On Thu, Apr 18, 2019 at 10:52 AM Stuart Reynolds
wrote:
> Is float8 a thing?
>
no, but np.float16 is -- so at least only twice as much memory as youo need
:-)
array([ nan, inf, -inf], dtype=float16)
I think masked arrays are going to be just as much, as they need to carry
the mask.
-CHB
>
Looks like a good fit. Thanks.
On Thu, Apr 18, 2019 at 11:17 AM Eric Wieser
wrote:
> One option here would be to use masked arrays:
>
> arr = np.ma.zeros(3, dtype=bool)
> arr[0] = True
> arr[1] = False
> arr[2] = np.ma.masked
>
> giving
>
> masked_array(data=[True, False, --],
> mas
One option here would be to use masked arrays:
arr = np.ma.zeros(3, dtype=bool)
arr[0] = True
arr[1] = False
arr[2] = np.ma.masked
giving
masked_array(data=[True, False, --],
mask=[False, False, True],
fill_value=True)
On Thu, 18 Apr 2019 at 10:51, Stuart Reynolds wrote:
>
Thanks. I’m aware of bool arrays.
I think the tricky part of what I’m looking for is NULLability and
interoperability with code the deals with billable data (float arrays).
Currently the options seem to be float arrays, or custom operations that
carry (unabstracted) categorical array data represen
Hi Stuart,
On Thu, 18 Apr 2019 09:12:31 -0700, Stuart Reynolds wrote:
> Is there an efficient way to represent bool arrays with null entries?
You can use the bool dtype:
In [5]: x = np.array([True, False, True])
Is there an efficient way to represent bool arrays with null entries?
Tools like pandas push you very hard into 64 bit float representations -
64 bits where 3 will suffice is neither efficient for memory, nor
(consequently), speed.
What I’m hoping for is that there’s a structure that is ‘viewed’
