Fortunately, people are already working on an open-source solution for reactive kernels in Jupyter. Reactivity is a neat feature already available in Pluto, the notebook system for Julia, and it’s also available in some Jupyter commercial distributions. If I’m running a reactive notebook and edit the cell a = 1, the notebook will automatically run the third and fourth cells ( total = a + b, and total) because they depend on the value of a, effectively updating and printing the new value of the total variable. For example, say I have a notebook like this: However, this does not solve the problem entirely, as we may still end up with hidden state during notebook development and find out until we push to the repository and the CI breaks.Ī popular approach are reactive notebooks, which automatically re-compute cells to prevent hidden state. We can partially solve this problem with testing: we can execute our notebooks linearly on each git push to ensure reproducible results.
Hidden makes it impossible to reproduce our results. This problem is so pervasive that there’s even a Nature blog post about it. But, the right notebook has hidden state: if we restart the kernel and run all cells in order, the final output won’t match (the recorded one is 42, but we’ll get 3). For example, the left notebook does not have hidden state, because if you restart the kernel and run all cells in order, you’ll get the same results (the number 3). Given a notebook, we say it has a hidden state when executing it linearly returns different outputs than the stored ones. So, what are the real problems? Hidden state Therefore, we highly encourage you to write pipelines instead of big monolithic notebooks. Modularized notebooks open many possibilities: they are easier to collaborate, test, and are computationally efficient (you can run independent notebooks in parallel!). Ploomber examples -n guides/first-pipeline -o example For example, you could write a test case with papermill like this: If we are thinking of testing notebooks as a whole, we can use papermill to execute them programmatically and evaluate their results.
Ipynb viewer github code#
ipynb file, effectively storing source code and outputs two different files. The main caveat is that output is lost once you close the file, but you can use Jupytext’s pairing function, which stores the results in a separate. When using Jupytext, you can use git diff and pull requests since your notebooks are simple scripts. This way, you can edit your code interactively, but it’ll store a. You can see that it clearly shows the difference, the current version on git has the graph with red borders, while the new one has green borders.Īnother option is to use nbdime, which allows you to do the same from the terminal (in fact, the JupyterLab extension uses nbdime under the hood).Ī third option (and my favorite!) is Jupytext. There is an official JupyerLab extension that integrates git right into the Jupyter interface, allowing you (among other things), to diff notebooks. This is how the diff view for a notebook looks like on GitHub:įortunately, we can easily fix this. ipynb files blow up git repository size, and secondly, git diff (and GitHub Pull Requests) won’t work. The caveat is that they are challenging to manage with git. Standalone notebooks are convenient: you can work on a notebook, save it, come back to it tomorrow, and the results will be there. ipynb files are JSON files with a pre-defined schema, and they keep the code and the output in the same file. In this blog post, I discuss the most common myths around notebooks and comment on the critical unsolved problems. Of course, there are valid reasons to avoid notebooks, but I wish the conversation centered on the real problems instead of discussing already solved issues over and over again. The use of Jupyter notebooks is one of the most divisive topics in the data community. Jupyter notebooks, you love them, or you hate them.