On Thu, Apr 18, 2019 at 10:52 AM Stuart Reynolds <stu...@stuartreynolds.net>
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
>
> On Thu, Apr 18, 2019 at 9:46 AM Stefan van der Walt <stef...@berkeley.edu>
> wrote:
>
>> 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])
>>
>>
>>
>> In [6]: x
>>
>>
>> Out[6]: array([ True, False, True])
>>
>> In [7]: x.dtype
>>
>>
>> Out[7]: dtype('bool')
>>
>> You should note that this stores one True/False value per byte, so it is
>> not optimal in terms of memory use. There is no easy way to do
>> bit-arrays with NumPy, because we use strides to determine how to move
>> from one memory location to the next.
>>
>> See also:
>> https://www.reddit.com/r/Python/comments/5oatp5/one_bit_data_type_in_numpy/
>>
>> > What I’m hoping for is that there’s a structure that is ‘viewed’ as
>> > nan-able float data, but backed but a more efficient structures
>> > internally.
>>
>> There are good implementations of this idea, such as:
>>
>> https://github.com/ilanschnell/bitarray
>>
>> Those structures cannot typically utilize the NumPy machinery, though.
>> With the new array function interface, you should at least be able to
>> build something that has something close to the NumPy API.
>>
>> Best regards,
>> Stéfan
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