Python reshape list to ndim array

You can think of reshaping that the new shape is filled row by row (last dimension varies fastest) from the flattened original list/array.

An easy solution is to shape the list into a (100, 28) array and then transpose it:

x = np.reshape(list_data, (100, 28)).T

Update regarding the updated example:

np.reshape([0, 0, 1, 1, 2, 2, 3, 3], (4, 2)).T
# array([[0, 1, 2, 3],
#        [0, 1, 2, 3]])

np.reshape([0, 0, 1, 1, 2, 2, 3, 3], (2, 4))
# array([[0, 0, 1, 1],
#        [2, 2, 3, 3]])