What is the best way to version control Jupyter Notebooks?

Version controlling Jupyter Notebooks can be challenging due to their mix of code, output, and rich text elements. Here's a step-by-step guide to effectively version control Jupyter Notebooks:

Step 1: Understand the Format of Jupyter Notebooks
Notebook Format: Jupyter Notebooks are stored in JSON format, with a mix of source code, output, and metadata.
Challenges: The inclusion of output and metadata can create noisy diffs, making it hard to track changes effectively.

Step 2: Use Git for Version Control
Initialize Git Repository: If not already done, initialize a Git repository in your project directory using git init.
Regular Commits: Commit changes regularly with meaningful commit messages.

Step 3: Clean Outputs Before Committing
Clear Outputs: To reduce noise in diffs, clear outputs before committing. You can do this manually in Jupyter (Cell > All Output > Clear) or use a tool like nbstripout to automate it.

Step 4: Use Tools to Simplify Diffs
nbdime Tool: Use tools like nbdime for diffing and merging notebooks. nbdime integrates with Git to provide clearer diffs and merges for notebooks.
Visual Diffs: Some platforms like GitHub or tools like ReviewNB provide visual diffing for notebooks, making it easier to see changes.

Step 5: Implement Notebook-specific Branching Strategies
Branching: Use branching strategies like Git Flow to manage changes, especially when collaborating.
Merge Conflicts: Be cautious with merge conflicts in notebooks, as automatic merging might not always be reliable.

Step 6: Store Large Files with Git LFS
Git LFS: If your notebooks contain large data files or binary assets, use Git Large File Storage (LFS) to handle them efficiently.

Step 7: Exclude Sensitive Information
Environment Variables for Sensitive Data: Store sensitive information like API keys in environment variables instead of in the notebook.
.gitignore: Use .gitignore to exclude files or directories that shouldn't be version-controlled (like local configuration files).

Step 8: Modularize Code
Refactor as Modules: For complex notebooks, refactor reusable code into separate Python modules or packages. This makes version control easier and your notebooks cleaner.

Step 9: Document Changes and Dependencies
Documentation: Keep documentation within notebooks up-to-date with changes.
Dependency Tracking: Use tools like pip freeze > requirements.txt to keep track of Python dependencies.

Step 10: Regular Backups
Remote Repositories: Regularly push changes to a remote repository as a backup.
Backup Branches: Consider having backup branches for major changes.

Step 11: Collaborate and Review
Code Reviews: Use pull requests for code review and collaboration. Encourage narrative and explanatory comments in notebooks for reviewers.

Step 12: Continuous Integration/Continuous Deployment (CI/CD)
CI/CD for Notebooks: Implement CI/CD pipelines to automatically run tests on notebooks. Tools like Papermill can execute notebooks as part of a CI/CD pipeline.

Conclusion
Version controlling Jupyter Notebooks requires a combination of standard version control practices, specialized tools, and careful handling of the notebook format's peculiarities. By following these steps, you can maintain a clean, efficient version history of your notebooks, facilitating collaboration and project management.