Hi all, So one of the things exposed in the numpy namespace are objects called np.int np.float np.bool etc. These are commonly used -- in fact, just yesterday on another project I saw a senior person reviewing a pull request instruct a more junior person that they should use np.float instead of float or np.float64. But AFAICT everyone who is actually using them is doing this based on a very easy-to-fall-for misconception, i.e., that these objects have something to do with numpy. In fact they are just aliases for the regular builtin Python types: 'int', 'float', 'bool', etc. NumPy *does have* special numpy-specific types -- but these are called np.int_, np.float_, np.bool_, etc. Apparently they were set up this way back in numpy 0.something, as a backwards compatibility (!) hack: https://github.com/numpy/numpy/pull/6103#issuecomment-123801937 Now, 10+ years later, they continue to confuse people on a regular, ongoing basis, and new users are still being taught misleading "facts" about them. I suggest that we should deprecate them, with no fixed schedule for actually removing them. (I have no idea if/when people will actually stop using them to the point that we can get away with removing them entirely, but in the mean time we should at least be publicizing that any code which is using them is almost certainly based on a misunderstanding.) The technical challenge here is that historically it has simply been impossible to deprecate a global constant like this without using version-specific hacks or accepting unacceptable slowdowns on every attribute access. But, python 3.5 finally adds the necessary machinery to do this in a future-proof way, so now it can be done safely across all versions of Python that we care about, including future unreleased versions: https://github.com/njsmith/metamodule/ Hence: https://github.com/numpy/numpy/pull/6103 Thoughts? -n P.S.: using metamodule.py also gives us the option of making np.testing lazily imported, which last time this came up was benchmarked to improve numpy's import speed by ~35% [1] -- not too bad given that most production code will never touch np.testing. But this is just a teaser postscript; I'm not proposing that we actually do this at this time :-). [1] http://mail.scipy.org/pipermail/numpy-discussion/2012-July/063147.html -- Nathaniel J. Smith -- http://vorpus.org