Mastering NumPy in Python: The Backbone of Scientific Computing

Chapter 2: Introduction to NumPy and Array Basics

🔹 1. What is NumPy?

NumPy (short for Numerical Python) is a powerful open-source library for numerical computations in Python. It provides a high-performance, N-dimensional array object called ndarray, and tools for efficiently operating on arrays. It is widely used in data science, machine learning, and scientific computing for tasks involving matrix algebra, data manipulation, and mathematical operations.

🔹 2. Why NumPy over Python Lists?

While Python lists are flexible, they are inefficient for large-scale numerical computations. NumPy arrays:

• Consume less memory
• Offer vectorized operations (no loops required)
• Are significantly faster
• Support broadcasting, masking, and multidimensional operations

🔹 3. Installing and Importing NumPy

To get started, you need to install NumPy:

pip install numpy

Then, import it in your Python code:

import numpy as np

🔹 4. Creating Arrays in NumPy

✅ 1D Array

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

✅ 2D Array

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

✅ Using Built-in Functions

🔹 5. Array Attributes

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

🔹 6. Indexing and Slicing Arrays

✅ 1D Indexing

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

print(arr[2])  # Output: 30

✅ 2D Indexing

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

print(arr[1, 2])  # Output: 6

✅ Slicing

print(arr[0:2])      # First two rows

print(arr[:, 1])     # All rows, column 1

🔹 7. Array Reshaping

You can reshape arrays to different dimensions:

a = np.arange(12)

b = a.reshape((3, 4))  # 3 rows, 4 columns

You can also flatten arrays:

flat = b.flatten()

🔹 8. Array Data Types

You can explicitly set the type of array elements:

arr = np.array([1.5, 2.3], dtype=np.float32)

🔹 9. Copy vs View in NumPy

• View (Shallow Copy): Changes in the view affect the original array.
• Copy (Deep Copy): Changes in the copy do not affect the original.

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

b = a.view()

c = a.copy()

🔹 10. Basic Array 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]

print(np.sqrt(a)) # [1. 1.41 1.73]

🔹 11. Broadcasting Basics

Broadcasting lets NumPy perform arithmetic between arrays of different shapes.

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

b = 2

print(a + b)  # [3 4 5]

🔹 12. Summary Table: Key Concepts