I know that you already got an answer to this effect, but to provide some more information, most data science workflows in Python rely heavily on calls into numerical libraries (numpy, scipy, pandas, tensorflow, pytorch, matplotlib) that are Python wrappers over compiled binaries (mostly C and Fortran, a not-inconsiderable amount of handwritten Assembly), that have been constructed so that the wrapper safely yields the GIL before invoking the underlying binary. This is all the more important when considering libraries like tensorflow or pytorch that may involve complex long-running interaction with training resources across a network. Control is yielded to allow the interpreter to continue carrying out tasks like displaying the ongoing training progress, or loading training data.

To that point, most third-party libraries used also delegate the number crunching to C/C++ code, so most time is still spent outside the GIL.