NumPy: Arrays Shape Manipulation

Arrays Shape Manipulation

• The shape of the array tells us how many elements the array has along each dimension. It is represented as tuple of integers. For example ‒

■ A one D array has a shape (n, )

■ A two D array has a shape(rows, columns)

■ A three D array has a shape(depth, rows, columns)

• For example ‒ Following Python program tells us the shape of the array

import numpy as np

arr = np.array([[1,2,3],

 [4,5,6]])

print(arr.shape)

Output

(2, 3)

This means that array has 2 rows and 3 columns.

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•  Manipulating the shape of the array means converting one D array into 2D format or combining or splitting the array data properly.

•  Following are some important functions that are used in arrays shape manipulation –

1) Reshape :

This function changes the shape of the array. For example ‒

For example ‒

In [11]: import numpy as np

a = np.array([10, 20, 30, 40, 50, 60])

b = a.reshape(3,2)

print("Original Array: \n",a)

print("Reshaped Array: \n",b)

Original Array:

[10 20 30 40 50 60]

Reshaped Array:

[[10 20]

[30 40]

[50 60]]

2) Flatten:

This function converts the array to 1D. It always returns a copy. For example ‒

Python program

import numpy as np

arr = np.array([[10,20],

[30,40],

[50,60]])

result = arr.flatten()

print(result)

Output

[10 20 30 40 50 60]

Note that the flatten function always returns a copy, so changes in resultant array do not affect the original.

3) Resize :

This function changes the shape and size of array. For example ‒

Python program

import numpy as np

arr = np.array([10,20,30,40])

arr.resize((2,3))

print(arr)

Output

[[10 20 30]

[40 0 0]]

When called as a method (arr.resize()), it modifies the array in place.

If the new shape has more elements than the original, NumPy fills the extra space with zeros.

Example:1

Write a Python program to demonstrate the exponentiation operation on arrays using NumPy.

Solution:

In [2]: import numpy as np

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

b = a ** 2

print("Exponentiation using ** operator ",b)

c = np.power(a,2)

print("Exponentiation using power() function ",c)

Exponentiation using ** operator [ 1 4 9 16 25]

Exponentiation using power() function [ 1 4 9 16 25]

Example:2

Write a Python program to demonstrate the modulus operation on arrays using NumPy.

Solution:

In [4]: import numpy as np

a = np.array([10,20,30,40])

b = np.array([2,3,7,9])

c = a % b

print("Modulus using % operator ",c)

d = np.mod(a,b)

print("Modulus using mod function",d)

Modulus using % operator [0 2 2 4]

Modulus using mod function [0 2 2 4]