On Fri, Jun 24, 2011 at 1:04 PM, Matthew Brett
<matthew.brett@gmail.com> wrote:
Hi,
Just as a use case, if I do this:
a = np.zeros((big_number,), dtype=np.int32)
a[0,0] = np.NA
I think I'm right in saying that, with the array.mask implementation
my array memory usage with grow by new big_number bytes, whereas with
the np.naint32 implementation you'd get something like:
Error('This data type does not allow missing values')
Is that right?
Not really, I much prefer having the operation of adding a mask always be very explicit. It should raise an exception along the lines of "Cannot assign the NA missing value to an array with no validity mask".
-Mark
See y'all,
Matthew