For centering and scaling a variable (e.g. prior to a regression, or to a visualization), the standard procedure, of course, is to subtract the mean then divide by the standard deviation.
But is it considered preferable to use the population standard deviation (i.e. divided by n) or the sample standard deviation (divided by n-1)? Does it depend on one's use?
Interestingly, the standard R and Python functions seem to make different choices here. Python's sklearn.preprocessing.scale() uses population standard deviation; R's scale() uses sample standard deviation.
(NOTE: there's a prior question here, but it pertains to a very specific psychological method, and the one answer isn't actually substantiated by anything.)