Never Have an Unmaintainable Jupyter Notebook Again!

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Data visualisation is a fundamental part of Data Science. The talk will start with a practical demonstration (using pandas, scikit-learn, and matplotlib) of how relying on summary statistics and predictions alone can leave you blind to the true nature of your datasets. I will make the point that visualisations are crucial in every step of the Data Science process and therefore that Jupyter Notebooks definitely do belong in Data Science. We will then look at how maintainability is a real challenge for Jupyter Notebooks, especially when trying to keep them under version control with git. Although there exists a plethora of code quality tools for Python scripts (flake8, black, mypy, etc.), most of them don't work on Jupyter Notebooks. To this end I will present nbQA, which allows any standard Python code quality tool to be run on a Jupyter Notebook. Finally, I will demonstrate how to use it within a workflow which lets practitioners keep the interactivity of their Jupyter Notebooks without having to sacrifice their maintainability.

Marco Gorelli
Marco Gorelli
26 min
02 Jul, 2021

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Video Summary and Transcription

Jupyter Notebooks are important for data science, but maintaining them can be challenging. Visualizing data sets and using code quality tools like NBQA can help address these challenges. Tools like nbdime and Precommit can assist with version control and future code quality. Configuring NBQA and other code quality tools can be done in the PyProject.toml file. NBQA has been integrated into various projects' continuous integration workflows. Moving code from notebooks to Python packages should be considered based on the need for reproducibility and self-contained solutions.

1. Introduction to Jupyter Notebooks

Short description:

We will discuss the importance of Jupyter Notebooks and the challenges of maintaining them. Then, I will demonstrate a workflow for keeping your Jupyter Notebooks maintainable.

Hello, friends. We are here today to talk about Jupyter Notebooks and how to keep them maintainable. We will start with a motivating example, in which I'll make the case for why you might care about using Jupyter Notebooks in the first place. Then, I'll address a couple of challenges which people often bring up when trying to keep their Jupyter Notebooks maintainable.

The first one has to do with version control, and anyone who's tried to look at the difference between two notebooks using git diff will know what I'm talking about. It's not easy. The second has to do with continuous integration and, more specifically, the lack of code-quality tools which are available to run on Jupyter Notebooks.

So, then, finally, I will demonstrate a workflow for keeping your Jupyter Notebooks maintainable. Let's dive straight in with our motivating example. I've prepared a pretty standard data science workflow here, absolutely standard. We'll go through it in a second. Now, you might be wondering why I'm showing you an absolutely standard data science workflow, and bear with me, there might be a twist at the end, might. So let's go through it.

2. Analyzing Summary Statistics

Short description:

We start by reading in four CSV files using Pandas read CSV. We print out summary statistics for all four data sets, which show that they are pretty similar.

We start by reading in four CSV files using Pandas read CSV, pretty standard. Each of these has two columns, x and y, pretty standard. So then we'll print out some summary statistics, so we'll print out the mean of x, the mean of y, the standard deviation of x, the standard deviation of y, and the correlation between x and y. We will do this for all four data sets, still pretty standard.

And then, using Scikit-learn, for each of these data sets we will fit a linear regression model, also pretty standard, and we will print out the mean squared error, also absolutely standard.

So where's the twist? Well, let's see what happens if we run this using Python. Right, look at that. If we look at what's been printed on the console, we'll see that the mean of x is the same for all four data sets, but so is the mean of y, the standard deviation of x, the standard deviation of y, the correlation between x and y, and the mean squared error from having fit a linear regression model is also almost identical. So if we look at this, we can tell that the four data sets must be pretty similar. That's what these summary statistics are telling us.

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