[Numpy-discussion] numpy.filled, again

Eric Firing efiring@hawaii....
Thu Jun 13 15:47:56 CDT 2013


On 2013/06/13 10:36 AM, Benjamin Root wrote:
>
> On Thu, Jun 13, 2013 at 9:36 AM, Aldcroft, Thomas
> <aldcroft@head.cfa.harvard.edu <mailto:aldcroft@head.cfa.harvard.edu>>
> wrote:
>
>
>
>
>     On Wed, Jun 12, 2013 at 2:55 PM, Eric Firing <efiring@hawaii.edu
>     <mailto:efiring@hawaii.edu>> wrote:
>
>         On 2013/06/12 8:13 AM, Warren Weckesser wrote:
>          > That's why I suggested 'filledwith' (add the underscore if
>         you like).
>          > This also allows a corresponding masked implementation,
>         'ma.filledwith',
>          > without clobbering the existing 'ma.filled'.
>
>         Consensus on np.filled? absolutely not, you do not have a consensus.
>
>         np.filledwith or filled_with: fine with me, maybe even with
>         everyone--let's see.  I would prefer the underscore version.
>
>
>     +1 on np.filled_with.  It's unique the meaning is extremely obvious.
>       We do use np.ma.filled in astropy so a big -1 on deprecating that
>     (which would then require doing numpy version checks to get the
>     right method).  Even when there is an NA dtype the numpy.ma
>     <http://numpy.ma> users won't go away anytime soon.
>
>
> I like np.filled_with(), but just to be devil's advocate, think of the
> syntax:
>
> np.filled_with((10, 24), np.nan)
>
> As I read that, I am filling the array with (10, 24), not NaNs.  Minor
> issue, for sure, but just thought I raise that.
>
> -1 on deprecation of np.ma.filled().  -1 on np.filled() due to collision
> with np.ma <http://np.ma> (both conceptually and programatically).
>
> np.values() might be a decent alternative.
>
> Cheers!
> Ben Root

Even if he is representing the devil, Ben raises a good point.  To 
summarize, the most recent set of suggestions that seem not to have been 
completely shot down include:

np.filled_with((10, 24), np.nan)
np.full((10, 24), np.nan)          # analogous to np.empty
np.values((10, 24), np.nan)        # seems clear, concise
np.initialized((10, 24), np.nan)   # a few more characters, but
                                    #  seems clear to me.

Personally, I like all of the last three better than the first.

Eric




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