learn-python/contrib/numpy/operations-on-arrays.md

Operations on Arrays

NumPy Arithmetic Operations

NumPy offers a broad array of operations for arrays, including arithmetic functions.

The arithmetic operations in NumPy are popular for their simplicity and efficiency in handling array calculations.

Addition

we can use the + operator to perform element-wise addition between two or more NumPy arrays.

Code

import numpy as np
array_1 = np.array([9, 10, 11, 12])
array_2 = np.array([1, 3, 5, 7])
result_1 = array_1 + array_2
print("Utilizing the + operator:", result_1) 

Output:

Utilizing the + operator: [10 13 16 19]

Subtraction

we can use the - operator to perform element-wise subtraction between two or more NumPy arrays.

Code

import numpy as np
array_1 = np.array([9, 10, 11, 12])
array_2 = np.array([1, 3, 5, 7])
result_1 = array_1 - array_2
print("Utilizing the - operator:", result_1) 

Output:

Utilizing the - operator: [8 7 6 5]

Multiplication

we can use the * operator to perform element-wise multiplication between two or more NumPy arrays.

Code

import numpy as np
array_1 = np.array([9, 10, 11, 12])
array_2 = np.array([1, 3, 5, 7])
result_1 = array_1 * array_2
print("Utilizing the * operator:", result_1) 

Output:

Utilizing the * operator: [9 30 55 84]

Division

we can use the / operator to perform element-wise division between two or more NumPy arrays.

Code

import numpy as np
array_1 = np.array([9, 10, 11, 12])
array_2 = np.array([1, 3, 5, 7])
result_1 = array_1 / array_2
print("Utilizing the / operator:", result_1) 

Output:

Utilizing the / operator: [9. 3.33333333 2.2 1.71428571]

Exponentiation

we can use the ** operator to perform element-wise exponentiation between two or more NumPy arrays.

Code

import numpy as np
array_1 = np.array([9, 10, 11, 12])
array_2 = np.array([1, 3, 5, 7])
result_1 = array_1 ** array_2
print("Utilizing the ** operator:", result_1) 

Output:

Utilizing the ** operator: [9 1000 161051 35831808]

Modulus

We can use the % operator to perform element-wise modulus operations between two or more NumPy arrays.

Code

import numpy as np
array_1 = np.array([9, 10, 11, 12])
array_2 = np.array([1, 3, 5, 7])
result_1 = array_1 % array_2
print("Utilizing the % operator:", result_1) 

Output:

Utilizing the % operator: [0 1 1 5]

NumPy Comparision Operations

NumPy provides various comparison operators that can compare elements across multiple NumPy arrays.

less than operator

The < operator returns True if the value of operand on left is less than the value of operand on right.

Code

import numpy as np
array_1 = np.array([12,15,20])
array_2 = np.array([20,15,12])
result_1 = array_1 < array_2
print("array_1 < array_2:",result_1)

Output:

array_1 < array_2 : [True False False]

less than or equal to operator

The <= operator returns True if the value of operand on left is lesser than or equal to the value of operand on right.

Code

import numpy as np
array_1 = np.array([12,15,20])
array_2 = np.array([20,15,12])
result_1 = array_1 <= array_2
print("array_1 <= array_2:",result_1)

Output:

array_1 <= array_2: [True True False]

greater than operator

The > operator returns True if the value of operand on left is greater than the value of operand on right.

Code

import numpy as np
array_1 = np.array([12,15,20])
array_2 = np.array([20,15,12])
result_2 = array_1 > array_2
print("array_1 > array_2:",result_2)    

Output:

array_1 > array_2 : [False False True]

greater than or equal to operator

The >= operator returns True if the value of operand on left is greater than or equal to the value of operand on right.

Code

import numpy as np
array_1 = np.array([12,15,20])
array_2 = np.array([20,15,12])
result_2 = array_1 >= array_2
print("array_1 >= array_2:",result_2)   

Output:

array_1 >= array_2: [False True True]

equal to operator

The == operator returns True if the value of operand on left is same as the value of operand on right.

Code

import numpy as np
array_1 = np.array([12,15,20])
array_2 = np.array([20,15,12])
result_3 = array_1 == array_2
print("array_1 == array_2:",result_3)

Output:

array_1 == array_2: [False True False]

not equal to operator

The != operator returns True if the value of operand on left is not equal to the value of operand on right.

Code

import numpy as np
array_1 = np.array([12,15,20])
array_2 = np.array([20,15,12])
result_3 = array_1 != array_2
print("array_1 != array_2:",result_3)    

Output:

array_1 != array_2: [True False True]

NumPy Logical Operations

Logical operators perform Boolean algebra. A branch of algebra that deals with True and False statements.

It illustrates the logical operations of AND, OR, and NOT using np.logical_and(), np.logical_or(), and np.logical_not() functions, respectively.

Logical AND

Evaluates the element-wise truth value of array_1 AND array_2

Code

import numpy as np
array_1 = np.array([True, False, True])
array_2 = np.array([False, False, True])
print(np.logical_and(array_1, array_2))

Output:

[False False True]

Logical OR

Evaluates the element-wise truth value of array_1 OR array_2

Code

import numpy as np
array_1 = np.array([True, False, True])
array_2 = np.array([False, False, True])
print(np.logical_or(array_1, array_2))

Output:

[True False True]

Logical NOT

Evaluates the element-wise truth value of array_1 NOT array_2

Code

import numpy as np
array_1 = np.array([True, False, True])
array_2 = np.array([False, False, True])
print(np.logical_not(array_1)) 

Output:

[False True False]