Introduction
Jupyter Notebooks are widely used to code. But their default .ipynb
format is not ideal for version control, making collaboration difficult.
Jupytext solves this issue by allowing notebooks to be stored as plain-text files like .py
, making them easier to track, edit, and share using Git.
In this article, we’ll explore Jupytext through code examples, screenshots, and practical applications.
Installation & Setup
Installation
If you're using Jupyter Notebook inside VS Code:
- Open VS Code Terminal (
Ctrl + ~
). - Navigate to your project folder:
cd path\to\your\project
- Create a virtual environment:
python -m venv venv
- Activate the virtual environment:
Windows:venv\Scripts\activate
- Install Jupytext:
pip install jupytext
Setup
- In VS Code, open Command Palette (
Ctrl + Shift + P
). - Search for "Python: Select Interpreter" and select the interpreter from your virtual environment.
- Open a new Jupyter Notebook (
.ipynb
) in VS Code. - Enable Jupytext:
jupyter labextension install jupyterlab-jupytext
- Restart VS Code.
Now, Jupytext is installed and ready to use within VS Code Jupyter Notebook.
Key Features & Explanation
- Convert notebooks to plain-text formats (
.py
,.md
,.Rmd
) for better version control. - Sync
.ipynb
and text-based files so that changes in one format reflect in the other. - Edit notebooks in any text editor (VS Code, Vim, Sublime) without needing Jupyter.
- Support multiple languages, including Python, R, and Julia.
Use Cases
- Version Control: Track notebook changes in a human-readable format using Git.
- Collaboration: Edit notebooks with text editors like VS Code without requiring Jupyter.
- Documentation: Store research notebooks as Markdown for better readability and accessibility.
- Interoperability: Work with notebooks in different programming languages using a single tool.
Conclusion
Jupytext makes Jupyter Notebooks more flexible, Git-friendly, and easier to manage. By converting .ipynb
files into editable formats, it enhances collaboration.
Whether you're working with large projects or simple research notes, Jupytext can significantly improve your workflow.