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 representations, such as:
0: false
1: true
2: NULL
... which wouldn’t be compatible with algorithms that use, say, np.isnan.
Ideally, it would be nice to have a structure that was float-like in that
it’s compatible with nan-aware operations, but it’s storage is just a
single byte per cell (or less).
Is float8 a thing?
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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