Python – numpy.reshape

numpy.reshape() gives a new shape to an array without changing its data. Its syntax is as follows −

numpy.reshape(arr, newshape, order='C')

Parameters

numpy.reshape() can accept the following parameters −

• arr − Input array.

• shape − endpoint of the sequence

• newshape − If an integer, then the result it will be a 1-D array of that length, and one dimension can be -1.

• order − It defines the order in which the input array elements should be read.

  • If the order is ‘C’, then it reads and writes the elements which are using a C-like index order where the last index changes the fastest and the first axis index changes slowly.

  • ‘F’ means to read and write the elements using a Fortran-like index order where the last index axis changes slowly and the first axis index changes fast.

  • ‘A’ means to read/write the elements in Fortran-like index order, when the array is contiguous in memory.

Example 1

Let us consider the following example −

# Import numpy
import numpy as np

# input array
x = np.array([[3,5,6], [7,8,9]])
print("Array Input :\n", x)

# reshape() function
y = np.reshape(x, (3, -3))
print("Reshaped Array: \n", y)

Output

It will generate the following output −

Array Input :
 [[3 5 6]
 [7 8 9]]
Reshaped Array:
 [[3 5]
 [6 7]
 [8 9]]

Example 2

Let us take another example −

# Import numpy
import numpy as np

# Create an input array
x = np.array([[1,3,4], [4,6,7]])
print("Array Input :\n", x)

# reshape() function
y = np.reshape(x, 6, order='C')
print("Reshaped Array: \n", y)

Output

It will generate the following output −

Array Input :
 [[1 3 4]
 [4 6 7]]
Reshaped Array:
 [1 3 4 4 6 7]