Why is a NumPy int not an instance of a Python int, but a NumPy float is an instance of a Python float?

Question

Consider the following:

>>> import numbers
>>> import numpy
>>> a = numpy.int_(0)
>>> isinstance(a, int)
False
>>> isinstance(a, numbers.Integral)
True
>>> b = numpy.float_(0)
>>> isinstance(b, float)
True
>>> isinstance(b, numbers.Real)
True

NumPy's numpy.int_ and numpy.float_ types are both in Python's numeric abstract base class hierarchy, but it is strange to me that a np.int_ object is not an instance of the built-in int class, while a np.float_ object is an instance of the built-in float type.

Why is this the case?

Amadan · Accepted Answer · 2018-07-05 08:48:53Z

Python integers can be arbitrary length: type(10**1000) is still int, and will print out a one and then a thousand zeros on your screen if you output it.

Numpy int64 (which is what int_ is on my machine) are integers represented by 8 bytes (64 bits), and anything over that cannot be represented. For example, np.int_(10)**1000 will give you a wrong answer - but quickly ;).

Thus, they are different kinds of numbers; subclassing one under the other makes as much sense as subclassing int under float would, is what I assume numpy people thought. It is best to keep them separate, so that no-one is confused about the fact that it would be unwise to confuse them.

The split is done because arbitrary-size integers are slow, while numpy tries to speed up computation by sticking to machine-friendly types.

On the other hand, floating point is the standard IEEE floating point, both in Python and in numpy, supported out-of-the-box by our processors.

lenik · Accepted Answer · 2018-07-05 14:01:58Z

2

Because numpy.int_() is actually 64-bit, and int can have an arbitrary size, it uses about 4 extra bytes for every 2^30 worth of bits you put in. int64 has constant size:

>>> import numpy as np
>>> a = np.int_(0)
>>> type(a)
<type 'numpy.int64'>
>>> b = 0
>>> type(b)
<type 'int'>

edited Jul 5, 2018 at 14:01

answered Jul 5, 2018 at 8:44

lenik

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2 Comments

Artem Mavrin Over a year ago

This doesn't answer my question. The type of a = np.float_(0) is numpy.float64, but it is still an instance of float. My question can be rephrased as "why is numpy.float64 a subclass of float but numpy.int64 is not a subclass of int?"

lenik Over a year ago

@ArtemMavrin int can have an arbitrary size, it uses about 4 extra bytes for every 2^30 worth of bits you put in. int64 has constant size.

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