Reproducible Notebook Template & Metadata Guide

Purpose

This template helps you organize exploratory notebooks so they are repeatable, auditable, and easy to hand off. Use it to capture context, environment, datasets, tests, execution order, and outputs in a standard, lightweight way that supports validation or production handoff.

How to use this template

Create a top header cell with the metadata block shown below. Follow with a single “Parameters and Setup” cell, then split exploratory analysis and confirmatory/test cells. Keep utility functions in their own cells or modules. Run the test cells before sharing or handoff.

Example header (YAML front matter)

Place a single markdown cell at the top of the notebook with machine-readable metadata and brief human context. This example uses YAML in a markdown cell so it is visible and extractable by tools.

---
title: "Customer Churn — EDA"
author: "A. Researcher"
date: 2024-04-12
purpose: "Explore churn drivers and generate hypotheses for modelling"
dataset_name: "cust_churn_v2"
dataset_version: "2024-04-05-commit-1a2b3c"
expected_outputs:
  - "summary_table.csv"
  - "feature_candidates.csv"
  - "eda_report.html"
github_commit: "{git_commit_hash}"
notebook_version: 1
notes: "Primary exploratory notebook. Confirmatory tests in separate notebook."
---

Core metadata fields (minimum)

• title
• author (name and contact)
• date (created/last-updated)
• purpose (one sentence)
• dataset_name and dataset_version (explicit version or snapshot id)
• github_commit or repo tag (capture commit hash programmatically)
• expected_outputs (files, plots, metrics)
• notebook_version (increment on structural changes)

Environment & dependency capture

Capture the execution environment precisely and include instructions for reproducing it. Prefer an environment file (conda, pip, or Dockerfile) and a single command to recreate the environment.

Examples:

# Conda environment export (run in the environment used to create the notebook)
conda env export --no-builds > environment.yml

# or a pip freeze
pip freeze > requirements.txt

# Dockerfile snippet
FROM python:3.10-slim
COPY requirements.txt ./
RUN pip install -r requirements.txt

In the notebook, also capture the runtime at top:

import sys, platform, pkg_resources, subprocess
print('python:', sys.version)
print('platform:', platform.platform())
# optionally capture relevant package versions
import pandas as pd
print('pandas', pd.__version__)
# capture git commit
import subprocess
commit = subprocess.check_output(['git','rev-parse','HEAD']).strip().decode()
print('git commit:', commit)

Parameters & reproducible execution practice

Use a single, clearly labeled parameters cell that sets data paths, dataset versions, random seeds, and toggles for long-running steps. If you use papermill for parameterization, keep the parameter names stable.

# Parameters cell
DATA_PATH = '/data/cust_churn_v2.parquet'
DATA_VERSION = '2024-04-05-commit-1a2b3c'
RANDOM_SEED = 42
SAVE_OUTPUTS = True

Number major sections and keep execution order linear when possible. If you must run cells out of order during exploration, add a short “Execution note” that lists the required sequence for a clean run.

Test cells and sanity checks

Add short automated checks that fail fast if data assumptions are violated. Keep these tests lightweight so they can run in CI.

# Sanity checks
assert df.shape[0] > 0, 'empty dataset'
required_cols = {'customer_id','signup_date','churn_flag'}
missing = required_cols - set(df.columns)
assert len(missing) == 0, f'missing columns: {missing}'
# basic distribution checks
assert df['age'].between(0,120).all(), 'age out of bounds'

Consider using pytest or nbval in CI for more extensive validation of notebook outputs.

Data provenance and visual provenance

Record exactly which dataset snapshot was used and any transformations applied. For figures and tables, save files with names that include dataset_version and commit hash.

fig.savefig(f'figs/churn_by_region_{DATA_VERSION}_{commit[:7]}.png')
summary.to_csv(f'outputs/summary_{DATA_VERSION}_{commit[:7]}.csv', index=False)

Include a short “Results summary” cell that gives the main findings in plain language and references the saved artifacts.

Avoiding common pitfalls

• Do not hard-code local file paths without documenting how to map them in a reproducible environment.
• Separate exploratory visualizations from confirmatory tests — exploratory notebooks can generate hypotheses but should not be treated as definitive evidence.
• Log hypotheses and exploratory decisions inline (a dedicated “Hunger & Hypotheses” section is useful).
• Prefer small, repeatable transformations rather than long in-place mutation chains that are hard to audit.

Handoff and export guidelines

1. Update metadata header and notebook_version.
2. Run parameters and tests from a fresh kernel (or use papermill to parameterize and execute).
3. Save outputs (CSV, model artifacts, figures) to a versioned output folder.
4. Export a static report for reviewers: jupyter nbconvert --to html eda_notebook.ipynb.
5. Include environment.yml or requirements.txt, a short README describing run steps, and links to data snapshots.

Minimal reproducible handoff checklist

Include this checklist in the repository or attach it to the notebook when handing off.

• [] Notebook file with updated metadata header
• [] parameters cell documented and stable
• [] environment.yml or requirements.txt included
• [] git commit hash recorded in header
• [] data snapshot or dataset_version documented and accessible
• [] tests/sanity checks present and passing
• [] exported artifacts (figures, CSVs) saved with versioned names
• [] README with run instructions and expected outputs
• [] explicit list of hypotheses and next recommended steps

Suggested tooling & automation

For teams and production handoffs, consider:

• Papermill — parameterize and execute notebooks in CI
• nbval — run notebook tests in pytest
• nbconvert — generate HTML/PDF reports for reviewers
• Continuous integration pipeline step that runs core tests and commits outputs to an artifacts store
• Docker container or pinned environment to ensure bit-for-bit reproducibility

Example minimal structure for repositories

/repo-root
  /notebooks
    eda_notebook.ipynb
  /data (or link to snapshot)
  /environment
    environment.yml
  /outputs
    summary_2024-04-05-1a2b3c.csv
  README.md

Final notes (practical discipline)

The goal is not to make exploration slower — it is to make useful findings portable, auditable, and testable. Keep the exploratory flow fast, but commit a short reproducibility layer: a clear parameters cell, lightweight sanity checks, artifact saving, and an updated header. These small steps multiply the value of exploratory work when it needs to be validated, rerun, or operationalized.