On Tue, May 6, 2008 at 9:53 AM, Keith Goodman <[EMAIL PROTECTED]> wrote:
> On Tue, May 6, 2008 at 9:45 AM, Anne Archibald
> <[EMAIL PROTECTED]> wrote:
> > In fact, if you want to use empty() down the road, it may
> > make sense to initialize your array to zeros()/0., so that if you ever
> > use the values, the NaNs will propagate and become obvious.
>
> Numpy has ones and zeros. Could we add a nans?
>
> I often initialize using x = nan * ones((n ,m)). But if it's in a
> loop, I'll avoid one copy by doing
>
> x = np.ones((n, m))
> x *= np.nan
>
> To many on the list using nans for missing values is like chewing gum
> you found on the sidewalk. But I use it all the time so I'd use a
> nans.
Why don't you just roll your own?
>>> def nans(shape, dtype=float):
... a = np.empty(shape, dtype)
... a.fill(np.nan)
... return a
...
>>> nans([3,4])
array([[ NaN, NaN, NaN, NaN],
[ NaN, NaN, NaN, NaN],
[ NaN, NaN, NaN, NaN]])
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