Changing an array's type by setting dtype directly does not alter the data at the byte-level, only how that data is interpreted as a number, string etc. As it happens, the byte-representations of zero are the same for integers (int64) and floats (float64), so the result of setting dtype is as expected. However, the 8-bytes representing 1.0 translate to the integer 4602678819172646912. To convert the data type properly, use astype() which returns a new array (with its own data):

In [x]: a = np.ones((3,))
In [x]: a
Out[x]: array([ 1.,  1.,  1.])

In [x]: a.astype('int')
In [x]: a
Out[x]: array([1, 1, 1])