On Wed, Jun 12, 2013 at 8:10 AM, Nathaniel Smith <njs@pobox.com> wrote:

Hi all,

It looks like we've gotten a bit confused and need to untangle something. There's a PR to add new functions 'np.filled' and 'np.filled_like': https://github.com/numpy/numpy/pull/2875 And there was a discussion about this on the list back in January: http://thread.gmane.org/gmane.comp.python.numeric.general/52763

I think a reasonable summary of the opinions in the thread are: - This functionality is great, ... - ...but we can't call it 'np.filled' because there's also 'np.ma.filled' which does something else... - ...but there really aren't any better names... - ...so we should overload np.empty, like: 'np.empty(shape, fill=value)'

In the mean time the original submitter has continued puttering along polishing the original patch, and it's ready to merge... except it's still the original interface, somehow the thread discussion and the PR discussion never met up.

So, we have to decide what to do.

Personally I think that overloading np.empty is horribly ugly, will continue confusing newbies and everyone else indefinitely, and I'm 100% convinced that we'll regret implementing such a warty interface for something that should be so idiomatic. (Unfortunately I got busy and didn't actually say this in the previous thread though.) So I think we should just merge the PR as is. The only downside is the np.ma inconsistency, but, np.ma is already inconsistent (cf. masked_array.fill versus masked_array.filled!), somewhat deprecated, and AFAICT there are far more people who will benefit from a clean np.filled idiom than who actually use np.ma (and in particular its fill-value functionality). So there would be two bad-but-IMHO-acceptable options: either live with an inconsistency between np.filled and np.ma.filled, or deprecate np.ma.filled in favor of masked_array.filled (which does exactly the same thing) and eventually switch np.ma.filled to be consistent with the new np.filled.

But, that's just my opinion.

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Why we need this animal whatever it is called Scientific Python @SciPyTip Create a 2x3 array filled with integer 7's: 7*np.ones((2,3), int) and I just did this yesterday, np.nan * np.ones(3) maybe I should have used np.zeros(3) / 0. Josef