You can use

df['MPRN'] = pd.to_numeric(df['MPRN'], errors="coerce").fillna(0).astype('int64')

z_as_int64 = numpy.int64(z)

It's that simple. Make sure you have a good reason, though - there are a few good reasons to do this, but most of the time, you can just use a regular int directly.

As others have said, 32-bit versions of numpy still support 64-bit dtypes. But if you really need to convert to int32, you can use the astype function:

>>> import numpy as np
>>> x = np.array([1,2,3], dtype=np.int64)
>>> x
array([1, 2, 3])
>>> x.dtype
dtype('int64')
>>> y = x.astype(np.int32)
>>> y
array([1, 2, 3], dtype=int32)

int and long were "unified" a few versions back. Before that it was possible to overflow an int through math ops.

3.x has further advanced this by eliminating long altogether and only having int.

• Python 2: sys.maxint contains the maximum value a Python int can hold.
  • On a 64-bit Python 2.7, the size is 24 bytes. Check with sys.getsizeof().
• Python 3: sys.maxsize contains the maximum size in bytes a Python int can be.
  • This will be gigabytes in 32 bits, and exabytes in 64 bits.
  • Such a large int would have a value similar to 8 to the power of sys.maxsize.