It might be interesting to note that Numpy provides various set routines operating on their arrays (and hence lists as well, by conversion): https://docs.scipy.org/doc/numpy/reference/routines.set.html
For [intersection](https://docs.scipy.org/doc/numpy/reference/generated/numpy.intersect1d.html) for example they do the following: 1. Concatenate the arrays, 2. Sort the result, 3. Compare subsequent elements for equality. Most likely because for each of the steps, there is a C extension that provides an efficient implementation. For [membership testing](https://github.com/numpy/numpy/blob/d9b1e32cb8ef90d6b4a47853241db2a28146a57d/numpy/lib/arraysetops.py#L560), i.e. check which elements of `a` are in `b`, however, they use a condition where they decide that sometimes it's faster to use a loop over `a` and `b` instead of the concat+sort approach: if len(b) < 10 * len(a) ** 0.145 Not sure where they got the exact numbers from (maybe from benchmarks?). _______________________________________________ Python-ideas mailing list -- python-ideas@python.org To unsubscribe send an email to python-ideas-le...@python.org https://mail.python.org/mailman3/lists/python-ideas.python.org/ Message archived at https://mail.python.org/archives/list/python-ideas@python.org/message/VFBIHAQBZNWO45KQAPUZ52YERO5ODBHP/ Code of Conduct: http://python.org/psf/codeofconduct/
