3.8. Array Shape

  • Any shape operation changes only ndarray.shape and ndarray.strides and does not touch data

3.8.1. Get shape

import numpy as np


a = np.array([1, 2, 3])

a.shape     # (3,)
import numpy as np


a = np.array([[1, 2, 3],
              [4, 5, 6]])

a.shape     # (2, 3)
import numpy as np


a = np.array([[1, 2, 3],
              [4, 5, 6],
              [7, 8, 9]])

a.shape     # (3, 3)
import numpy as np


a = np.array([[[ 1,  2,  3],
               [ 4,  5,  6],
               [ 5,  6,  7]],
              [[11, 22, 33],
               [44, 55, 66],
               [77, 88, 99]]])

a.shape         # (2, 3, 3)

3.8.2. Reshape

  • Returns new array

  • Does not modify inplace

  • a.reshape(1, 2) is equivalent to a.reshape((1, 2))

import numpy as np


a = np.array([1, 2, 3])

a.reshape(1, 3)
# array([[1, 2, 3]])

a.reshape(3, 1)
# array([[1],
#        [2],
#        [3]])
import numpy as np


a = np.array([[1, 2, 3],
              [4, 5, 6]])

a.reshape(3, 2)
# array([[1, 2],
#        [3, 4],
#        [5, 6]])

a.reshape(1, 6)
# array([[1, 2, 3, 4, 5, 6]])

a.reshape(6, 1)
# array([[1],
#        [2],
#        [3],
#        [4],
#        [5],
#        [6]])

a.reshape(5, 2)
# ValueError: cannot reshape array of size 6 into shape (5,2)
import numpy as np


a = np.array([1, 2, 3, 4, 5, 6, 7, 8])

a.reshape(2, 4)
# array([[1, 2, 3, 4],
#        [5, 6, 7, 8]])

a.reshape(2, 4, 1)
# array([[[1],
#         [2],
#         [3],
#         [4]],
#        [[5],
#         [6],
#         [7],
#         [8]]])

a.reshape(2, 2, 2)
# array([[[1, 2],
#         [3, 4]],
#        [[5, 6],
#         [7, 8]]])

a.reshape(1, 2, 4)
# array([[[1, 2, 3, 4],
#         [5, 6, 7, 8]]])

a.reshape(4, 2, 1)
#array([[[1],
#        [2]],
#       [[3],
#        [4]],
#       [[5],
#        [6]],
#       [[7],
#        [8]]])

a.reshape(1, 8, 1)
# array([[[1],
#         [2],
#         [3],
#         [4],
#         [5],
#         [6],
#         [7],
#         [8]]])

a.reshape(2, 3, 1)
# ValueError: cannot reshape array of size 8 into shape (2,3,1)

3.8.3. Flatten

  • Returns new array (makes memory copy - expensive)

  • Does not modify inplace

import numpy as np


a = np.array([1, 2, 3])

a.flatten()
# array([1, 2, 3])
import numpy as np


a = np.array([[1, 2, 3],
              [4, 5, 6]])

a.flatten()
# array([1, 2, 3, 4, 5, 6])
import numpy as np


a = np.array([[1, 2, 3],
              [4, 5, 6],
              [7, 8, 9]])

a.flatten()
# array([1, 2, 3, 4, 5, 6, 7, 8, 9])

3.8.4. Ravel

  • Ravel is the same as Flatten but returns a reference (or view) of the array if possible (i.e. memory is contiguous)

  • Otherwise returns new array (makes memory copy)

import numpy as np


a = np.array([1, 2, 3])

a.ravel()
# array([1, 2, 3])
import numpy as np


a = np.array([[1, 2, 3],
              [4, 5, 6]])

a.ravel()
# array([1, 2, 3, 4, 5, 6])
import numpy as np


a = np.array([[1, 2, 3],
              [4, 5, 6],
              [7, 8, 9]])

a.ravel()
# array([1, 2, 3, 4, 5, 6, 7, 8, 9])

3.8.5. Assignments

3.8.5.1. Shape

  • Complexity level: easy

  • Lines of code to write: 5 lines

  • Estimated time of completion: 5 min

  • Filename: solution/numpy_shape.py

English
  1. Given a: ndarray (see below)

  2. Flatten using method .ravel()

  3. Print a

  4. Change shape back to 3x3

  5. Print a

Polish
  1. Dany a: ndarray (patrz poniżej)

  2. Spłaszcz używając metody .ravel()

  3. Wypisz a

  4. Zmień kształt na powrót na 3x3

  5. Wypisz a

Input
a = np.array([[1, 2, 3],
              [4, 5, 6],
              [7, 8, 9]])