Confusing Type Of Results Of Binary Arithmetic Operations Involving Python Builtin Numeric Types And Numpy Types Float64 And Complex128

Using multiplication operator mul for example, code import numpy as np v = 1.0 a = float(v) b = np.float64(v) print(type(a*b), type(a.__mul__(b))) a = complex(v) b = np.complex

Solution 1:

I may be wrong about the priority. There is a priority value that controls how ndarray and its subclasses interact, but with the Python numbers it may be just a matter of NotImplemented, or subclassing

https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types

Note If the right operand’s type is a subclass of the left operand’s type 
and that subclass provides a different implementation of the reflected 
method for the operation, this method will be called before the left 
operand’s non-reflected method. This behavior allows subclasses to 
override their ancestors’ operations.

With your example:

In [107]: a = 1.0; b = np.float64(1.0)
In [108]: type(a*b)
Out[108]: numpy.float64
In [109]: type(a.__mul__(b))
Out[109]: float
In [110]: type(b.__rmul__(a))
Out[110]: numpy.float64
In [111]: type(b).__mro__
Out[111]: 
(numpy.float64,
 numpy.floating,
 numpy.inexact,
 numpy.number,
 numpy.generic,
 float,
 object)

b controls a*b because type(b) is a subclass of type(a). b.__rmul__ is used.

np.complex128 is also a subclass of complex.

With integers, not implemented is the factor

In [113]: c = 1; d = np.int64(1)
In [114]: type(c*d)
Out[114]: numpy.int64
In [115]: c.__mul__(d)
Out[115]: NotImplemented
In [116]: d.__rmul__(c)
Out[116]: 1
In [117]: type(_)
Out[117]: numpy.int64
In [118]: type(d).__mro__
Out[118]: 
(numpy.int64,
 numpy.signedinteger,
 numpy.integer,
 numpy.number,
 numpy.generic,
 object)

np.int64 is not a subclass of int.