You also may want to work on notebooks in a terminal (maybe over an SSH connection) where you don’t have a web browser handy.Ī third reason is to be able to work more effectively with notebooks and the notebook content, specifically the Python source code. Maybe you write and test bits of code in an IPython session and prefer that to a notebook where code cells can easily get run out of order. Or perhaps you use a text editor like Vim or Emacs and prefer the full power of your favorite editor. Maybe you are most comfortable writing code in an IDE like P圜harm or Visual Studio Code. All of these may change and pollute your diff and make versioning a challenge.Ī second reason to consider Jupytext is if you prefer to work outside the standard Jupyter notebook authoring environments. ipynb file) contains code, output, and metadata. Using a specialized diff tool like nbdime will make the diffs easier to navigate, but in the end, the single notebook file (i.e. My article on version control describes the situation and gives some background and good solutions for this issue, but they may not be perfect for every situation. First, you may be struggling with properly doing version control in your notebooks. There are several good reasons to consider using Jupytext. It also allows for changes in these other documents to be synced back to the notebook file (the. ![]() Jupytext is a Jupyter plugin that automatically saves Jupyter notebooks in a variety of human readable (and editable) outputs. Because of this, it can produce messy diffs in your version control system. But under the hood, a Jupyter notebook is just a JSON document, and the content of that document is often not very human readable. Many IDEs support Jupyter notebooks natively, and the Jupyter notebook server and JupyterLab environments are effective ways to write notebooks. ![]() Jupyter notebooks are a great way to interactively write Python code and include documentation, program output, and data visualization inline with the code that produced it.
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