Jupyter notebooks as Markdown documents, Julia, Python or R scripts.

You've always wanted to

  • edit Jupyter notebooks as e.g. plain Python scripts in your favorite editor?
  • do version control of Jupyter notebooks with clear and meaningful diffs?
  • collaborate on Jupyter notebooks using standard (text oriented) merge tools?

Jupytext can convert notebooks to and from

  • Julia, Python and R scripts (extensions .jl, .py and .R),
  • Markdown documents (extension .md),
  • R Markdown documents (extension .Rmd).

Jupytext is available from within Jupyter. You can work as usual on your notebook in Jupyter, and save and read it in the formats you choose. The text representations can be edited outside of Jupyter (see our demo below). When the notebook is refreshed in Jupyter, cell inputs are loaded from the script or Markdown document and kernel variables are preserved. Cell outputs are taken from the original Jupyter notebook if you use paired notebooks, which we recommend.

Format Extension Text editor Git friendly Preserve outputs
Jupyter notebook .ipynb
Script or Markdown .jl/.py/.R/.md/.Rmd
Paired notebook (.jl/.py/.R/.md/.Rmd) + .ipynb

Example usage

Writing notebooks as plain text

You like to work with scripts? The good news is that plain scripts, which you can draft and test in your favorite IDE, open naturally as notebooks in Jupyter when using Jupytext. Run the notebook in Jupyter to generate the outputs, associate an .ipynb representation, save and share your research!

Code refactoring

In the animation below we propose a quick demo of Jupytext. While the example remains simple, it shows how your favorite text editor or IDE can be used to edit your Jupyter notebooks. IDEs are more convenient than Jupyter for navigating through code, editing and executing cells or fractions of cells, and debugging.

  • We start with a Jupyter notebook.
  • The notebook includes a plot of the world population. The plot legend is not in order of decreasing population, we'll fix this.
  • We want the notebook to be saved as both a .ipynb and a .py file: we add a jupytext_formats entry to the notebook metadata.
  • The Python script can be opened with PyCharm:
    • Navigating in the code and documentation is easier than in Jupyter.
    • The console is convenient for quick tests. We don't need to create cells for this.
    • We find out that the columns of the data frame were not in the correct order. We update the corresponding cell, and get the correct plot.
  • The Jupyter notebook is refreshed in the browser. Modified inputs are loaded from the Python script. Outputs and variables are preserved. We finally rerun the code and get the correct plot.


Install Jupytext with

pip install jupytext --upgrade

Then, configure Jupyter to use Jupytext:

  • generate a Jupyter config, if you don't have one yet, with jupyter notebook --generate-config
  • edit .jupyter/jupyter_notebook_config.py and append the following:
c.NotebookApp.contents_manager_class = "jupytext.TextFileContentsManager"
  • and restart jupyter, i.e. run
jupyter notebook

Paired notebooks

The idea of paired notebooks is to store a .ipynb file alongside the text-only version. This lets us get the best of both worlds: a text-only document to put under version control, and an easily sharable notebook which stores the outputs.

To enable paired notebooks, add a jupytext_formats entry to the notebook metadata with Edit/Edit Notebook Metadata in Jupyter's menu:

  "jupytext_formats": "ipynb,py",
  "kernelspec": {
    "name": "python3",
  "language_info": {

When you save the notebook, both the Jupyter notebook and the python scripts are updated. You can edit the text version
and then get the updated version in Jupyter by refreshing your browser (deactivate Jupyter's autosave by running %autosave 0 in a cell).

Accepted formats are: ipynb, md, Rmd, py and R. In case you want multiple text extensions, please note that the
order matters: the first non-ipynb extension
is the one used as the reference source for notebook inputs when you open the ipynb file.

Finally, it is also possible to pair every notebook with a text representation. If you add

c.NotebookApp.contents_manager_class = "jupytext.TextFileContentsManager"
c.ContentsManager.default_jupytext_formats = "ipynb,py" # or "ipynb,nb.py" # or "ipynb,md" # or "ipynb,Rmd"

to your Jupyter configuration file, then every Jupyter notebook that you save will have a companion .py (.nb.py, .md, or .Rmd) notebook. And every .py (.nb.py, .md, or .Rmd) notebook will have a companion .ipynb notebook.

Command line conversion

The package provides a jupytext script for command line conversion between the various notebook extensions:

jupytext notebook.ipynb --to md --test          # Test round trip conversion
jupytext notebook.ipynb --to md --output -      # display the markdown version on screen

jupytext notebook.ipynb --to markdown           # create a notebook.md file
jupytext notebook.ipynb --to python             # create a notebook.py file
jupytext notebook.ipynb --output script.py      # create a notebook.py file

jupytext notebook.md --to notebook              # overwrite notebook.ipynb (remove outputs)
jupytext notebook.md --to notebook --update     # update notebook.ipynb (preserve outputs)

jupytext notebook1.md notebook2.py --to ipynb   # overwrite notebook1.ipynb notebook2.ipynb

Round-trip conversion

Round-trip conversion is safe! A few hundred tests help guarantee this.

  • Script to Jupyter notebook, to script again is identity. If you
    associate a Jupyter kernel with your notebook, that information will go to
    a yaml header at the top of your script.
  • Markdown to Jupyter notebook, to Markdown again is identity.
  • Jupyter to script, then back to Jupyter again preserves source and metadata.
  • Jupyter to Markdown, and Jupyter again preserves source and metadata (cell metadata available only for R Markdown). Note that Markdown cells with two consecutive blank lines will be split into multiple cells (as the two blank line pattern is used to separate cells).

Format specifications

Markdown and R markdown

Our implementation for Jupyter notebooks as Markdown or R Markdown documents is straightforward:

  • A YAML header contains the notebook metadata (Jupyter kernel, etc)
  • Markdown cells are inserted verbatim, and separated with two blank lines
  • Code and raw cells start with triple backticks collated with cell language, and end with triple backticks. Cell metadata are available in the R Markdown format.

R scripts

Implement these specifications:

  • Jupyter metadata in YAML format, in a #' -escaped header
  • Markdown cells are commented with #'
  • Code cells are exported verbatim. Cell metadata are signalled with #+. Cells end with a blank line, an explicit start of cell marker, or a Markdown comment.

Python and Julia scripts

We wanted to represent Jupyter notebooks with the least explicit markers possible. The rationale for that is to allow arbitrary python files to open as Jupyter notebooks, even files which were never prepared to become a notebook. Precisely:

  • Jupyter metadata go to an escaped YAML header
  • Markdown cells are commented with # , and separated with a blank line
  • Code cells are exported verbatim (except for Jupyter magics, which are escaped), and separated with blank lines. Code cells are reconstructed from consistent python paragraphs (no function, class or multiline comment will be broken). A start-of-cell delimiter # + is used for cells that contain blank lines (outside of functions, classes, etc). # + {} is used for cells that have explicit metadata (inside the curly bracket, in JSON format). The end of cell delimiter is # -, and is omitted when followed by another explicit start of cell marker.

Will my notebook really run in an IDE?

Well, that's what we expect. There's however a big difference in the python environments between Python IDEs and Jupyter: in the IDE code is executed with python and not in a Jupyter kernel. For this reason, jupytext escapes Jupyter magics found in your notebook. Comment a magic with #noescape on the same line to avoid escaping. User defined magics can be escaped with #escape (magics are not escaped in the plain Markdown representation).

Also, you may want some cells to be active only in the Python, or R Markdown representation. For this, use the active cell metadata. Set "active": "ipynb" if you want that cell to be active only in the Jupyter notebook. And "active": "py" if you want it to be active only in the Python script. And "active": "ipynb,py" if you want it to be active in both, but not in the R Markdown representation...