NumPy Fundamentals
Master the fundamental package for scientific computing in Python. This is a foundational concept in artificial intelligence and machine learning that professional developers rely on daily. The explanations below are written to be beginner-friendly while covering the depth and nuance that comes from real-world AI/ML experience. Take your time with each section and practice the examples
45 min•By Priygop Team•Last updated: Feb 2026
What is NumPy?
NumPy (Numerical Python) is the fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of high-level mathematical functions to operate on these arrays.
Key Features
- N-dimensional Arrays: efficient array operations
- Mathematical Functions: Built-in mathematical operations
- Linear Algebra: Matrix operations and decompositions
- Random Number Generation: Various probability distributions
Creating NumPy Arrays
Example
import numpy as np
# Create arrays
arr1 = np.array([1, 2, 3, 4, 5])
arr2 = np.zeros((3, 4)) # 3x4 array of zeros
arr3 = np.ones((2, 3)) # 2x3 array of ones
arr4 = np.arange(0, 10, 2) # Array from 0 to 10, step 2
arr5 = np.linspace(0, 1, 5) # 5 evenly spaced values from 0 to 1
# Random arrays
random_arr = np.random.rand(3, 3) # 3x3 random array
normal_arr = np.random.normal(0, 1, 100) # 100 normal distributed valuesArray Operations
Example
# Basic operations
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b) # [5 7 9]
print(a * b) # [4 10 18]
print(a ** 2) # [1 4 9]
# Statistical operations
data = np.array([1, 2, 3, 4, 5])
print(np.mean(data)) # 3.0
print(np.std(data)) # 1.414...
print(np.median(data)) # 3.0
print(np.max(data)) # 5
print(np.min(data)) # 1