to bottom before sharing
# Menu: Kernel -> Restart & Run All
# This verifies the notebook executes cleanly in order

Parameterized Notebooks

Use papermill for parameterized execution:

# Parameters cell (tag with "parameters" in cell metadata)
input_file = "data/experiment_001.csv"
alpha = 0.05
n_bootstrap = 1000
output_dir = "results/experiment_001"

# Execute with different parameters
papermill 04_modeling.ipynb output/run_001.ipynb \
  -p input_file "data/experiment_001.csv" \
  -p alpha 0.01 \
  -p n_bootstrap 5000

# Batch execution
for i in $(seq 1 10); do
  papermill 04_modeling.ipynb "output/run_${i}.ipynb" \
    -p input_file "data/experiment_${i}.csv"
done

JupyterLab Extensions for Research

Version Control for Notebooks

Jupyter notebooks contain output cells, which create noisy diffs. Solutions:

# Option 1: Strip outputs before committing
pip install nbstripout
nbstripout --install  # adds git filter

# Option 2: Use jupytext to maintain .py mirrors
jupytext --set-formats ipynb,py:percent notebook.ipynb
# Now edit the .py file and sync: jupytext --sync notebook.ipynb

# Option 3: Use nbdime for meaningful diffs
nbdime config-git --enable --global
git diff notebook.ipynb  # now shows structured diff

Remote Computing Integration

Connecting to HPC Clusters

# SSH tunnel to remote Jupyter server
ssh -N -L 8888:localhost:8888 user@cluster.university.edu

# On the cluster:
jupyter lab --no-browser --port=8888

# Then open http://localhost:8888 in your local browser

Google Colab Integration

For quick sharing and GPU access, export notebooks to Colab format. Add a Colab badge to your repository README for one-click access. Remember that Colab environments are ephemeral -- always save results to Google Drive or download locally.

Converting to Publications

Use jupyter-book or nbconvert to transform notebooks into LaTeX, HTML, or PDF outputs suitable for supplementary materials in journal submissions. Always run the full notebook from a clean kernel before conversion to ensure all outputs are current and reproducible.