![]() In the example, we make a simple notebook with the following code: import matplotlib.pyplot as plt If you want to follow along, you can either manually create a notebook and git repository and mimic the code and examples below, or you can clone my git repository – specifically editing the test notebook, and work with that. In this example I will demonstrate how the three parts of a Jupyter notebook can change values during the develpment process, and how the files interact with the default git tools. The best way to understand the issues is to look at simple example. A simple diff example tells us how messy it is But there are ways to make life easier for developers and data scientists who use Jupyter notebooks. Since Jupyter notebooks are just JSON documents that contain code, metadata, and output, comparing notebook versions can be clunky when using standard version control tools like git. Most of us already know about version control, but how to use it effectively with Jupyter notebooks is not obvious. Version control allows for checkpointing artifacts, comparing different versions, and branching into different paths of development. Version control, also known as source code control, is used to track changes in code and other artifacts in software development and data science work.
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