People love to hate @ProjectJupyter notebooks!

They're missing out on a great communication tool.

Here's how data scientists and machine learners can
build great Jupyter Notebooks that people love to read

Fix your dang code

You can make a mess in notebooks when experimenting.

All Imports into Cell 1
Extract some functions and classes
Comments.
Fix your variable names
Fix the order and outputs
Use black to standardize your code format https://github.com/drillan/jupyter-black

Jupyter has Markdown Cells!

Use them.
If you're not using Markdown in your Notebooks, you should not be using Notebooks.

Write some prose to explain your code.
Whether analysis or model,
do yourself a favour and document it.

Here's a markdown guide: https://www.markdownguide.org/getting-started/

Final checks

We all do it.

We forget to run a cell and wonder for an embarrassing amount of time, why we have an error in the next one.

Before you share your notebook:
Restart Kernel to forget all variables
Run All Cells

Not by hand. Automatic.
It'll show any mix-ups

If this thread was informative
make sure to follow @jesperdramsch

and share the first tweet for others to learn!

I am writing 30 threads in 30 days about

machine learning
data science
Python coding

Find them here: https://twitter.com/JesperDramsch/status/1479863422967767040

Extra Credit

Use nbconvert to run all cells from the command line

Write a utils file with reusable functions

Learn keyboard shortcuts for efficiency

Make it interactive with ipykernel

TL;DR

Care about code quality
Use markdown cells (More than just headings)
Make sure the notebook runs
Use extra tools to make your life easier