NumPy & Data Manipulation

By the end of this session you will learn:

• What NumPy is

• Why NumPy is faster than Python lists

• Core NumPy operations

• Data manipulation techniques

• A real-world recommendation system example

1. Introduction (3 Minutes)

Python is widely used in data science, machine learning, and AI systems.

At the core of these systems lies a powerful library called NumPy.

NumPy stands for Numerical Python and provides:

High-performance arrays

Mathematical operations

Matrix computations

Fast vectorized operations

Many popular libraries are built on top of NumPy:

Pandas

Scikit-Learn

TensorFlow

PyTorch

Understanding NumPy helps us understand how modern AI systems process data efficiently.

1. Why NumPy? (Python List vs NumPy Array)

Python lists are flexible but not optimized for large numerical computations.

NumPy arrays are implemented in C, making them significantly faster.

Example: Performance Comparison

import numpy as np

import time

size = 1000000

list1 = list(range(size))

list2 = list(range(size))

start = time.time()

result = [x + y for x, y in zip(list1, list2)]

print("Python list time:", time.time() - start)

arr1 = np.arange(size)

arr2 = np.arange(size)

start = time.time()

result = arr1 + arr2

print("NumPy array time:", time.time() - start)

Explanation:

NumPy performs operations on entire arrays at once, called vectorization.

This removes the need for loops.

1. Creating NumPy Arrays (5 Minutes)

NumPy arrays are called ndarrays (N-dimensional arrays).

Basic Array Creation

import numpy as np

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

print(arr)

Creating Arrays of Zeros

zeros = np.zeros((3,3))

print(zeros)

Creates a 3×3 matrix of zeros.

Creating Arrays of Ones

ones = np.ones((2,4))

print(ones)

Creates a 2×4 matrix filled with ones.

Creating Ranges

numbers = np.arange(0,10,2)

print(numbers)

Output

[0 2 4 6 8]

1. Array Shapes and Dimensions

NumPy arrays can be multi-dimensional.

Example:

matrix = np.array([

[1,2,3],

[4,5,6],

[7,8,9]

])

print(matrix.shape)

Output

(3,3)

Meaning:

3 rows

3 columns

1. Vectorized Operations (5 Minutes)

NumPy allows mathematical operations without loops.

Example:

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

print(arr + 5)

print(arr * 2)

print(arr / 10)

Output

[15 25 35 45]

[20 40 60 80]

[1. 2. 3. 4.]

Explanation:

The operation is automatically applied to each element of the array.

This is called broadcasting.

1. Indexing and Slicing
   1D Array
   data = np.array([10,20,30,40,50])

print(data[0])

print(data[1:4])

Output

10

[20 30 40]

2D Array

matrix = np.array([

[1,2,3],

[4,5,6],

[7,8,9]

])

print(matrix[1,2])

Output

6

Extract Column

print(matrix[:,1])

Output

[2 5 8]

Explanation

: means all rows

1. Aggregation Functions

NumPy provides built-in functions for data analysis.

Example:

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

print("Mean:", np.mean(data))

print("Sum:", np.sum(data))

print("Max:", np.max(data))

print("Min:", np.min(data))

Output

Mean: 25

Sum: 100

Max: 40

Min: 10

1. Boolean Filtering (Very Useful for Data Cleaning)

Example dataset:

scores = np.array([55,78,90,34,88,67])

Find students who passed:

passed = scores[scores > 60]

print(passed)

Output

[78 90 88 67]

Explanation:

NumPy allows filtering data without loops.

This is heavily used in data preprocessing pipelines.

1. Real World Example – Web Response Time Analysis

Suppose we are monitoring API response times.

response_times = np.array([120,115,130,200,500,110,118])

Average response time

print("Average:", np.mean(response_times))

Find slow responses

slow = response_times[response_times > 200]

print("Slow responses:", slow)

Output

[500]

Normalize Data

Normalization is commonly used in machine learning pipelines.

normalized = (response_times - np.mean(response_times)) / np.std(response_times)

print("Normalized values:")

print(normalized)

1. Real-World Example – Netflix Recommendation System

Recommendation systems power platforms like Netflix.

Users and movies can be represented as vectors.

Movie List

movies = ["Interstellar", "Inception", "Titanic", "Avengers"]

User Ratings Matrix

ratings = np.array([

[5,4,1,1], # Alice

[4,5,1,1], # Bob

[1,1,5,4] # Charlie

])

print(ratings)

Rows represent users

Columns represent movies

1. Cosine Similarity

Recommendation systems often compute similarity between users.

Formula

similarity = (A · B) / (|A| |B|)

Implementing Similarity

from numpy.linalg import norm

alice = ratings[0]

bob = ratings[1]

charlie = ratings[2]

sim_alice_bob = np.dot(alice,bob) / (norm(alice)*norm(bob))

sim_alice_charlie = np.dot(alice,charlie) / (norm(alice)*norm(charlie))

print("Alice vs Bob:", sim_alice_bob)

print("Alice vs Charlie:", sim_alice_charlie)

Expected output

Alice vs Bob: ~0.98

Alice vs Charlie: ~0.32

Explanation

Alice and Bob have similar movie taste.

Charlie has different preferences.

1. Recommendation Logic

If Alice hasn't watched Avengers, we can recommend movies liked by similar users.

alice_ratings = np.array([5,4,1,0])

bob_ratings = ratings[1]

recommended_index = np.argmax(bob_ratings)

print("Recommended movie:", movies[recommended_index])

1. Key Takeaways

NumPy provides:

Fast array computations

Vectorized mathematical operations

Powerful data manipulation tools

It is the foundation of the Python data ecosystem.

Libraries built on NumPy include:

Pandas

TensorFlow

PyTorch

Scikit-learn

Understanding NumPy is a critical step toward machine learning and AI development.

Optional 1-Minute Closing

In modern systems:

• AI models

• Recommendation systems

• Image processing

• Fraud detection

all rely heavily on matrix operations.

NumPy makes these computations simple and extremely efficient.