python + numpy:如果numpy.log的操作数太大，为什么会抛出属性错误? [英] python+numpy: why does numpy.log throw an attribute error if its operand is too big?

问题描述

运行

np.log(math.factorial(21))

抛出一个AttributeError: log.这是为什么?我可以想象一个ValueError或某种UseYourHighSchoolMathsError，但是为什么属性错误?

throws an AttributeError: log. Why is that? I could imagine a ValueError, or some sort of UseYourHighSchoolMathsError, but why the attribute error?

推荐答案

math.factorial(21)的结果是Python长的. numpy无法将其转换为其数字类型之一，因此将其保留为dtype=object.一元ufuncs用于对象数组的方式是，它们只是尝试在对象上调用相同名称的方法.例如

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)

由于Python上没有.log()方法，因此您会得到AttributeError.

Since there is no .log() method on a Python long, you get the AttributeError.

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