NumPy reshape()

import numpy as np

# create an array
originalArray = np.array([0, 1, 2, 3, 4, 5, 6, 7])

# reshape the array
reshapedArray = np.reshape(originalArray, (2,4))

print(reshapedArray)

'''
Output

[[0 1 2 3]
 [4 5 6 7]]
'''

reshape() Syntax

The syntax of reshape() is:

numpy.reshape(array, shape, order)

reshape() Arguments

reshape() takes three arguments:

• array - an original array that is to be reshaped
• shape - desired new shape of the array (can be integer or tuple of integers)
• order (optional) - specifies the order in which the array elements are reshaped

reshape() Return Value

The reshape() method returns the reshaped array.

Note: The reshape() method throws an error if the shape doesn't match the number of elements.

Example 1: Reshape 1D Array to 3D Array

import numpy as np

# create an array
originalArray = np.array([0, 1, 2, 3, 4, 5, 6, 7])

# reshape the array to 3D
reshapedArray = np.reshape(originalArray, (2, 2, 2))

print(reshapedArray)

Output

[[[0 1]
  [2 3]]

 [[4 5]
  [6 7]]]

Using Optional Order Argument in reshape()

The order argument specifies the order in which the array elements are reshaped.

The order can be:

• 'C' - elements are stored row-wise
• 'F' - elements are stored column-wise
• 'A' - elements are stored based on the original array's memory layout.

Example 2: Reshape Array Row-Wise

import numpy as np

originalArray = np.array([0, 1, 2, 3, 4, 5, 6, 7])

# reshape the array to 2D
# the last argument 'C' reshapes the array row-wise
reshapedArray = np.reshape(originalArray, (2, 4), 'C')

print(reshapedArray)

Output

[[0 1 2 3]
 [4 5 6 7]]

Example 3: Reshape Array Column-Wise

import numpy as np

originalArray = np.array([0, 1, 2, 3, 4, 5, 6, 7])

# reshape the array to 2D
# the last argument 'F' reshapes the array column-wise
reshapedArray = np.reshape(originalArray, (2, 4), 'F')

print(reshapedArray)

Output

[[0 2 4 6]
 [1 3 5 7]]

Example 4: Flatten a Multidimensional Array to 1D Array

In our previous examples, we used tuples as the shape argument (second argument), which determines the shape of the new array.

However, if we use -1 as a shape argument, the reshape() method reshapes the original array into a one-dimensional array.

import numpy as np

originalArray = np.array([[0,1,2,3],[4,5,6,7]])

# flatten the array
# to flatten the array, -1 is used as the second argument
reshapedArray = np.reshape(originalArray, -1)

print(reshapedArray)

# Output = [0 1 2 3 4 5 6 7]