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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?
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Python's numpy. log() is a mathematical function that computes the natural logarithm of an input array's elements. The natural logarithm is the inverse of the exponential function, such that log (exp(x)) = x.
log() in Python. The numpy. log() is a mathematical function that helps user to calculate Natural logarithm of x where x belongs to all the input array elements. Natural logarithm log is the inverse of the exp(), so that log(exp(x)) = x.
NumPy Arrays are faster than Python Lists because of the following reasons: An array is a collection of homogeneous data-types that are stored in contiguous memory locations. On the other hand, a list in Python is a collection of heterogeneous data types stored in non-contiguous memory locations.
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.
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Prefer the math.log() function, that does the job even on long integers.
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