Having manually sifted through hundreds of randomly sampled notebooks, I feel I can confidently speak on the distribution of characteristics in them, at least up through a couple years ago.
1. Notebooks on GitHub are not necessarily an entirely representative sample of notebooks at large. If the author is putting it on GitHub, there's already a significant selection bias towards certain topics, despite notebooks being used in practically any discipline you can think of at least to some extent.
2. Notebooks in repositories that contain requirements.txt files are a minority and tend to be intended/cleaned up for sharing (itself not the norm).
3. What's more common is just a handful of !pip install at the top.
4. Even more common is just some details on dependencies in an adjoining README.
A very non-trivial chunk of notebooks on GitHub are just copies of the "Hands On ML 3" textbook/exercise set. If my memory serves there are tens of thousands of copies of that one repository. The fork count shown by GitHub doesn't account for the plethora of copies that weren't made using it.