NumPy arrays as ctypes: int vs. long

I ran into the following strange behavior of ctypes. When I convert a numpy array via ctypes to an int pointer, some values ​​are lost and extra zeros are added. More specifically, when I convert a numpy array with the following code

from numpy import *
import ctypes
from numpy.ctypeslib import ndpointer

x = arange(1, 10)
xInt = x.ctypes.data_as(ctypes.POINTER(ctypes.c_int * len(x)))
xLong = x.ctypes.data_as(ctypes.POINTER(ctypes.c_long * len(x)))

      

        
        
        
      

    

Then I get the following confusing results:

print [i for i in xInt.contents]
[1, 0, 2, 0, 3, 0, 4, 0, 5]

print [i for i in xLong.contents]
[1, 2, 3, 4, 5, 6, 7, 8, 9]

      

        
        
        
      

    

What's happening?