Yeah, jupyter notebooks don't guarantee any specifics about versions of code used for that output. In the real world you can expect everyone in the lab including all of the students to be editing jupyter notebooks at whim. The only way to do this would be to have proper version control and of your code, a snapshot of the environment, and to log all this along with the run that generated the output. This is possible with regular python using git, proper log files, etc. Jupyter notebooks seem like an extra roadblock.
Ooh. That's a nice utility funtion that I will write soon. We tend to look at requirements as something we hope the package manager gets right, and then we ignore at runtime, but there are a bunch of errors we could avoid if we verified at runtime. Sometimes when writing a library you have to have different code paths for different versions.
Something like
`if check_versions(pandas__gt="2.0.0", pandas__lt="3.0.0"):`
