On Tue, May 6, 2008 at 10:03 AM, Timothy Hochberg <[EMAIL PROTECTED]> wrote:
> 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]])
I learn a lot from this list. I didn't know about fill. Looks like it
is much faster than adding nan.
>> timeit nans0((500,500))
10 loops, best of 3: 30.5 ms per loop
>> timeit nans1((500,500))
1000 loops, best of 3: 956 µs per loop
def nans0(shape, dtype=float):
a = np.ones(shape, dtype)
a += np.nan
return a
def nans1(shape, dtype=float):
a = np.empty(shape, dtype)
a.fill(np.nan)
No need to roll my own. I'll smoke yours.
return a
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