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.

numpy.ndarray.tolist will do it:

a.tolist()

If your data is a pandas series you can call their tolist wrapper with the same result.

Use val.item() to convert most NumPy values to a native Python type:

import numpy as np

# for example, numpy.float32 -> python float
val = np.float32(0)
pyval = val.item()
print(type(pyval))         # <class 'float'>

# and similar...
type(np.float64(0).item()) # <class 'float'>
type(np.uint32(0).item())  # <class 'int'>
type(np.int16(0).item())   # <class 'int'>
type(np.cfloat(0).item())  # <class 'complex'>
type(np.datetime64(0, 'D').item())  # <class 'datetime.date'>
type(np.datetime64('2001-01-01 00:00:00').item())  # <class 'datetime.datetime'>
type(np.timedelta64(0, 'D').item()) # <class 'datetime.timedelta'>
...

(A related method np.asscalar(val) was deprecated with 1.16, and removed with 1.23).

For the curious, to build a table of conversions of NumPy array scalars for your system:

for name in dir(np):
    obj = getattr(np, name)
    if hasattr(obj, 'dtype'):
        try:
            if 'time' in name:
                npn = obj(0, 'D')
            else:
                npn = obj(0)
            nat = npn.item()
            print('{0} ({1!r}) -> {2}'.format(name, npn.dtype.char, type(nat)))
        except:
            pass

There are a few NumPy types that have no native Python equivalent on some systems, including: clongdouble, clongfloat, complex192, complex256, float128, longcomplex, longdouble and longfloat. These need to be converted to their nearest NumPy equivalent before using .item().

Use .astype.

>>> a = numpy.array([1, 2, 3, 4], dtype=numpy.float64)
>>> a
array([ 1.,  2.,  3.,  4.])
>>> a.astype(numpy.int64)
array([1, 2, 3, 4])

See the documentation for more options.

This is merely a repost of the comments to close the question.

The built-in int isn't a valid dtype. It will be converted to whatever np.int_ is, which may be np.int64 or np.int32 depending on your platform. You can, but you must use dtype=object, which essentially removes the advantages of numpy to give you a Python list.

By juanpa.arrivillaga