Why is numpy.int32 not recognized as an int type

Why should numpy.int32 descend from int? int is a specific class. It is one way of representing integers. That doesn't mean that every class that represents integers should descend from int. numpy.int32 has different semantics and different methods - for example, it has most of the functionality needed to operate like a 0-dimensional array - and inheriting from int isn't particularly useful for implementing numpy.int32.

On some builds of Python 2 (Windows only?), numpy.int32 actually will descend from int (which is also 32-bit on those builds), but I believe this design decision dates back to a time when int performed wraparound arithmetic like numpy.int32 instead of promoting to long on overflow, and when operator.index didn't exist. It was a more reasonable decision back then.

As for how to treat numpy.int32 like int, numbers.Integral does a sort of okay job, but the implementation relies on people explicitly register-ing their classes with numbers.Integral, and people often don't think to do that. NumPy didn't add the register calls until 2014, 6 years after numbers.Integral was introduced. Similar libraries like SymPy still don't have the calls.

I find operator.index to be a better check:

try:
    real_int = operator.index(some_intlike_thing)
except TypeError:
    # Not intlike.
    do_something_about_that()

operator.index is the hook an int-like class has to implement to make its instances usable as a sequence index. It's a stricter check than int(x), which would accept 3.5 and '3'. Since there's a concrete, easily noticeable impact if this hook is missing, it's more likely to be present than numbers.Integral support.

__mro__ lists the inheritance stack of a class:

np.int32.__mro__
Out[30]: 
(numpy.int32,
 numpy.signedinteger,
 numpy.integer,
 numpy.number,
 numpy.generic,
 object)

int.__mro__
Out[31]: (int, object)

For a basic array:

x=np.array([1,2,3])    
x.dtype
Out[33]: dtype('int32')

isinstance of classes on this stack returns True:

isinstance(x[0], np.int32)
Out[37]: True    
isinstance(x[0], np.number)
Out[38]: True    

int isn't on this stack:

isinstance(x[0], int)
Out[39]: False    
isinstance(x[0], object)
Out[40]: True

item extracts a value from its numpy wrapper:

isinstance(x[0].item(), int)
Out[41]: True

@kazemakase suggests using the numbers module:

isinstance(x[0], numbers.Integral)
Out[47]: True

edit

isinstance accepts a tuple of classes, so we can handle both the int and numpy cases with one test:

In [259]: isinstance(x[0], (int,np.integer))                                                           
Out[259]: True
In [260]: isinstance(x[0].item(), (int,np.integer))                                                    
Out[260]: True