How to Use the Command Jupytext (with Examples)
Jupytext is a versatile command-line tool that allows developers and data scientists to seamlessly convert Jupyter notebooks into various text-based formats and vice versa. This functionality facilitates version control, sharing, and collaboration by allowing notebooks to be represented as human-readable and editable text files. Jupytext also supports synchronization between notebook formats, ensuring consistency and efficiency in the workflow.
Use case 1: Turn a notebook into a paired .ipynb/.py notebook
Code:
jupytext --set-formats ipynb,py notebook.ipynb
Motivation:
This use case is particularly useful for developers who wish to maintain both Jupyter notebook (.ipynb) and script (.py) versions of their code. Paired notebooks allow users to edit code in a text editor while retaining the computational and visual benefits of Jupyter notebooks. This dual-format setup ensures that the notebook can be used interactively or as a script in production environments.
Explanation:
jupytext: The command-line tool used for converting and synchronizing Jupyter notebooks.--set-formats ipynb,py: This option specifies that the input file should be paired with the mentioned formats, meaning changes in one representation will be reflected in the other.notebook.ipynb: The Jupyter notebook that is to be paired with a Python script format.
Example output:
After running the command, you will observe two files: notebook.ipynb (the original Jupyter notebook) and notebook.py (the newly created paired Python script). You can now edit the script and update the notebook, and vice versa, maintaining changes across both.
Use case 2: Convert a notebook to a .py file
Code:
jupytext --to py notebook.ipynb
Motivation:
Converting a notebook into a .py file is essential for version control, as text files can be easily tracked using systems like Git. Python scripts are also easier to integrate into automated pipelines and are well-suited for environments where Jupyter notebooks are not supported.
Explanation:
jupytext: The command-line tool for conversion between notebook formats.--to py: This flag indicates the conversion of the Jupyter notebook into a Python script.notebook.ipynb: The original notebook which is to be converted.
Example output:
Executing this command will generate a notebook.py file containing the code and markdown cells from the notebook, formatted as per Python syntax. Cells are represented as commented sections that retain their context.
Use case 3: Convert a .py file to a notebook with no outputs
Code:
jupytext --to notebook notebook.py
Motivation:
This conversion is useful when you have a script and wish to utilize the interactive features of Jupyter notebooks, such as inline visualization, without the need for existing output. Starting with a script allows for quick prototyping and testing in the notebook environment.
Explanation:
jupytext: This is the command-line tool used for format conversion.--to notebook: Indicates that the conversion should create a Jupyter notebook from the Python script.notebook.py: Specifies the Python script to be transformed into a Jupyter notebook.
Example output:
The command will produce a notebook.ipynb file. This new notebook will contain all the code from the notebook.py script, ready for execution, although initially, it won’t include any cell outputs.
Use case 4: Convert a .md file to a notebook and run it
Code:
jupytext --to notebook --execute notebook.md
Motivation:
Markdown files often contain both narrative text and code snippets. Converting a .md file into a Jupyter notebook and executing it allows users to benefit from the markdown’s readability while leveraging the computational power of Jupyter, which can execute code blocks and visualize outputs.
Explanation:
jupytext: The tool for notebook conversions.--to notebook: Directs the command to convert markdown content into a Jupyter notebook.--execute: Instructs the tool to execute the notebook after conversion, running all code cells present in the markdown file.notebook.md: The original markdown file containing code snippets and narratives.
Example output:
Executing the command results in a notebook.ipynb file that is not only converted from the markdown but also populated with outputs generated from executing the code blocks.
Use case 5: Update the input cells in a notebook and preserve outputs and metadata
Code:
jupytext --update --to notebook notebook.py
Motivation:
For scenarios where the code has been updated in the script file, but you want to retain the outputs and metadata in an existing notebook, this functionality ensures that changes to the code don’t erase or overwrite valuable information derived from previous runs.
Explanation:
jupytext: The tool used for managing notebook formats.--update: This ensures that only input cells are updated, while existing cell outputs and metadata in the notebook are preserved.--to notebook: Specifies the target conversion into a Jupyter notebook format.notebook.py: The script file containing updated code inputs.
Example output:
The resulting notebook.ipynb maintains its original outputs and metadata, but with updated code cells reflecting the latest changes from notebook.py.
Use case 6: Update all paired representations of a notebook
Code:
jupytext --sync notebook.ipynb
Motivation:
Keeping all paired representations of notebooks—whether .ipynb, .py, or any other format—consistent is essential, especially in collaborative projects or where automated processes rely on different formats. Synchronization ensures that any changes made in one format are reflected across the board.
Explanation:
jupytext: The command-line interface for notebook management.--sync: This command synchronizes all existing paired formats of the provided notebook, updating their content to match.notebook.ipynb: The notebook being synchronized with all of its paired formats.
Example output:
By running this command, you ensure that all paired files reach a consistent state, meaning the contents of the .ipynb, .py, and any other defined formats remain aligned and updated.
Conclusion:
Jupytext is a powerful tool for managing and converting Jupyter notebooks between multiple text-based formats. The versatility it offers makes it indispensable for developers and data scientists who prioritize readability, version control, and collaborative environments. By leveraging these use cases, users can optimize their workflow while ensuring that their notebooks are always in the desired format and up-to-date.
