python+numpy: why does numpy.log throw an attribute error if its operand is too big?
Question:
Running
np.log(math.factorial(21))
throws an AttributeError: log. Why is that? I could imagine a ValueError, or some sort of UseYourHighSchoolMathsError, but why the attribute error?
Answers:
The result of math.factorial(21) is a Python long. numpy cannot convert it to one of its numeric types, so it leaves it as dtype=object. The way that unary ufuncs work for object arrays is that they simply try to call a method of the same name on the object. E.g.
np.log(np.array([x], dtype=object)) <-> np.array([x.log()], dtype=object)
Since there is no .log() method on a Python long, you get the AttributeError.
Prefer the math.log() function, that does the job even on long integers.
Running
np.log(math.factorial(21))
throws an AttributeError: log. Why is that? I could imagine a ValueError, or some sort of UseYourHighSchoolMathsError, but why the attribute error?
The result of math.factorial(21) is a Python long. numpy cannot convert it to one of its numeric types, so it leaves it as dtype=object. The way that unary ufuncs work for object arrays is that they simply try to call a method of the same name on the object. E.g.
np.log(np.array([x], dtype=object)) <-> np.array([x.log()], dtype=object)
Since there is no .log() method on a Python long, you get the AttributeError.
Prefer the math.log() function, that does the job even on long integers.