NumPy Basics and Arrays

Duration: 18 min

Try it in Google Colab:

Visual: NumPy Array Structure

1D Array (Vector)
[1, 2, 3, 4, 5]

2D Array (Matrix)
[[1, 2, 3],
 [4, 5, 6]]

3D Array (Tensor)
[[[1, 2], [3, 4]],
 [[5, 6], [7, 8]]]

Key Concepts Table

What is NumPy?

NumPy is a Python library for numerical computing. It provides efficient arrays and mathematical functions to implement the math concepts you learned.

Creating Arrays

import numpy as np

# 1D array (vector)
a = np.array([1, 2, 3])

# 2D array (matrix)
A = np.array([[1, 2], [3, 4]])

# Array of zeros
zeros = np.zeros((2, 3))

# Array of ones
ones = np.ones((2, 3))

# Range of values
range_arr = np.arange(0, 10, 2)  # [0, 2, 4, 6, 8]

# Random array
random_arr = np.random.rand(3, 3)

Array Operations

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

# Element-wise operations
print(a + b)      # [5, 7, 9]
print(a * b)      # [4, 10, 18]
print(a / b)      # [0.25, 0.4, 0.5]

# Scalar operations
print(a * 2)      # [2, 4, 6]
print(a + 10)     # [11, 12, 13]

Array Properties

A = np.array([[1, 2, 3], [4, 5, 6]])

print(A.shape)    # (2, 3) - 2 rows, 3 columns
print(A.size)     # 6 - total elements
print(A.dtype)    # int64 - data type
print(A.ndim)     # 2 - number of dimensions

Indexing and Slicing

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

print(a[0])       # 10 - first element
print(a[-1])      # 50 - last element
print(a[1:4])     # [20, 30, 40] - elements 1 to 3
print(a[::2])     # [10, 30, 50] - every 2nd element

Practice Quizzes

Quiz 1: What is the shape of a 3x4 matrix?

• [✓] (3,4)
• (4,3)
• 12
• 7

Quiz 2: What does broadcasting allow?

• Sending data
• [✓] Operating on different shapes
• Creating arrays
• Indexing arrays

Quiz 3: How do you create a NumPy array?

• np.create()
• [✓] np.array()
• np.make()
• np.new()