Approches to transform the shape of ndarray

• numpy.reshape(array, (shape), order = 'C')
  transform [8, 1] to [2, 4]

import numpy as np

arrayA = np.arange(8)
# arrayA = array([0, 1, 2, 3, 4, 5, 6, 7])

np.reshape(arrayA, (2, 4))
# array([[0, 1, 2, 3],
        [4, 5, 6, 7]])

If the number of elements is not same between input and output, raise ValueError

• numpy.reshape(array, (shape), order = 'F')

np.reshape(arrayA, (2, 4), order = 'F')

# array([[0, 2, 4, 6],
        [1, 3, 5, 7]])

• ndarray.reshape()

arrayB = arrayA.reshape(2, 4)

ndarray.reshape() doesn't change the data from original matrix, it returns the modified one

• numpy.resize()

arrayA = np.arange(8)
arrayB = arrayA.resize(arrayA, (2, 4))
# array([[0, 1, 2, 3],
         [4, 5, 6, 7]])

arrayC = np.resize(arrayA, (3, 4))
arrayC
# array([[0, 1, 2, 3],
         [4, 5, 6, 7],
         [0, 1, 2 ,3]])

If the new size is larger than the original one, it will repeat the elements to fill the spaces.