How to find the minimal numpy dtype to store a maximum integer value?

Question

I need to create a very large numpy array that will hold non-negative integer values. I know in advance what the largest integer will be, so I want to try to use the smallest datatype possible. So far I have the following:

>>> import numpy as np
>>> def minimal_type(max_val, types=[np.uint8,np.uint16,np.uint32,np.uint64]):
    ''' finds the minimal data type needed to correctly store the given max_val
        returns None if none of the provided types are sufficient
    '''
    for t in types:
        if max_val <= np.iinfo(t).max:
            return t
    return None

>>> print(minimal_type(42))
<class 'numpy.uint8'>
>>> print(minimal_type(255))
<class 'numpy.uint8'>
>>> print(minimal_type(256))
<class 'numpy.uint16'>
>>> print(minimal_type(4200000000))
<class 'numpy.uint32'>
>>>

Is there a numpy builtin way to achieve this functionality?

user2357112 · Accepted Answer · 2020-08-11 21:53:35Z

12

It's numpy.min_scalar_type. Examples from the docs:

>>> np.min_scalar_type(10)
dtype('uint8')
>>> np.min_scalar_type(-260)
dtype('int16')
>>> np.min_scalar_type(3.1)
dtype('float16')
>>> np.min_scalar_type(1e50)
dtype('float64')
>>> np.min_scalar_type(np.arange(4,dtype='f8'))
dtype('float64')

You might not be interested in the behavior for floats, but I'm including it anyway for other people who come across the question, particularly since the use of float16 and the lack of float->int demotion might be surprising.

answered Aug 11, 2020 at 21:53

user2357112

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