ong `axis` where the dimension is smaller by `n`. The
    type of the output is the same as the type of the difference
    between any two elements of `a`. This is the same as the type of
    `a` in most cases. A notable exception is `datetime64`, which
    results in a `timedelta64` output array.

See Also
--------
gradient, ediff1d, cumsum

Notes
-----
Type is preserved for boolean arrays, so the result will contain
`False` when consecutive elements are the same and `True` when they
differ.

For unsigned integer arrays, the results will also be unsigned. This
should not be surprising, as the result is consistent with
calculating the difference directly:

>>> u8_arr = np.array([1, 0], dtype=np.uint8)
>>> np.diff(u8_arr)
array([255], dtype=uint8)
>>> u8_arr[1,...] - u8_arr[0,...]
np.uint8(255)

If this is not desirable, then the array should be cast to a larger
integer type first:

>>> i16_arr = u8_arr.astype(np.int16)
>>> np.diff(i16_arr)
array([-1], dtype=int16)

Examples
--------
>>> import numpy as np
>>> x = np.array([1, 2, 4, 7, 0])
>>> np.diff(x)
array([ 1,  2,  3, -7])
>>> np.diff(x, n=2)
array([  1,   1, -10])

>>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
>>> np.diff(x)
array([[2, 3, 4],
       [5, 1, 2]])
>>> np.diff(x, axis=0)
array([[-1,  2,  0, -2]])

>>> x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64)
>>> np.diff(x)
array([1, 1], dtype='timedelta64[D]')

r