Software Automation
Numpy

Part 1: What is NumPy?

NumPy (short for Numerical Python) is a library that helps you work with arrays and perform fast mathematical operations. It is widely used in:

• Data science

• Artificial intelligence

• Machine learning

• Scientific computing
  
  

Why not just use Python lists?

Python lists are great for storing collections of items, but they are:

• Slower for numerical operations

• Less flexible when handling multi-dimensional data (like grids or matrices)

NumPy arrays:

• Are faster and more efficient

• Let you apply mathematical operations to entire datasets at once (called vectorisation)

• Support multi-dimensional structures like 2D matrices and 3D tensors
  
  

Real-world analogy

Think of a NumPy array like a spreadsheet of numbers. Instead of going cell by cell (like with a Python loop), NumPy lets you do calculations on the whole sheet at once.

Part 2: Getting Started with NumPy

Before we can use NumPy, we need to install and import it.

Step 1: Install NumPy (only once per environment)

If you're using Google Colab or Jupyter notebooks, NumPy might already be installed. If not:

pip install numpy

Step 2: Import NumPy

Import NumPy using the common alias np:

import numpy as np

Now any time you want to use NumPy’s features, you’ll write np.function_name().

Broadcasting

Broadcasting allows arrays with different shapes to still operate together:

Broadcasting saves time by eliminating the need for nested loops.

Part 7: Lab Exercises

Try the following problems to test your understanding. Discuss with a classmate or ask your teacher if stuck.

Exercise 1: Creating Arrays

1. Create a 5x5 array filled with zeros.

2. Make a 3x3 identity matrix.

3. Create a 1D array containing the numbers from 10 to 50 inclusive.
   
   

Exercise 2: Indexing and Slicing

1. Use this array: arr = np.array([5, 10, 15, 20, 25, 30])

   • Slice the second to fourth elements.

   • Slice every second element.

2. Create a 3x3 array (your own values) and extract:

   • The first column.

   • The last row.
     
     

Exercise 3: Arithmetic and Aggregation

1. Create two 1D arrays with three numbers each. Perform:

   • Addition

   • Subtraction

   • Multiplication

2. Make an array with values from 1 to 100. Calculate:

   • Mean

   • Maximum

   • Standard Deviation (np.std())
     
     

Exercise 4: Reshaping and Broadcasting

1. Create a 1D array with 12 elements. Reshape it into 3 rows and 4 columns.

2. Multiply all elements in a 3x3 matrix by 5.

3. Add the vector [1, 2, 3] to each row of a 3x3 matrix.

🚀 Challenge Task

If you finish early, try this:

1. Create a 10x10 matrix of random numbers between 0 and 1.

2. Replace all numbers greater than 0.5 with 1, and the rest with 0.

3. Count how many 1s and 0s are in the matrix using np.sum().