You're looking for numpy.iinfo for integer types. Documentation here.

There's also numpy.finfo for floating point types. Documentation here.

The numbers in your array_int32 are too large to be represented with 16 bits (a signed integer value with 16 bits can only represent a maximum value of 2^16-1=32767). Apparently, numpy just sets the resulting numbers to zero in this case.

This behavior can be modified by changing the optional casting argument of astype The documentation states

Starting in NumPy 1.9, astype method now returns an error if the string dtype to cast to is not long enough in ‘safe’ casting mode to hold the max value of integer/float array that is being casted. Previously the casting was allowed even if the result was truncated.

So, an additional requirement casting='safe' will result in a TypeError, as the conversion from 32 (or 64) bits downto 16, as the maximum value of original type is too large for the new type, e.g.

import numpy as np
array_int32 = np.array([31784960, 69074944, 165871616])
array_int16 = array_int32.astype(np.int16, casting='safe')

results in

TypeError: Cannot cast array from dtype('int64') to dtype('int16') according to the rule 'safe'