On Sun, 6 Jan 2019 19:40:32 -0800 Stephan Hoyer <sho...@gmail.com> wrote: > On Sun, Jan 6, 2019 at 4:27 PM Steven D'Aprano <st...@pearwood.info> wrote: > > > I propose adding a "nan_policy" keyword-only parameter to the relevant > > statistics functions (mean, median, variance etc), and defining the > > following policies: > > > > IGNORE: quietly ignore all NANs > > FAIL: raise an exception if any NAN is seen in the data > > PASS: pass NANs through unchanged (the default) > > RETURN: return a NAN if any NAN is seen in the data > > WARN: ignore all NANs but raise a warning if one is seen > > > > I don't think PASS should be the default behavior, and I'm not sure it > would be productive to actually implement all of these options. > > For reference, NumPy and pandas (the two most popular packages for data > analytics in Python) support two of these modes: > - RETURN (numpy.mean() and skipna=False for pandas) > - IGNORE (numpy.nanmean() and skipna=True for pandas) > > RETURN is the default behavior for NumPy; IGNORE is the default for pandas.
I agree with Stephan that RETURN and IGNORE are the only useful modes of operation here. Regards Antoine. _______________________________________________ Python-ideas mailing list Python-ideas@python.org https://mail.python.org/mailman/listinfo/python-ideas Code of Conduct: http://python.org/psf/codeofconduct/
