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Numpy is not designed for arbitrary precision calculation, either in (decimal) real numbers or in integer arithmetic. It is hence significantly faster for data computation where such precision is not needed (which is most of data analysis in government, medicine, economics, etc.). In mathematics, where this is needed, we have things like Sage integers; these are NOT just "int"s, but will use up all your available memory if you let it.

The negative answer is a fun thing that happens with such integers - you literally went around the corner from infinity and through negative numbers! This is a very common behavior with integers - see the Numpy docs:

int64   Integer (-9223372036854775808 to 9223372036854775807)