Skip to main content

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

Project description

CI Documentation Status codecov.io Language grade: Python Code style: black GitHub language count Conda Version Pypi pyversions Binder:notebook Binder:lab launch - renku

Have you always wished Jupyter notebooks were plain text documents? Wished you could edit them in your favorite IDE? And get clear and meaningful diffs when doing version control? Then... Jupytext may well be the tool you're looking for!

Jupytext is a plugin for Jupyter that can save Jupyter notebooks as either

Use cases

Common use cases for Jupytext are:

  • Doing version control on Jupyter Notebooks
  • Editing, merging or refactoring notebooks in your favorite text editor
  • Applying Q&A checks on notebooks.

Install

You can install Jupytext with

  • pip install jupytext
  • or conda install jupytext -c conda-forge.

Please note that Jupytext includes an extension for Jupyter Lab. In the latest version of Jupytext, this extension is compatible with Jupyter Lab >= 3.0 only. If you use Jupyter Lab 2.x, please either stay with Jupytext 1.8.2, or install, on top of the latest pip or conda version of Jupytext, a version of the extension that is compatible with Jupyter Lab 2.x:

jupyter labextension install jupyterlab-jupytext@1.2.2  # For Jupyter Lab 2.x

Then, restart your Jupyter server (for more installation details, see the install section in the documentation).

When Jupytext is installed, .py and .md files have a notebook icon. And you can really open and run these files as notebooks

    With a click on the text file in Jupyter Notebook

    (click on the image above to try this on Binder)

    With a click on the text file in JupyterLab (⭐New⭐) To do that, you will need to change the default viewer for text notebooks by copy-pasting the following settings (or the subset that matches your use case) in the `Document Manager` section:
    {
      "defaultViewers": {
        "markdown": "Jupytext Notebook",
        "myst": "Jupytext Notebook",
        "r-markdown": "Jupytext Notebook",
        "quarto": "Jupytext Notebook",
        "julia": "Jupytext Notebook",
        "python": "Jupytext Notebook",
        "r": "Jupytext Notebook"
      }
    }
    

    Here is a screencast of the steps to follow:

    (click on the image above to try this on Binder)

    Another possibility is to activate this with a default_setting_overrides.json file in the .jupyter/labconfig folder with e.g.

    wget https://raw.githubusercontent.com/mwouts/jupytext/main/binder/labconfig/default_setting_overrides.json -P  ~/.jupyter/labconfig/
    

    Note: to open links to .md files in notebooks with the Notebook editor, use jupyterlab>=4.0.0a16.

    With a right click and open with notebook in Jupyter Lab

    (click on the image above to try this on Binder)

Paired notebooks

The most convenient way to use Jupytext is probably through paired notebooks.

To pair a given .ipynb or text notebook to an additional notebook format, use either

    the "pair notebook with..." commands in Jupyter Lab

    the "pair notebook with..." menu entries in Jupyter Notebook

    jupytext at the command line

    with e.g.

    jupytext --set-formats ipynb,py:percent notebook.ipynb
    

    see the documentation.

    or a local or global jupytext.toml configuration file.

    with e.g. the following content:

    formats = "ipynb,py:percent"
    

    see the documentation.

When you save a paired notebook in Jupyter, both the .ipynb file and the text version are updated on disk.

When a paired notebook is opened or reloaded in Jupyter, the input cells are loaded from the text file, and combined with the output cells from the .ipynb file.

You can edit the text representation of the notebook in your favorite editor, and get the changes back in Jupyter by simply reloading the notebook (Ctrl+R in Jupyter Notebook, "reload notebook" in Jupyter Lab). And the changes are propagated to the .ipynb file when you save the notebook.

Alternatively, you can synchronise the two representations by running jupytext --sync notebook.ipynb at the command line.

Which text format?

Jupytext implements many text formats for Jupyter Notebooks. If your notebook is mostly made of code, you will probably prefer to save it as a script:

  • Use the percent format, a format with explicit cell delimiters (# %%), supported by many IDE (Spyder, Hydrogen, VS Code, PyCharm and PTVS)
  • Or use the light format, if you prefer to see fewer cell markers.

If your notebook contains more text than code, if you are writing a documentation or a book, you probably want to save your notebook as a Markdown document

More resources?

If you're new to Jupytext, you may want to start with the FAQ or with the Tutorials, or with this short introduction to Jupytext: .

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

jupytext-1.14.5.tar.gz (830.5 kB view details)

Uploaded Source

Built Distribution

jupytext-1.14.5-py3-none-any.whl (298.7 kB view details)

Uploaded Python 3

File details

Details for the file jupytext-1.14.5.tar.gz.

File metadata

  • Download URL: jupytext-1.14.5.tar.gz
  • Upload date:
  • Size: 830.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.16

File hashes

Hashes for jupytext-1.14.5.tar.gz
Algorithm Hash digest
SHA256 976e66be8056459a2067e0ec3ff68cc31e00c31895faf9eb893022d319e8f5b4
MD5 277c1312773b031c09bfa82cf4ba81ec
BLAKE2b-256 0a7fc495114a3814df1d9d41855e939a6a0cb133a1a11a34a22bde934d115ff4

See more details on using hashes here.

Provenance

File details

Details for the file jupytext-1.14.5-py3-none-any.whl.

File metadata

  • Download URL: jupytext-1.14.5-py3-none-any.whl
  • Upload date:
  • Size: 298.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.16

File hashes

Hashes for jupytext-1.14.5-py3-none-any.whl
Algorithm Hash digest
SHA256 a5dbe60d0ea158bbf82c2bce74aba8d0c220ad7edcda09e017c5eba229b34dc8
MD5 61a9c279c4c5e30d96a6a9e79e3b76ec
BLAKE2b-256 c8e61acb564671a00d7af3448536203bc01a6da7b39064a7a3e8a4de59d8bdfa

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page