Operations on NumPy Arrays

NumPy arrays support a wide range of operations, making it a powerful library for numerical computations. These include:

Arithmetic operations.
Universal functions (like sin(), cos(), log()).
Aggregation operations (like sum(), mean(), std()).

Arithmetic Operations

NumPy allows element-wise arithmetic operations between arrays or between arrays and scalars.

Examples

import numpy as np
 
# Create arrays
array1 = np.array([1, 2, 3])
array2 = np.array([4, 5, 6])
 
# Addition
print("Addition:", array1 + array2)  # Output: [5 7 9]
 
# Subtraction
print("Subtraction:", array1 - array2)  # Output: [-3 -3 -3]
 
# Multiplication
print("Multiplication:", array1 * array2)  # Output: [4 10 18]
 
# Division
print("Division:", array1 / array2)  # Output: [0.25 0.4  0.5 ]
 
# Scalar operations
print("Scalar Multiplication:", array1 * 2)  # Output: [2 4 6]

Universal Functions (ufuncs)

Universal functions operate element-wise on arrays. Some commonly used ufuncs are:

np.sin()
np.cos()
np.log()
np.exp()

Examples

# Create an array
array = np.array([0, np.pi / 2, np.pi])
 
# Sine function
print("Sine:", np.sin(array))  # Output: [0. 1. 0.]
 
# Cosine function
print("Cosine:", np.cos(array))  # Output: [ 1.  0. -1.]
 
# Logarithm (natural log)
log_array = np.array([1, np.e, np.e**2])
print("Logarithm:", np.log(log_array))  # Output: [0. 1. 2.]
 
# Exponential
print("Exponential:", np.exp([1, 2, 3]))  # Output: [ 2.71828183  7.3890561  20.08553692]

Aggregation Operations

Aggregation functions compute a single value from an array, such as the sum, mean, or standard deviation.

Common Aggregation Functions

Function	Description
`np.sum()`	Sum of all elements
`np.mean()`	Mean (average) of elements
`np.std()`	Standard deviation
`np.min()`	Minimum value
`np.max()`	Maximum value

Examples

# Create an array
array = np.array([1, 2, 3, 4, 5])
 
# Sum
print("Sum:", np.sum(array))  # Output: 15
 
# Mean
print("Mean:", np.mean(array))  # Output: 3.0
 
# Standard Deviation
print("Standard Deviation:", np.std(array))  # Output: 1.4142135623730951
 
# Min and Max
print("Minimum:", np.min(array))  # Output: 1
print("Maximum:", np.max(array))  # Output: 5

Try It Yourself

Problem 1: Perform Arithmetic Operations

Create two arrays and perform addition, subtraction, multiplication, and division. Print the results.

Show Code

import numpy as np
 
# Arrays
array1 = np.array([10, 20, 30])
array2 = np.array([1, 2, 3])
 
# Perform operations
print("Addition:", array1 + array2)
print("Subtraction:", array1 - array2)
print("Multiplication:", array1 * array2)
print("Division:", array1 / array2)

Problem 2: Use Universal Functions

Create an array of angles (in radians) and compute the sine, cosine, and exponential values.

Show Code

import numpy as np
 
# Angles in radians
angles = np.array([0, np.pi / 4, np.pi / 2])
 
# Compute trigonometric values
print("Sine:", np.sin(angles))
print("Cosine:", np.cos(angles))
print("Exponential:", np.exp(angles))

Problem 3: Aggregate Array Data

Create an array and find its sum, mean, standard deviation, and maximum value.

Show Code

import numpy as np
 
# Create an array
array = np.array([4, 7, 1, 8, 3])
 
# Perform aggregations
print("Sum:", np.sum(array))
print("Mean:", np.mean(array))
print("Standard Deviation:", np.std(array))
print("Maximum:", np.max(array))

Pyground

Play with Python!

Output:

Numpy Data Types Indexing