Class 12 Computer Science (Python) – Chapter 5 Exception Handling & Generator Function

Chapter 5 – Exception Handling and Generator Function introduces two powerful features of Python programming: how to manage runtime errors gracefully and how to create custom iterators using generator functions. This chapter helps students write robust programs that can handle unexpected conditions and optimize performance using Python’s advanced control mechanisms.

What You Will Learn in Chapter 5

This chapter focuses on developing error-resistant programs and understanding how Python handles unexpected runtime situations. It also explains the concept of generator functions, which allow memory-efficient iteration using the yield keyword. These two topics are crucial for writing efficient and professional Python code.

Key Topics Covered

Exception Handling in Python

  • What is an Exception? An exception is an error that occurs during program execution and disrupts the normal flow of the program.
  • Common examples include ZeroDivisionError, ValueError, IndexError, FileNotFoundError, etc.

Handling Exceptions

  • try-except Block: Used to handle exceptions and prevent program crashes.
  • else Clause: Executes code if no exception occurs inside the try block.
  • finally Block: Executes regardless of whether an exception was raised or not—often used to close files or release resources.

Raising Exceptions

  • Python allows programmers to raise exceptions manually using the raise statement.
  • This is helpful when you want to validate inputs or enforce specific rules in your code.

Generator Functions

  • What is a Generator? A generator is a special type of iterator in Python that returns items one at a time using the yield keyword.
  • Generators help in writing memory-efficient code, especially for large data sets or infinite sequences.

Creating a Generator Function

  • Use the yield statement instead of return to produce a sequence of values over time.
  • Each time next() is called, the function resumes from where it left off.

Iterators vs Generators

  • Iterators implement the __iter__() and __next__() methods.
  • Generators automatically create iterator objects using yield, making them simpler and more readable.

Download Chapter 5 Solutions PDF – Exception Handling & Generator Function

Our downloadable PDF includes:

  • All NCERT intext and exercise solutions
  • Concept explanations with real-world examples
  • Python code snippets and outputs
  • Board exam-based short and long answers
  • Quick review notes for fast revision

Highlights of Our NCERT Solutions

  • Realistic Examples demonstrating exception handling and generators
  • Step-by-step solutions for better understanding
  • Well-commented Python programs
  • Flowcharts and diagrams to explain code flow and logic
  • Exam-oriented summaries for last-minute prep

Recommended Preparation Tips

  • Practice writing try-except blocks with multiple types of exceptions.
  • Understand how finally ensures code execution regardless of exceptions.
  • Create simple generator functions to generate series like Fibonacci or prime numbers.
  • Focus on real-world applications of exception handling like file reading and data validation.
  • Review differences between return and yield with examples.

Additional Study Resources

  • Flashcards: Python errors and exceptions
  • Quick reference sheet for generator vs iterator
  • CBSE Past Year Questions on exception handling
  • Online quizzes and MCQs on generator functions

Mastering Chapter 5 – Exception Handling & Generator Function

Understanding exception handling equips students to write safer and more reliable code. Generator functions, on the other hand, teach efficient ways to manage large datasets. Together, these concepts are vital for both board exams and real-world Python development.

Strong command over these topics will not only help you score high in your CBSE Class 12 Computer Science exam but also prepare you for advanced programming and entrance tests like CUET or coding interviews.

Class 12 Computer Science (Python) – Chapter 5 Exception Handling & Generator Function

NCERT Textbook Questions Solved – Class 12 Computer Science (Python)

Intext Questions

Question 1: What do you understand by an exception in Python?

Answer: An exception is a runtime error that occurs during the execution of a program. It interrupts the normal flow of the program and can be handled using error handling techniques like try-except blocks.

Question 2: What is the difference between syntax errors and exceptions?

Syntax ErrorsExceptions
Occur due to incorrect Python syntax.Occur during program execution.
Detected during code compilation.Detected during runtime.
Prevent the program from running.Program runs until the exception is raised.

Question 3: What is the purpose of the try and except statements in Python?

Answer: The try block is used to test a block of code for errors. The except block is used to catch and handle those errors gracefully without crashing the program.

Question 4: What is the use of finally block?

Answer: The finally block is used to define code that must be executed, regardless of whether an exception occurred or not. It is commonly used for cleanup actions like closing files.

Question 5: What does the raise statement do in Python?

Answer: The raise statement allows the programmer to manually trigger an exception. It is used to enforce certain conditions during program execution.

Question 6: What is a generator function in Python?

Answer: A generator function uses the yield keyword to return one value at a time from a sequence. It does not store all values in memory, making it more memory efficient for large datasets.

Exercise Questions

Question 1: What is an exception? How is it different from a syntax error?

Answer: An exception is a runtime error that occurs while the program is running, such as division by zero. A syntax error occurs when the Python syntax is not followed correctly. Syntax errors are detected before execution, while exceptions are detected during execution.

Question 2: What is the purpose of try and except in exception handling?

Answer: The try block allows the program to test a block of code for errors. The except block enables the program to catch and handle those errors without terminating the program abruptly.

Question 3: Write a program to demonstrate the use of multiple except blocks.

try:
    num1 = int(input(\"Enter a number: \"))
    num2 = int(input(\"Enter another number: \"))
    result = num1 / num2
    print(\"Result is:\", result)
except ValueError:
    print(\"Invalid input! Please enter integers only.\")
except ZeroDivisionError:
    print(\"Error! Cannot divide by zero.\")

Question 4: What will be the output of the following code?

try:
    x = 5 / 0
except:
    print(\"Exception occurred\")
finally:
    print(\"Finally block executed\")

Answer:
Exception occurred
Finally block executed

Question 5: What is the use of finally block in exception handling? Explain with an example.

Answer: The finally block is used to execute code regardless of whether an exception occurred or not. It is typically used for cleanup operations.

try:
    f = open(\"data.txt\", \"r\")
    content = f.read()
except FileNotFoundError:
    print(\"File not found.\")
finally:
    print(\"Closing file...\")
    f.close()

Question 6: Write a Python program to read two numbers and divide them. Handle possible exceptions.

try:
    a = int(input(\"Enter numerator: \"))
    b = int(input(\"Enter denominator: \"))
    print(\"Result =\", a / b)
except ZeroDivisionError:
    print(\"Cannot divide by zero.\")
except ValueError:
    print(\"Invalid input. Please enter numbers only.\")

Question 7: Write a generator function that generates even numbers up to n.

def even_generator(n):
    for i in range(2, n+1, 2):
        yield i
# Using the generator
for num in even_generator(10):
    print(num)

Question 8: How is a generator function different from a normal function?

Normal FunctionGenerator Function
Uses return to return a value and terminates.Uses yield to return values one at a time.
Returns only once.Can be resumed from the last yield point.
Consumes more memory.More memory-efficient.

Question 9: Write a generator to produce Fibonacci series up to n terms.

def fibonacci(n):
    a, b = 0, 1
    count = 0
    while count < n:
        yield a
        a, b = b, a + b
        count += 1
# Using the generator
for num in fibonacci(10):
    print(num)