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’ as nan-able float data, but backed but a more efficient structures internally. Thanks - Stu
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/
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
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 <stefanv@berkeley.edu> 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, --], mask=[False, False, True], fill_value=True) On Thu, 18 Apr 2019 at 10:51, Stuart Reynolds <stuart@stuartreynolds.net> wrote:
On Thu, Apr 18, 2019 at 10:52 AM Stuart Reynolds <stuart@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
-- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker@noaa.gov
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/
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
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 <stefanv@berkeley.edu> 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, --], mask=[False, False, True], fill_value=True) On Thu, 18 Apr 2019 at 10:51, Stuart Reynolds <stuart@stuartreynolds.net> wrote:
On Thu, Apr 18, 2019 at 10:52 AM Stuart Reynolds <stuart@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
-- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker@noaa.gov
participants (4)
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Chris Barker -
Eric Wieser -
Stefan van der Walt -
Stuart Reynolds