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.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
Why is this the case?
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.
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
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.