Chapter 2: Python Programming for AI

Learning Outcomes

By the end of this chapter, students will be able to:

Explain the basics of Python programming language and write programs with basic concepts of tokens
Use selective and iterative statements effectively
Gain practical knowledge on how to use libraries efficiently

Python is a high-level, interpreted programming language known for its readability and simplicity. It supports multiple programming paradigms including procedural, object-oriented, and functional programming.

Key Features

Readability: Clean syntax with indentation-based blocks
Versatility: Used in web development, data science, AI, and more
Libraries: Extensive collection of libraries for various tasks
Cross-platform: Runs on Windows, macOS, Linux, and more

Level 1: Basics of Python Programming

Character Sets and Tokens

Character Set: Letters (A-Z, a-z), Digits (0-9), Special symbols (+, -, *, /, etc.)
Tokens: The smallest unit of a program
- Keywords: Reserved words (if, else, for, while, def, class, etc.)
- Identifiers: Names given to variables, functions, classes
- Literals: Constant values (numbers, strings, booleans)
- Operators: Arithmetic, relational, logical, assignment
- Punctuators: Brackets, commas, colons, semicolons

Modes of Python

Interactive Mode: Execute commands one at a time in Python shell
Script Mode: Write complete programs in .py files and execute

Operators

# Arithmetic Operators
a = 10
b = 3
print(a + b)   # Addition: 13
print(a - b)   # Subtraction: 7
print(a * b)   # Multiplication: 30
print(a / b)   # Division: 3.333...
print(a // b)  # Floor Division: 3
print(a % b)   # Modulus: 1
print(a ** b)  # Exponentiation: 1000

# Relational Operators
print(a > b)   # Greater than: True
print(a < b)   # Less than: False
print(a == b)  # Equal to: False
print(a != b)  # Not equal to: True

# Logical Operators
x = True
y = False
print(x and y)  # False
print(x or y)   # True
print(not x)    # False

Data Types

# Integer
age = 25

# Float
price = 19.99

# String
name = "Python"

# Boolean
is_active = True

# List (mutable sequence)
fruits = ["apple", "banana", "cherry"]

# Tuple (immutable sequence)
coordinates = (10, 20)

# Dictionary (key-value pairs)
student = {"name": "Alice", "age": 17}

# Set (unique elements)
unique_numbers = {1, 2, 3, 4, 5}

Control Statements

# Conditional Statements
score = 85
if score >= 90:
    print("Grade: A")
elif score >= 80:
    print("Grade: B")
elif score >= 70:
    print("Grade: C")
else:
    print("Grade: F")

# For Loop
for i in range(5):
    print(i)

# While Loop
count = 0
while count < 5:
    print(count)
    count += 1

# Break and Continue
for num in range(10):
    if num == 5:
        break  # Exit loop
    if num == 2:
        continue  # Skip to next iteration
    print(num)

Level 2: CSV Files and Libraries

Working with CSV Files

import csv

# Reading CSV file
with open('data.csv', 'r') as file:
    reader = csv.reader(file)
    for row in reader:
        print(row)

# Writing to CSV file
with open('output.csv', 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerow(['Name', 'Age', 'Grade'])
    writer.writerow(['Alice', 17, 'A'])
    writer.writerow(['Bob', 18, 'B'])

NumPy Library

NumPy is a library for numerical computing with support for arrays and matrices.

import numpy as np

# Creating arrays
arr1 = np.array([1, 2, 3, 4, 5])
arr2 = np.array([[1, 2, 3], [4, 5, 6]])

# Array operations
print(arr1 + 10)      # Add 10 to each element
print(arr1 * 2)       # Multiply each element by 2
print(arr1.mean())    # Calculate mean
print(arr1.sum())     # Calculate sum

# Matrix operations
matrix = np.array([[1, 2], [3, 4]])
print(np.transpose(matrix))  # Transpose
print(np.linalg.det(matrix)) # Determinant

Pandas Library

Pandas is a library for data manipulation and analysis.

import pandas as pd

# Creating a DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [17, 18, 16],
    'Grade': ['A', 'B', 'A']
}
df = pd.DataFrame(data)

# Reading CSV file
df = pd.read_csv('students.csv')

# Basic operations
print(df.head())        # First 5 rows
print(df.describe())    # Statistical summary
print(df['Age'].mean()) # Mean of Age column

# Filtering data
filtered = df[df['Age'] > 16]

# Sorting data
sorted_df = df.sort_values('Age')

Scikit-learn Library

Scikit-learn is a library for machine learning.

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import numpy as np

# Sample data
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 6, 8, 10])

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Create and train model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

Practical Activities

Activity 1: Python Basics (Level 1)

Write programs using operators, data types, and control statements:

Program 1: Calculator

def calculator():
    num1 = float(input("Enter first number: "))
    operator = input("Enter operator (+, -, *, /): ")
    num2 = float(input("Enter second number: "))
    
    if operator == '+':
        result = num1 + num2
    elif operator == '-':
        result = num1 - num2
    elif operator == '*':
        result = num1 * num2
    elif operator == '/':
        result = num1 / num2 if num2 != 0 else "Error: Division by zero"
    else:
        result = "Invalid operator"
    
    print(f"Result: {result}")

calculator()

Program 2: Prime Number Checker

def is_prime(n):
    if n < 2:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

number = int(input("Enter a number: "))
if is_prime(number):
    print(f"{number} is a prime number")
else:
    print(f"{number} is not a prime number")

Program 3: Factorial Calculator

def factorial(n):
    if n == 0 or n == 1:
        return 1
    else:
        result = 1
        for i in range(2, n + 1):
            result *= i
        return result

num = int(input("Enter a number: "))
print(f"Factorial of {num} is {factorial(num)}")

Activity 2: Libraries in AI (Level 2)

Write programs using NumPy, Pandas, and Scikit-learn:

Program 1: NumPy Array Operations

import numpy as np

# Create arrays
arr = np.array([10, 20, 30, 40, 50])
matrix = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Array statistics
print(f"Mean: {arr.mean()}")
print(f"Standard Deviation: {arr.std()}")
print(f"Sum: {arr.sum()}")

# Matrix operations
print(f"Matrix Transpose:\n{matrix.T}")
print(f"Matrix Sum: {matrix.sum()}")

Program 2: Pandas Data Analysis

import pandas as pd

# Create DataFrame
data = {
    'Student': ['Alice', 'Bob', 'Charlie', 'Diana', 'Eve'],
    'Math': [85, 90, 78, 92, 88],
    'Science': [88, 85, 82, 95, 90],
    'English': [92, 80, 85, 88, 95]
}
df = pd.DataFrame(data)

# Analysis
print("First 3 rows:")
print(df.head(3))

print("\nStatistical Summary:")
print(df.describe())

print(f"\nAverage Math Score: {df['Math'].mean()}")
print(f"Highest Science Score: {df['Science'].max()}")

Competency-Based Questions

Example Questions

Write a Python program to find the factorial of a number. (2 marks)
Explain the difference between lists and tuples in Python. (3 marks)
Write a function to check if a number is prime. (4 marks)
Use NumPy to create a 3x3 matrix and perform matrix multiplication. (5 marks)
Explain the role of Pandas in data analysis for AI projects. (6 marks)

Answers to Example Questions

Answer:

def factorial(n):
    if n == 0 or n == 1:
        return 1
    result = 1
    for i in range(2, n + 1):
        result *= i
    return result

num = int(input("Enter a number: "))
print(f"Factorial of {num} is {factorial(num)}")

Answer:

Feature	List	Tuple
Mutability	Mutable (can be changed)	Immutable (cannot be changed)
Syntax	Square brackets `[]`	Parentheses `()`
Performance	Slower	Faster
Use case	When data needs modification	When data should remain constant
Example	`[1, 2, 3]`	`(1, 2, 3)`

Answer:

def is_prime(n):
    if n < 2:
        return False
    for i in range(2, int(n ** 0.5) + 1):
        if n % i == 0:
            return False
    return True

number = int(input("Enter a number: "))
if is_prime(number):
    print(f"{number} is prime")
else:
    print(f"{number} is not prime")

Answer:

import numpy as np

# Create two 3x3 matrices
A = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
B = np.array([[9, 8, 7], [6, 5, 4], [3, 2, 1]])

# Matrix multiplication
C = np.dot(A, B)  # or A @ B
print("Matrix A:\n", A)
print("Matrix B:\n", B)
print("A × B:\n", C)

Answer: Pandas plays a crucial role in AI data analysis:
- Data Loading: Reads various formats (CSV, Excel, SQL, JSON)
- Data Cleaning: Handles missing values, duplicates, and outliers
- Data Transformation: Reshaping, merging, and aggregating data
- Exploration: Statistical summaries and data inspection with describe(), info()
- Feature Engineering: Creating new features for ML models
- Integration: Works seamlessly with NumPy, Scikit-learn, and visualization libraries

Official Sample Paper Questions

What is the purpose of the NumPy library in Python? (2 marks)
Write a Python program to sort a list of numbers in ascending order. (3 marks)
Explain the difference between a list and a dictionary in Python. (4 marks)
Use Pandas to read a CSV file and display the first five rows. (5 marks)
Discuss the importance of Python in the field of artificial intelligence. (6 marks)

Answers to Official Sample Paper Questions

Answer: NumPy provides support for large, multi-dimensional arrays and matrices, along with mathematical functions to operate on them efficiently. It forms the foundation for scientific computing and AI in Python.

Answer:

numbers = [64, 34, 25, 12, 22, 11, 90]
numbers.sort()  # In-place sorting
print("Sorted list:", numbers)
# Or using sorted(): sorted_numbers = sorted(numbers)

Answer:

Feature	List	Dictionary
Structure	Ordered sequence	Key-value pairs
Access	By index	By key
Syntax	`[1, 2, 3]`	`{'a': 1, 'b': 2}`
Order	Maintains insertion order	Maintains insertion order (Python 3.7+)
Use	Sequential data	Associated data mapping

Answer:

import pandas as pd

# Read CSV file
df = pd.read_csv('data.csv')

# Display first five rows
print(df.head())

Answer: Python is crucial for AI because:
- Simple Syntax: Easy to learn and read, faster prototyping
- Extensive Libraries: TensorFlow, PyTorch, Scikit-learn, Keras
- Data Handling: NumPy, Pandas for efficient data manipulation
- Community Support: Large community, extensive documentation
- Integration: Interfaces with C/C++ for performance-critical code
- Versatility: Suitable for research, development, and production

Practice Questions

Multiple Choice Questions

Which of the following is a mutable data type in Python? a) int b) float c) list d) tuple
What is the output of the following code?
```
print("Hello" + "World")
```
a) HelloWorld b) Hello World c) Error d) None
Which library is used for numerical operations in Python? a) NumPy b) Pandas c) Matplotlib d) Scikit-learn
What does the // operator do in Python? a) Regular division b) Floor division c) Modulus d) Exponentiation
Which keyword is used to define a function in Python? a) function b) func c) def d) define

Short Answer Questions

Define a variable in Python and provide an example.
Explain the difference between a for loop and a while loop.
What is the purpose of the Pandas library in Python?
Write a Python program to calculate the sum of even numbers from 1 to 100.

Long Answer Questions

Discuss the role of Python in artificial intelligence and machine learning.
Explain the difference between lists and tuples in Python with examples.
Write a Python program to read a CSV file and calculate the average of a specific column.

Summary

Key Points

Python is a versatile programming language with simple syntax
It supports multiple programming paradigms and has extensive libraries
Python is widely used in AI for data processing, model training, and deployment
Key libraries include NumPy for numerical operations, Pandas for data manipulation, and Scikit-learn for machine learning

Important Terminologies

Token: Smallest unit of a program (keywords, identifiers, literals, operators)
Variable: Container for storing data values
Data Type: Classification of data (int, float, str, list, dict)
Control Statement: Statements that control the flow of execution
NumPy: Library for numerical computing with arrays
Pandas: Library for data manipulation and analysis
Scikit-learn: Library for machine learning algorithms

Solutions to Practice Questions

Multiple Choice Answers

c) list
a) HelloWorld
a) NumPy
b) Floor division
c) def

Short Answer Model Answers

A variable is a named storage location for data values. Example: x = 10 assigns the value 10 to variable x.
A for loop iterates over a sequence (like a list or range), while a while loop runs as long as a condition is true.
Pandas is used for data manipulation and analysis, providing data structures like DataFrames for handling tabular data.

sum = 0
for i in range(1, 101):
    if i % 2 == 0:
        sum += i
print(sum)  # Output: 2550

Long Answer Model Answers

Python’s simplicity, readability, and extensive libraries make it ideal for AI development. It enables rapid prototyping and deployment of machine learning models with libraries like TensorFlow, PyTorch, and Scikit-learn.
Lists are mutable (can be changed after creation) and use square brackets [], while tuples are immutable (cannot be changed) and use parentheses (). Lists are better for collections that need modification; tuples are better for fixed collections.

import pandas as pd

df = pd.read_csv('data.csv')
average = df['column_name'].mean()
print(f"Average: {average}")

IBM Skills Build Integration

Complete the IBM Skills Build - Python for Data Science course to:

Gain hands-on experience with Python programming
Learn to use NumPy, Pandas, and data visualization libraries
Practice with real-world datasets
Earn a certification to add to your portfolio

References

CBSE Artificial Intelligence Curriculum for Class XI (2025-2026)
IBM Skills Build - Python for Data Science
Python Official Documentation (python.org)
NumPy, Pandas, and Scikit-learn Documentation

Artificial Intelligence grade 11 for CBSE

Chapter 2: Python Programming for AI

Learning Outcomes

Theory

Python Basics

Key Features

Level 1: Basics of Python Programming

Character Sets and Tokens

Modes of Python

Operators

Data Types

Control Statements

Level 2: CSV Files and Libraries

Working with CSV Files

NumPy Library

Pandas Library

Scikit-learn Library

Practical Activities

Activity 1: Python Basics (Level 1)

Activity 2: Libraries in AI (Level 2)

Competency-Based Questions

Example Questions

Answers to Example Questions

Official Sample Paper Questions

Answers to Official Sample Paper Questions

Practice Questions

Multiple Choice Questions

Short Answer Questions

Long Answer Questions

Summary

Key Points

Important Terminologies

Solutions to Practice Questions

Multiple Choice Answers

Short Answer Model Answers

Long Answer Model Answers

IBM Skills Build Integration

References

Keyboard shortcuts

Artificial Intelligence grade 11 for CBSE