Jupyter notebooks are hard to diff and merge since they contain both code and output, but tools and practices can make version control easier.

The scrapbook library allows you to save state inside the notebook file itself, making it easier to develop workflows using Jupyter notebooks.

Have you ever created a Jupyter notebook and wished you could generate the notebook with a different set of parameters? If so, you've probably done at least one of the following: Edited the variables in a cell and reran the notebook, saving off a copy as neededSaved a copy of the notebook and maybe hacked … Continue reading Parameterizing and automating Jupyter notebooks with papermill

Jupyter notebooks are a popular way to share data and code, and there are multiple ways to run and edit notebooks.

Jupyter widgets can make notebooks be more interactive and make data exploration much easier, especially for end users who are not coders.

Jupyter notebooks can easily have hidden state. Use these methods to see all the variables that exist in your Jupyter notebooks.

Using the %autoreload magic in IPython or Jupyter can help you continue working without restarting your session after making local changes.