Easy2Siksha

GNDU Question Paper-2022

BCA 3

Semester

INTRODUCTION TO PYTHON PROGRAMMING

Time Allowed: Three Hours Maximum Marks: 75

Note: Attempt Five questions in all, selecting at least One question from each section. The

Fifth question may be attempted from any section. All questions carry equal marks.

SECTION-A

1.(a) What are the technical strengths of Python. Write a simple Python program that

takes numeric input from a user.

(b) Explain different operators and illustrate how tuples are immutable.

2.(a) How the list is appended and also explain different operators used on the list?

(b) How to create a dictionary using a constructor? Illustrate to iterate over a dictionary.

SECTION-B

3.(a) Explain While and For loops in Python.

(b) Write a program to calculate the factorial of a number using recursion

4.(a) Differentiate packages and modules.

(b) What are the different methods of importing the external Python module?

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SECTION-C

5.(a) How to open and write into Python Files? Explain with the program.

(b) What is the significance of Exception Handling? Write a program to handle multiple

exceptions.

6. (a) How to overload an Operator in Python? Define the use of Destructor Program.

(b) Discuss the concept of overriding the methods with the program.

SECTION-D

7.(a) How is table created using Python? Illustrate how to search a table in Python? 8

(b) Explain data modelling with examples.

8. Explain the following commands with examples:

(a) Modify Command

Easy2Siksha

GNDU Answer Paper-2022

BCA 3

Semester

INTRODUCTION TO PYTHON PROGRAMMING

Time Allowed: Three Hours Maximum Marks: 75

Note: Attempt Five questions in all, selecting at least One question from each section. The

Fifth question may be attempted from any section. All questions carry equal marks.

SECTION-A

1.(a) What are the technical strengths of Python. Write a simple Python program that

takes numeric input from a user.

(b) Explain different operators and illustrate how tuples are immutable.

Ans: (a).Simplified Version of the Question:

"Why is Python so good at solving problems, and can you show me a simple example of

Python asking for a number from a person?"

Why Python is Strong and Special (Like a Superhero)

Imagine Python is like a helpful friend who always gets the job done quickly and makes

things easy for everyone. Let’s explore why Python is so special, comparing it to real-life

scenarios:

1. Python is Easy to Read and Write (Like a Storybook)

Python uses simple and clear words, just like telling a story. You don’t need to memorize

strange symbols or complex rules. Here’s an example:

• Other languages are like reading old, hard-to-understand handwriting.

• Python is like a clean, printed book written for kids—it’s neat and friendly!

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2. Python Has a Big Toolbox (Like a Magical Bag of Tools)

Python comes with everything you need for most tasks. Imagine a magical bag where,

whenever you need something, it’s already inside. This "toolbox" of Python includes:

• Tools for math (like calculators).

• Tools for games (like building Minecraft).

• Tools for science (like space rockets).

For example:

3. Python Can Talk to Everyone (Like a Translator)

Python can work with many different systems, apps, or devices. Think of Python as a

translator who helps people speaking different languages talk to each other. It can:

• Connect to websites.

• Manage data from databases.

• Work with apps on your phone.

4. Python Learns New Tricks Easily (Like a Quick Learner)

Python can adapt and grow. New technologies? No problem! There are always libraries

(special add-ons) to help Python learn new things.

For example:

• Want to do Artificial Intelligence? Use a library called TensorFlow.

• Want to create beautiful charts? Use Matplotlib.

5. Python is Popular and Has Many Friends (A Huge Community)

Python is used by millions of people, so there’s always someone to help you. Think of it as

joining a club where everyone shares tips, tricks, and solutions.

6. Python is Free and Open (Like a Public Library)

Python is completely free to use, just like borrowing books from a library. You don’t have to

pay anything to start coding.

Simple Python Program (Like a Fun Game)

Let’s now look at a small program that asks for a number from the user and does something

fun. It’s like a mini-game you can play on your computer.

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The Task: Ask for a Number and Double It

Imagine you’re asking your friend:

1. “Hey, give me a number.”

2. You then double it and say, “Your number doubled is: [answer].”

Here’s how we do it in Python:

Breaking it Down Like a Story

1. Input: You ask the user to give a number, just like asking a friend to tell you their

favorite number.

2. Convert: The computer needs to know it’s a number, so we convert the input into a

numeric format.

3. Calculate: The program doubles the number (like math magic).

4. Output: Finally, it shows the result on the screen.

Why Python Makes This So Simple

• No extra steps. In other languages, you might need more lines of code.

• Human-friendly words. "Input," "print," and "float" are easy to understand.

• Works everywhere. This small code can run on any computer.

Other Cool Python Features (Bonus Powers)

1. It Works Everywhere: From big companies like Google to small robots, Python runs

everywhere.

2. It Saves Time: Python has tools to solve problems quickly, so you don’t have to

invent everything yourself.

3. It’s Future-Proof: Python is growing, so learning it now is like learning a skill that will

always be useful.

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Conclusion (Wrapping Up the Story)

Python is like a helpful superhero who:

1. Makes programming easy (like reading a storybook).

2. Gives you all the tools you need (like a magical bag).

3. Works with everyone and everything (like a friendly translator).

And just like that, you now know why Python is special and how to write a small program

with it! So, whenever you think about Python, remember this simple story of how it solves

problems and makes coding fun.

(b) Explain different operators and illustrate how tuples are immutable.

Ans: The Story of Operators and the Unshakeable Tuples

Once upon a time in a magical land called Pythonia, there were two kinds of citizens:

Operators and Tuples. Operators were like busy helpers, always ready to perform tasks like

adding, subtracting, or comparing things. Tuples, on the other hand, were calm, composed

individuals who never changed once they were born. This is the tale of how these two

worked together and why Tuples stayed unshakably the same.

Meet the Operators

Operators in Pythonia are like tools or magic wands that help solve problems. Let’s meet

them and see what they do!

1. Arithmetic Operators (The Math Wizards)

These operators are great at math. Here’s what they do:

• Addition (+): Adds two numbers.

Imagine Tim the Wizard adding apples: 2 + 3 = 5.

• Subtraction (-): Subtracts one number from another.

Tim subtracts oranges: 5 - 2 = 3.

• Multiplication (*): Multiplies numbers.

Tim multiplies grapes: 3 * 4 = 12.

• Division (/): Divides numbers (gives a floating-point result).

Tim divides candies: 10 / 2 = 5.0.

• Floor Division (//): Divides but gives the whole number only (integer part).

Tim divides cakes equally: 11 // 3 = 3.

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• Modulus (%): Finds the remainder when dividing.

Tim checks leftovers: 11 % 3 = 2.

• Exponentiation (**): Raises one number to the power of another.

Tim powers up: 2 ** 3 = 8.

2. Comparison Operators (The Judges)

These operators decide if things are greater, lesser, or equal.

• Equal to (==): Checks if two values are the same.

Is Tim’s treasure chest equal to Sam’s? 5 == 5 → True!

• Not equal to (!=): Checks if two values are different.

Is Tim’s chest different from Sam’s? 5 != 4 → True!

• Greater than (>): Checks if one value is larger.

Is Tim’s gold bigger? 7 > 3 → True!

• Less than (<): Checks if one value is smaller.

Is Tim’s ruby smaller? 2 < 10 → True!

3. Logical Operators (The Thinkers)

Logical operators combine ideas to make decisions.

• AND (and): Both conditions must be true.

Does Tim have gold and rubies?

• OR (or): At least one condition must be true.

Does Tim have gold or diamonds?

Python

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• NOT (not): Reverses the condition.

Does Tim not have diamonds?

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4. Assignment Operators (The Record Keepers)

These help store and update values in variables.

• =: Assigns a value.

Tim’s wallet gets filled: wallet = 50.

• +=: Adds and updates.

Tim earns more: wallet += 20 → Wallet now has 70.

• -=: Subtracts and updates.

Tim spends: wallet -= 30 → Wallet now has 40.

Tuples: The Unshakeable Heroes

Now let’s talk about Tuples. They’re a group of items bundled together, just like friends

holding hands in a circle. What makes them special is this:

Once a Tuple is created, it can’t be changed.

What’s a Tuple?

• A Tuple is created by placing items inside parentheses ().

python

Immutability (Why Tuples Can’t Change)

Imagine a Tuple as a group of knights swearing an unbreakable vow.

• You can’t add new members to a Tuple.

• You can’t remove members.

• You can’t even change existing members.

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Why is this Useful?

Because Tuples are immutable, they’re reliable. If you pass them around, you’re sure they

won’t get altered accidentally.

How Tuples Work with Operators

Although Tuples are unchangeable, you can still use operators with them. Here’s how:

1. Accessing Elements

You can use indexing to see what’s inside a Tuple.

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2. Combining Tuples

You can join Tuples together with +.

Python

3. Repeating Tuples

You can multiply Tuples with *.

Python

4. Membership Testing

You can check if an item is inside a Tuple using in.

Python

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Illustrating Immutability

Here’s why Tuples can’t be changed:

Trying to Change a Tuple

Python

But you can create a new Tuple instead:

Python

Why Pythonia Loves Tuples

1. Speed: Tuples are faster than lists.

2. Safety: Because they can’t change, they’re great for storing constants.

3. Convenience: They can be used as keys in dictionaries, unlike lists.

And so, in the magical land of Pythonia, Operators kept working their magic, and Tuples

stayed steadfast and unchanging, ensuring everything remained predictable and reliable.

The two lived happily ever after, helping programmers build great things!

2.(a) How the list is appended and also explain different operators used on the list?

(b) How to create a dictionary using a constructor? Illustrate to iterate over a dictionary.

Ans:(a). The List Adventure: How Lists Grow and What They Can Do!

Imagine a list is like a magical backpack 󼩷󼩸󼩹󼩺󼩻 that can hold all kinds of items—books, apples,

your favorite toys, or even another smaller backpack! You can add things to it, take things

out, or rearrange them however you like. This magical backpack belongs to a wizard named

Codey, who uses spells (called operators) to control the backpack. Let’s follow Codey and

see how he works with his list!

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Chapter 1: Adding Items to the List (Appended Magic!)

One day, Codey finds a shiny ruby gem 󹨿󹩀󹩁󹩂󹩃󹩄 and wants to add it to his backpack. Here’s how

he does it:

1. Using the append() spell:

Codey waves his wand and chants:

Python

Instantly, the ruby is added to the end of the backpack. Now the list looks like this:

Python

The append() spell is simple—it always puts the new item at the very back of the backpack.

2. Using the extend() spell:

Next, Codey finds three more gems (emerald, sapphire, and diamond) and wants to

add them all at once.

Python

The backpack now holds:

Python

The extend() spell lets Codey add multiple items to the backpack in one go. It's like dumping

a whole pouch of treasures inside!

3. Using the insert() spell:

Suddenly, Codey remembers he wants to keep the emerald at the front! So he uses

another chant:

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Python

The insert() spell lets Codey place an item at any position. The first number (0) tells where to

place it, and the second ('emerald') is the item to be added. Now the list looks like this:

Python

Chapter 2: Different Operators and How They Work

Codey’s backpack can do so much more than just hold items. Let’s see how he uses

operators (magic symbols) to play with the list.

1. Checking What's Inside (in Operator)

Codey wants to know if the sapphire is still in the backpack. He uses the in operator to find

out:

Python

2. Joining Two Backpacks Together (+ Operator)

One day, Codey meets his friend, Ella, who also has a magical backpack with items:

Python

They decide to combine their treasures for a big adventure:

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Python

The new list becomes:

Python

3. Multiplying the Fun (* Operator)

Codey wants to pack extra apples for the journey. Instead of adding them one by one, he

duplicates them using the * operator:

Python

This creates:

Python

4. Counting Items (len() Spell)

To check how much his backpack holds, Codey uses the len() spell:

Python

It tells him the total number of items. Handy for packing light!

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5. Removing Items (remove() and pop() Spells)

Oops! Codey accidentally packed a rotten apple 󷋛󷋜. He uses the remove() spell:

Python

If Codey wants to take out the last item, he uses the pop() spell:

Python

This removes the last item and shows what it was.

6. Sorting Items (sort() Spell)

Codey decides to arrange his gems alphabetically:

Python

The backpack now looks tidy:

Python

Chapter 3: Special Backpack Tricks (Slicing and Indexing)

Codey’s backpack is so magical that he can peek inside or take out a part of it without

opening it fully!

1. Slicing

Codey wants to see just the first three items. He uses slicing:

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Python

This returns:

Python

2. Indexing

If Codey wants to know where the ruby is, he asks:

Python

It tells him the position of the ruby in the backpack.

Chapter 4: Lists Within Lists (The Nested Backpack)

Sometimes, Codey packs a smaller magical backpack inside his main one! This is called

nesting:

Python

Codey can reach into the smaller backpack like this:

Python

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This returns 'ruby', the second item in the smaller backpack.

Chapter 5: Codey’s Backpack Adventure Summary

Here’s a quick summary of everything Codey learned about lists:

Spell/Operator

What It Does

Example

append()

Adds one item

backpack.append('item')

extend()

Adds multiple items

backpack.extend(['a', 'b'])

insert()

Adds at a position

backpack.insert(1, 'item')

Combines lists

list1 + list2

Duplicates items

['a'] * 3

remove()

Removes an item

backpack.remove('item')

pop()

Removes last item

backpack.pop()

sort()

Sorts the list

backpack.sort()

len()

Counts items

len(backpack)

Checks for item

'item' in backpack

Slicing

Takes part of list

backpack[0:2]

Nesting

Lists inside lists

list[1][0]

Final Words from Codey

"Lists are like backpacks, and spells (operators) are the tools to control them. Use them

wisely on your coding adventures!"

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(b) How to create a dictionary using a constructor? Illustrate to iterate over a dictionary.

Ans: The Tale of the Dictionary Constructor and the Treasure Hunt

Once upon a time in the magical world of Pythonia, there lived a young programmer named

Alex. Alex loved solving puzzles and making lists of things—like favorite foods, books, and

games. One day, Alex discovered a special tool in the Pythonia world: the Dictionary

Constructor! This tool could magically create dictionaries to organize all sorts of things. But

there was a twist—Alex had to learn how to use this tool and explore the treasure hidden

inside a dictionary. Let’s join Alex on this fun journey!

What is a Dictionary in Python?

A dictionary in Python is like a magical treasure chest. It helps you store data in pairs: a key

(like the name of an item) and its value (what the item is). For example:

• Key: "Favorite Food"

• Value: "Pizza"

Together, they become a pair: {"Favorite Food": "Pizza"}. A dictionary can hold many such

treasures (key-value pairs)!

How to Create a Dictionary Using a Constructor

Alex learned that there were different ways to create a dictionary. One of the coolest ways

was using the dict() constructor. Here's how it works:

The dict() constructor is like a recipe. You tell it what key-value pairs you want, and it

creates the dictionary for you. Here's an example:

Python

When Alex ran this code, the treasure chest appeared:

Python

The keys (Food, Drink, Dessert) describe the treasures, and the values (Pizza, Coke, Ice

Cream) are the treasures themselves.

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Iterating Over a Dictionary: The Treasure Hunt

Now Alex wanted to explore the dictionary and look at each treasure inside. In Python,

exploring (or iterating over) a dictionary is super fun and easy. You can use a for loop! Here

are three ways Alex learned to do this:

1. Iterating Over Keys

Alex started by looking at the labels (keys) of the treasures:

Python

This revealed:

2. Iterating Over Values

Next, Alex checked the actual treasures (values):

Python

This revealed:

3. Iterating Over Both Keys and Values

Finally, Alex wanted to see the full picture—the key and its corresponding value:

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Python

This revealed:

Bringing It All Together: The Big Dictionary Adventure

To make the story even more magical, Alex decided to create a dictionary for an entire day's

adventure. Here’s how:

Python

When Alex ran this code, the adventure plan looked like this:

The Moral of the Story

• Creating dictionaries with dict(): It’s simple and looks clean.

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• Iterating over dictionaries: It’s like a treasure hunt where you can explore keys,

values, or both!

By mastering this skill, Alex could organize any adventure—or anything else—effortlessly.

A Quick Recap

1. Create a dictionary using dict():

o Example: dict(Key1="Value1", Key2="Value2")

2. Explore a dictionary:

o Keys: for key in my_dict:

o Values: for value in my_dict.values():

o Keys and Values: for key, value in my_dict.items():

Your Turn!

Let’s say you want to create a dictionary of your favorite books and their authors. Use the

dict() constructor and iterate over it to display the books and authors.

Python

SECTION-B

3.(a) Explain While and For loops in Python.

(b) Write a program to calculate the factorial of a number using recursion

Ans:(a).Meet Whiley and Forry, the Python Loops!

Once upon a time in the land of Pythonia, two hardworking friends, Whiley and Forry, were

known for their amazing ability to repeat tasks over and over again. They were loops, and

their job was to help programmers automate repetitive tasks.

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Whiley the While Loop

Whiley was a bit of a dreamer. He loved working as long as his conditions were true. Here's

how he worked:

1. Whiley would ask a question: "Should I keep working?"

2. If the answer was Yes (True), he would do the task assigned to him.

3. After completing one round of work, he would ask the same question again.

4. This would continue until the answer became No (False).

Example of Whiley in action:

Imagine Whiley is a robot helping a kid named Tim eat candies. Tim can eat candies only if

the jar is not empty. Here's Whiley's plan:

Python

• How it works:

Whiley keeps checking if there are candies in the jar (candies > 0).

As long as there are candies, Tim keeps eating one.

When the candies are gone, Whiley stops working.

Forry the For Loop

Forry was more organized than Whiley. Instead of checking conditions repeatedly, Forry

liked to work with a specific list or range of items. His rule was simple:

1. Forry would take each item from a collection (like a list, range, or string) one by one.

2. For every item, he would perform a task.

3. When he finished all the items, he stopped.

Example of Forry in action:

Now imagine Forry is helping Tim sort through his toy collection. Here's Forry's plan:

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Python

• How it works:

Forry takes the first toy ("car") and says, “Tim is playing with his car.”

Then he moves to the second toy ("doll") and does the same.

This continues until all toys are sorted.

Comparing Whiley and Forry

Feature

Whiley (While Loop)

Forry (For Loop)

Focus

Repeats tasks as long as a condition is

true.

Works through a set of items or

numbers.

Flexibility

Good for unknown or changing numbers

of repetitions.

Best when the number of

repetitions is known.

Example

task

"Eat candies until the jar is empty."

"Play with every toy in the box."

When to Use Whiley and Forry

1. Use Whiley (while loop) when:

o You’re unsure how many times a task needs to repeat.

o A condition needs to be checked after every iteration.

o Example: Waiting for a user to guess a number correctly.

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Python

2. Use Forry (for loop) when:

o You know exactly how many items to iterate through.

o You’re working with lists, ranges, or sequences.

o Example: Printing the first 10 numbers.

Python

The Loops and Breaks Club

Both Whiley and Forry sometimes need to take a break during their work. They have a

special tool called break, which stops their work immediately. They also have another tool,

continue, which skips one task but doesn’t stop completely.

Break in Action

Imagine Tim gets tired and wants to stop eating candies early. Whiley would use break:

Python

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Continue in Action

Now imagine Tim skips eating candies with odd numbers. Whiley would use continue:

Python

Special Skills: Nested Loops

Both Whiley and Forry can work together, like a team, in nested loops. For example, let’s say

Tim has a box of toys and each toy has multiple parts. Forry can go through each toy, and for

each toy, Whiley can help sort the parts:

Python

Fun Analogies

1. Whiley = Repetitive Questioning

Like someone saying, “Are we there yet?” until the destination is reached.

2. Forry = Checking Items Off a List

Like ticking items off a shopping list until the cart is full.

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(b) Write a program to calculate the factorial of a number using recursion

Ans: A Fun Journey Into Factorial Land

Imagine you're going on a little adventure to a magical land where numbers behave in

interesting ways. Today, we’re going to visit Factorial Land, where numbers love to multiply

and grow bigger and bigger in a unique way. In this magical land, there’s a fascinating rule:

Every number loves to grow by multiplying itself with all the numbers before it! Let's take a

walk through this fun journey and learn how to calculate the factorial of a number using a

magic trick called recursion.

What is a Factorial?

To start, let’s break down the rule of factorials. If you're in Factorial Land and you meet a

number, let's say 5, it will tell you its factorial. The factorial of 5 is simply 5 × 4 × 3 × 2 × 1.

It’s as if the number is multiplying itself with all the numbers before it until it reaches 1.

For example:

• 5! = 5 × 4 × 3 × 2 × 1 = 120

• 4! = 4 × 3 × 2 × 1 = 24

• 3! = 3 × 2 × 1 = 6

• 2! = 2 × 1 = 2

• 1! = 1 (1 is special; it stays as 1)

And guess what? There’s even a rule for 0!. You might be wondering, "What’s the factorial

of 0?" Well, in Factorial Land, they say that 0! = 1, which is a little surprise but is the way it

works in mathematics.

Why Use Recursion?

Now that we know what a factorial is, let’s talk about how we can calculate it using

recursion. Recursion is like a magical mirror that can reflect itself back! It’s like when you

look into a mirror and you see another mirror reflecting your image over and over again, but

with a twist. In programming, recursion happens when a function calls itself to solve smaller

pieces of a problem until it reaches a simple base case.

Think about it like this:

• If you want to calculate the factorial of a number, say 5, you can think of it like this:

“What is the factorial of 5?”

• You can break it down into: “What is the factorial of 4?” (And you keep asking

smaller questions until you get down to 1, which is simple and known).

Once we reach that base case (which is the smallest possible problem), we can start putting

everything together, just like solving a puzzle!

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Breaking Down the Recursion Process

Let’s think of a fun way to represent this. Imagine you are a wizard in Factorial Land, and

you want to calculate the factorial of a number using a magical spell. Here’s how it goes:

1. Start with the number you want to calculate (let's say it’s 5).

2. Ask yourself: What is the factorial of this number?

o For 5, you ask: “What is 5 × factorial of 4?”

o Now, you go on an adventure to find 4!.

3. Repeat the process for 4: What’s the factorial of 4? That’s 4 × factorial of 3, and so

on.

4. When you get to 1, you know that 1! = 1, which is where your magical journey ends.

You can now travel back through your recursive calls, multiplying the numbers

together.

It’s like going down a staircase step by step, then coming back up, multiplying the steps as

you go!

Let’s Create the Magic Code (Recursion in Python)

Now, let's use the Python programming language to perform this magical journey of finding

the factorial using recursion. Here's the fun spell (program) that does it!

Python

How Does the Code Work?

Let’s break down the code step by step:

1. Defining the factorial function:

o We start by defining a function called factorial. It takes a number n as an

input and returns the factorial of that number.

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2. The base case:

o Every recursion needs a stopping point, otherwise, it would go on forever.

The base case is when the number n is 0 or 1. In this case, the function simply

returns 1 because, by definition, 0! = 1 and 1! = 1.

3. The recursive case:

o If the number n is greater than 1, the function calls itself with n-1. This is the

key to recursion! The function keeps calling itself with smaller and smaller

numbers until it reaches 1 or 0.

4. Multiplying the results:

o As the recursive calls return, the function multiplies the results together. This

happens when the recursive calls start coming back from the smaller

numbers.

5. Testing the function:

o We test our magical factorial function by calling it with the number 5. The

function then calculates 5 × 4 × 3 × 2 × 1 step by step.

An Example Walkthrough

Let’s walk through what happens when we try to calculate 5!:

1. The function is called with 5:

o factorial(5) calls 5 × factorial(4)

2. Now, factorial(4) is called:

o factorial(4) calls 4 × factorial(3)

3. Next, factorial(3) is called:

o factorial(3) calls 3 × factorial(2)

4. Then, factorial(2) is called:

o factorial(2) calls 2 × factorial(1)

5. Finally, factorial(1) is called:

o Since 1 is our base case, we return 1.

Now, the recursive calls start returning:

• factorial(2) returns 2 × 1 = 2

• factorial(3) returns 3 × 2 = 6

• factorial(4) returns 4 × 6 = 24

• factorial(5) returns 5 × 24 = 120

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And there you have it! The factorial of 5 is 120.

Recursion in Real Life

Recursion is like climbing up a staircase, but instead of going straight to the top, you ask

yourself: "What’s one step lower than where I am?" Once you reach the bottom (the base

case), you can start going back up, and as you go, you put together all the pieces you need.

In Factorial Land, recursion helps break down a huge problem (like calculating the factorial

of 100) into much smaller problems that are easier to solve. Once you solve the smallest

problem (like calculating 1!), you can combine all the answers to solve the bigger problem.

Why Use Recursion?

Recursion is super helpful for problems that can be broken down into smaller subproblems.

For example, other problems like finding Fibonacci numbers, solving puzzles like mazes, or

even traversing trees in programming often use recursion. It's a way of solving complex

problems by breaking them into smaller, more manageable pieces.

Conclusion

Recursion in Factorial Land is a magical journey of asking yourself smaller and smaller

questions, until you reach the smallest one, and then using those answers to solve the

bigger problem. In this fun and easy-to-understand adventure, you learned how to calculate

the factorial of a number using recursion. You also saw how the number multiplies itself

step by step, just like climbing a staircase and coming back down.

4.(a) Differentiate packages and modules.

(b) What are the different methods of importing the external Python module?

Ans:(a). The Story of Packages and Modules: A Library Adventure 󹴷󹴺󹴸󹴹󹴻󹴼󹴽󹴾󹴿󹵀󹵁󹵂󽄻󽄼󽄽

Imagine you're in a giant library called "Python's Code Library." Inside this library, there are

shelves (packages) and books (modules). Each shelf and book helps you learn or solve a

problem in Python programming. Let's dive into the story to make the differences crystal

clear!

The Books: Modules

Think of modules as individual books in this library. Each book is about a specific topic. For

example:

• A book titled "math" teaches you calculations like addition, subtraction, or finding

square roots.

• Another book titled "os" explains how to interact with your computer's operating

system.

Easy2Siksha

These books (modules) are simple files with Python code, usually ending in .py. For example:

Python

The Shelves: Packages

Now imagine that the library organizes these books into shelves called packages. A package

is like a collection of books (modules) grouped together because they are about similar

topics. For example:

• A shelf called "utilities" might contain books (modules) about math, strings, and files.

In Python, a package is just a folder containing multiple modules and a special file named

__init__.py. This file helps Python recognize the folder as a package.

Key Differences Between Packages and Modules

Feature

Module (Books)

Package (Shelves)

Definition

A single file containing Python

code (e.g., math.py).

A folder containing multiple modules (and a

special __init__.py file).

Structure

Simple and standalone.

Structured and hierarchical, organizing related

modules.

Usage

Used to define functions,

variables, and classes.

Used to group related modules for better

organization and usability.

Example

math.py (a module with

mathematical functions).

utilities (a package containing modules like

math_tools.py and string_tools.py).

Importing

Import a module directly:

import math.

Import a module from a package: from utilities

import math_tools.

Purpose

To reuse code by writing it

once in a module.

To organize multiple modules into a coherent

structure.

Size

Smaller (single file).

Larger (multiple files in a folder).

A Code Example: Using Modules and Packages

Easy2Siksha

Creating a Module

You can create a file called math_tools.py with the following code:

Python

You can use this module in your program:

Python

Creating a Package

Now, create a folder named utilities. Inside this folder:

1. Add a file called __init__.py (it can be empty).

2. Add the module math_tools.py as above.

3. Add another module, string_tools.py:

Python

You can use this package as follows:

Easy2Siksha

Python

Exam-Ready Answer for Packages vs. Modules

1. Definition:

o A module is a single file with Python code.

o A package is a folder containing multiple modules and a special __init__.py

file.

2. Structure:

o A module is a standalone .py file.

o A package is a directory (folder) with related modules.

3. Purpose:

o A module is used to define specific functions, variables, or classes.

o A package is used to group related modules, making code organization easier.

4. Example:

o Module: math.py with functions like add and subtract.

o Package: A folder named utilities containing math_tools.py and

string_tools.py.

5. Importing:

o Module: import math.

o Package: from utilities import math_tools.

(b) What are the different methods of importing the external Python module?

Ans: A Fun Story: The Adventure of Python and Its New Friends

Once upon a time, in the kingdom of CodeLand, lived a language named Python. Python was

smart and friendly but sometimes needed help from other experts, known as modules.

These modules were like magical tools that could solve specific problems or teach Python

new tricks.

Easy2Siksha

One day, Python decided to invite these modules to a big party called "The Program." But

how would Python bring these experts to the party? Let’s follow Python’s journey and learn

the different ways to import these external modules. Each method is like an invitation style,

and Python loves keeping things simple yet magical!

Method 1: "Simple Invitation" – The Basic import

Python’s first idea was the simplest. It sent a plain and direct invitation:

Python

For example, if Python wanted help from the math module to do calculations, it would say:

Python

This is like saying, "Hey Math, come to the party and bring all your tricks!" Now Python

could use all the functions from Math by calling them as math.function_name().

Method 2: "Nickname Invitation" – Using as for an Alias

Sometimes, Python thought, "What if the module has a long name? Calling it repeatedly

might get tiring." So, it decided to give modules nicknames:

Python

For instance, if Python invited numpy, a module for numerical operations, it might give it a

shorter nickname like np:

Python

Now, Python could call the module quickly, just like how friends call each other by short

names!

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Method 3: "Exclusive Invitation" – Importing Specific Items

Python sometimes needed only a few tricks from a module, not the whole bag of tricks. To

avoid extra clutter, it would send an exclusive invitation for specific functions:

Python

For example, if Python wanted only the sqrt function from the math module, it would write:

Python

This saved time and energy since Python didn’t need to mention math.sqrt—just sqrt was

enough.

Method 4: "Custom Invitation" – Import Specific Items with a Nickname

Sometimes, Python wanted to call a specific item by a different name. It combined the

exclusive and nickname methods like this:

Python

For example, if it invited only the factorial function from math but called it fact, it would say:

Python

It was like giving a nickname to just one guest instead of the whole group.

Method 5: "Bring Everyone" – Using from module_name import *

Once in a while, Python threw a party where it wanted every function and item from a

module without calling their names separately. It used the * symbol, which means

“everything”:

Easy2Siksha

Python

For example, inviting all of math looked like this:

Python

But Python was careful with this method because it could make things messy if two modules

brought the same function name.

Method 6: "Dynamic Invitation" – Using importlib for Importing During the Party

Sometimes, Python wanted to invite modules dynamically—like deciding during the party

itself. It used a special helper called importlib:

Python

This method was useful when Python didn’t know beforehand which module it would need.

Method 7: "Custom Modules" – Inviting Local Friends

Python also had its own friends—modules it created itself! If Python’s local friend lived in

the same neighborhood (folder), it would invite them like this:

Python

For example, if Python’s friend was a file named my_module.py with a function greet(),

Python would say:

Easy2Siksha

Python

Method 8: "Packages" – Inviting Families of Modules

What if Python wanted to invite a whole family of modules? It used packages, which were

like folders containing related modules. To invite a specific module from a package, Python

wrote:

Python

For example, if Python had a package called my_package with a module module1, it would

say:

Python

Method 9: "Special Tools" – Installing and Importing External Modules

Not all friends lived in CodeLand. Some were far away in a land called PyPI (Python Package

Index). Python had to bring them using a special transporter called pip:

After installing, Python could invite them just like regular modules:

Python

Easy2Siksha

Key Points for Your Exam

1. Basic Import: import module_name brings the whole module.

2. Alias: import module_name as alias gives a short name to a module.

3. Specific Import: from module_name import specific_item brings only what’s needed.

4. Alias for Specific Items: from module_name import specific_item as alias gives

nicknames to specific functions.

5. Wildcard Import: from module_name import * imports everything but may cause

conflicts.

6. Dynamic Import: importlib.import_module() imports modules during runtime.

7. Custom Modules: You can import Python files you’ve created.

8. Packages: Import from folders of related modules.

9. External Modules: Install with pip and then import.

Conclusion

Python’s party was a huge success because it could import modules in so many flexible

ways. Each method was like a different invitation style, tailored to the situation. Now you,

too, can host amazing code parties with Python and its module friends!

Keep this story in mind during exams, and you’ll never forget how to import modules in

Python. 󷮎󷮏󷮐󷮑󷮒󷮓󷮔󷮕󷮖󷮗󷓠󷓡󷓢󷓣󷓤󷓥󷓨󷓩󷓪󷓫󷓦󷓧󷓬

SECTION-C

5.(a) How to open and write into Python Files? Explain with the program.

(b) What is the significance of Exception Handling? Write a program to handle multiple

exceptions.

Ans.(a). How to Open and Write into Python Files:

Imagine you’re a detective, and your job is to keep a record of clues in a notebook. This

notebook represents a "file" in Python. Just like you open your notebook, write clues, and

close it to keep everything safe, Python handles files in the same way! Let’s dive into this

adventure and learn step by step how to open and write into files in Python, with a fun

program at the end to summarize it all.

Step 1: Opening a File

To write clues (data) into your file, you need to first open it. In Python, this is done using the

open() function.

Easy2Siksha

• Think of open() as the key to your notebook.

• You tell Python the name of the file and what you want to do with it (write, read, or

both).

Syntax:

Here’s what the modes mean:

• "w": Write mode (creates a new file or overwrites an existing one).

• "a": Append mode (adds data to the end of the file without deleting existing data).

• "r": Read mode (just reads the file, no writing).

For our detective story, let’s say you want to create a new notebook called "clues.txt" and

write into it.

Step 2: Writing into the File

Once your notebook is open, you need a pen (Python method) to write your clues. The

method we use is write().

Syntax:

Step 3: Closing the File

After writing the clues, you must close the file to make sure your secrets are saved safely.

Python’s close() method does this.

Syntax:

Program Example: Detective Python’s Notebook

Here’s how you can open and write into a file in Python:

Easy2Siksha

Python

Step-by-Step Explanation of the Code

1. Opening the File

o We use open("clues.txt", "w") to create or open the file named "clues.txt" in

write mode.

o If "clues.txt" doesn’t exist, Python creates it for us.

2. Writing Clues

o The write() method is used to add text (our clues) into the file.

o Each line ends with \n to ensure proper formatting.

3. Closing the File

o Using close() ensures the file is properly saved and prevents data loss.

o Forgetting to close the file is like leaving your notebook open and

unattended!

Additional Modes for Special Cases

• Append Mode ("a")

Imagine your detective work isn’t done yet, and you have more clues to add. Use "a"

mode:

Python

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This adds the new clue without deleting the existing ones.

• Read Mode ("r")

If you want to revisit the clues you’ve written:

Python

This reads and prints all the contents of the file.

Why Is File Handling Important?

• Organizing Data: Files let you store data persistently, even when the program stops

running.

• Easy Data Sharing: You can save results or logs and share them with others.

• Automation: From keeping records to managing logs, file handling makes Python

programs more powerful.

Common Mistakes and How to Avoid Them

1. Forgetting to Close the File

o Always close the file using close(). Alternatively, use a with block (explained

below).

2. Overwriting Data Accidentally

o Be careful with "w" mode; it overwrites everything in the file. Use "a" mode if

you want to add data.

3. File Not Found Error

o This happens when trying to open a non-existent file in "r" mode. Double-

check file names.

Pro Tip: Using with for Safer File Handling

Python provides a cleaner way to handle files using a with block. It automatically closes the

file after you’re done.

Example:

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Output of the Program

When you run the first program, a file named "clues.txt" will be created. The file will

contain:

Conclusion

Think of file handling in Python as managing a notebook for your detective adventures:

1. Open the file to begin writing (open()).

2. Write your clues or notes (write()).

3. Close the file to save everything safely (close()).

(b) What is the significance of Exception Handling? Write a program to handle multiple

exceptions.

Ans:(b). The Tale of Bob and the Broken Bridge: Understanding Exception Handling

Imagine Bob, an adventurous young boy, setting out on a journey to cross a rickety bridge to

reach a treasure chest on the other side. But there’s a catch: the bridge is unstable and has

different types of traps waiting for him. If Bob doesn’t handle these traps wisely, he might

fall into the river or get stuck halfway.

Now, let’s compare Bob’s journey to writing computer programs. Just like Bob, programs

often encounter unexpected problems, like a trap on the bridge. These problems are called

exceptions. If we don’t handle exceptions, our program can “fall” and crash. But if we use

exception handling, we can guide the program safely past the traps and continue its

journey.

Significance of Exception Handling

Think of exception handling as Bob’s toolkit. It helps him:

1. Catch Problems Early: Bob uses his toolkit to spot traps in advance and plan how to

tackle them. Similarly, exception handling identifies problems before they crash the

program.

2. Keep the Journey Going: Even if Bob encounters a trap, he finds a way around it and

continues. In programs, exceptions let you handle errors without stopping the entire

process.

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3. Make the Program User-Friendly: Imagine if Bob simply gave up every time he saw a

trap—he’d never reach the treasure! In the same way, exception handling ensures

the program doesn’t abruptly stop, confusing users.

4. Debugging Made Easy: Bob writes down what went wrong so he doesn’t repeat the

same mistakes. Exception handling records errors, making it easier for developers to

fix them later.

Bob’s Toolkit in Code: Handling Multiple Exceptions

Let’s see how Bob would handle different traps (exceptions) using Python. These traps could

be:

• A broken plank (ZeroDivisionError)

• A missing part of the bridge (ValueError)

• A gust of wind pushing him off (FileNotFoundError)

Here’s how Bob prepares his toolkit to handle all these exceptions:

Python

Step-by-Step Explanation of the Program

1. try Block:

o Bob begins his journey.

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o The program attempts to:

▪ Get a number from the user and divide 100 by it.

▪ Open a file called treasure_map.txt.

o If something goes wrong, the program doesn’t crash; it jumps to the

appropriate except block.

2. except Blocks:

o Each except block handles a specific trap:

▪ ZeroDivisionError: Caught if the user enters 0, causing a division by

zero.

▪ ValueError: Caught if the user enters something that’s not a number.

▪ FileNotFoundError: Caught if the file treasure_map.txt is not found.

3. finally Block:

o Bob always ensures he gets back home safely, regardless of what happens on

the bridge. Similarly, the finally block is always executed, whether an

exception occurs or not.

What Happens During Execution?

Let’s see what could happen during Bob’s journey:

Case 1: A Safe Journey

Input: 10

File: treasure_map.txt exists.

Output:

Case 2: A Broken Plank

Input: 0

Output:

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Case 3: Missing Part of the Bridge

Input: abc

Output:

Case 4: Treasure Map Missing

Input: 5

File: treasure_map.txt does not exist.

Output:

Python

Why is Exception Handling Important in Real Life?

• Banking Applications: Imagine transferring money online, and the connection drops

mid-way. Exception handling ensures the transaction is either completed or

reversed, so no money is lost.

• Web Browsers: When you visit a webpage and the server is down, the browser

doesn’t crash—it shows a helpful error message instead.

• File Management: When you open a file that doesn’t exist, exception handling

prevents your program from crashing and informs you about the missing file.

Key Takeaways

1. Safety First: Exception handling keeps your program running smoothly, even if

something unexpected happens.

2. User Experience Matters: By handling errors gracefully, you provide users with

helpful feedback instead of leaving them puzzled by a program crash.

3. Plan for the Worst: Always assume things can go wrong and prepare your program

to handle them.

Easy2Siksha

6. (a) How to overload an Operator in Python? Define the use of Destructor Program.

(b) Discuss the concept of overriding the methods with the program.

Ans.(a). Story Time: Operators and the Destructor

Once upon a time, in the magical land of Pythonia, there were powerful characters called

Operators. These Operators were like tools—used for simple tasks like addition (+),

subtraction (-), and comparison (==). But one day, a curious programmer named Pyro

wanted the Operators to do more than just their basic tasks.

"Why can’t my Operators behave differently depending on the context?" Pyro wondered.

Pyro had a genius idea: Overloading Operators! Overloading meant teaching Operators new

tricks so they could understand and work with Pyro’s custom objects.

Meanwhile, in another part of Pythonia, there was an important guardian called the

Destructor. This Destructor had a single job: to clean up objects when they were no longer

needed, like cleaning up after a big party. This ensured Pythonia remained clean and

efficient.

And so, Pyro learned to overload Operators and use Destructors wisely, making their

programs magical and powerful. Let’s learn how Pyro did it!

00What is Operator Overloading?

In Python, Operator Overloading means giving an operator (like + or *) a new meaning when

it is used with objects of your own class. For example, if you want to add two objects of a

custom class using +, you can define what the + should do for those objects.

Python has special methods (called magic methods or dunder methods) for this purpose. For

example:

• __add__ for +

• __sub__ for -

• __mul__ for *

• __eq__ for ==

By defining these methods in your class, you can "teach" the operators how to behave with

your objects.

How Does Operator Overloading Work?

Imagine you’re building a class called Box that represents boxes with specific dimensions. If

you want to add two Box objects together to calculate the total volume, you can use

Operator Overloading.

Here's an example:

Easy2Siksha

Python

Explanation of the Code:

1. The __add__ method is defined to overload the + operator.

2. When you write box1 + box2, Python calls box1.__add__(box2).

3. This method combines the dimensions of box1 and box2 to create a new Box object.

4. The volume method calculates the volume of the resulting Box.

Why Use Operator Overloading?

1. Readability: Makes your code more intuitive (e.g., using + instead of a method like

add_boxes).

2. Flexibility: Allows objects to behave like built-in types.

3. Customization: Tailor the behavior of operators to match your class's purpose.

What is a Destructor?

A Destructor in Python is like the clean-up crew at the end of a party. When an object is no

longer needed, the Destructor ensures all resources used by the object are released.

In Python, the destructor is defined using the __del__ method. It’s automatically called

when an object is deleted or goes out of scope.

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How Does the Destructor Work?

Here’s an example:

Output:

Explanation of the Code:

1. The __del__ method is called when the object is deleted.

2. In this case, it prints a message saying the party is over and resources are being

cleaned up.

Key Points About the Destructor

1. Automatic Cleanup: Destructors are often called automatically when an object goes

out of scope or the program ends.

2. Explicit Cleanup: You can use del to delete an object manually and trigger the

destructor.

3. Not Always Needed: In Python, memory management is handled by the garbage

collector, so you don’t need destructors for every class.

Combining Operator Overloading and Destructors

Let’s create a program that uses both Operator Overloading and a Destructor for a class

called Treasure.

Easy2Siksha

Python

Output:

Summary for Exams

1. Operator Overloading allows you to redefine how operators (+, -, etc.) behave for

your custom classes. Use special methods like __add__, __sub__, etc., to achieve

this.

2. Destructor (__del__) cleans up resources when an object is no longer needed. It’s

useful for managing memory and performing cleanup tasks.

Easy2Siksha

3. Practical Use: Combine operator overloading and destructors in a class to make your

objects behave like built-in types while ensuring efficient resource management.

(b) Discuss the concept of overriding the methods with the program.

Ans: Imagine you’re the owner of a big magical library, and every time a new wizard joins

your library, they get a special spellbook that tells them how to cast spells. Now, sometimes

these wizards don’t like the default spells written in their spellbooks. So, what do they do?

They write new instructions on top of the old ones. This way, when they use their spellbook,

the magic follows their rules instead of the original ones.

This process of replacing old instructions with new ones is like overriding methods in

programming. Let’s explore how this magical concept works in real-world coding!

What is Overriding?

In simple terms, method overriding happens when a child class (a subclass) changes or

customizes a method it inherited from its parent class (a superclass). It’s like a child wizard

rewriting their spellbook to suit their own needs.

Here’s the official definition:

Overriding is a feature in Object-Oriented Programming where a subclass provides its own

implementation of a method that is already defined in its parent class.

Why Do We Override?

1. To Customize Behavior:

Just like our wizards want their spells to work differently, programmers override

methods to make a class behave in a specific way.

2. To Extend Functionality:

Sometimes, the default behavior isn’t enough. We override methods to add extra

features.

The Rules of Overriding (The Wizard’s Code of Conduct):

1. Same Method Name:

The method in the child class must have the exact same name as the one in the

parent class.

2. Same Parameters:

The parameters (inputs) of the method must also match. You can’t change the type,

number, or order of arguments.

3. Access Modifier Cannot Be Restricted:

If the parent’s method is marked public, the overridden method in the child class

must also be public (or more accessible).

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4. Return Type Must Match:

The return type of the overridden method should be the same (or a compatible

type).

How to Override Methods (Wizard Instructions):

Let’s look at a real-world coding example to see overriding in action.

A Magical Example in Java

Here, we have a generic Animal class. It has a method sound() that prints a general

message: "Animals make sounds."

The Child Class (Subclass):

The Dog class inherits from Animal. But dogs don’t just make random sounds; they bark! So,

the sound() method is overridden to display a more specific message: "Dogs bark."

Main Program:

Easy2Siksha

Output:

What Happened Here?

1. When myAnimal.sound() was called, the parent class method ran, and we saw

"Animals make sounds."

2. When myDog.sound() was called, the overridden method in the Dog class took over,

and we saw "Dogs bark."

This is the magic of overriding: the child class rewrites the parent’s method to create its own

version.

More Fun Examples

The Parent Class (Vehicle):

The Child Class (Car):

java

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Another Child Class (Boat):

java

Main Program:

java

Key Concepts to Remember for Exams:

1. Dynamic Method Dispatch (Runtime Polymorphism):

The overridden method that gets called is determined at runtime, based on the

object’s type.

2. Use of @Override Annotation:

Always use @Override above your method. It helps the compiler check if you’re truly

overriding a method from the parent class.

3. Cannot Override final Methods:

If a method in the parent class is marked final, you cannot override it in the child

class.

4. Static Methods Are Not Overridden:

Static methods are tied to the class, not objects, so they cannot be overridden.

However, they can be hidden.

Easy2Siksha

Comparison: Overloading vs. Overriding

Feature

Overloading

Overriding

Definition

Same method name but different

parameters.

Same method name and same

parameters.

Occurs In

The same class.

Parent-child (inheritance)

relationships.

Time of

Binding

Compile-time (Static Polymorphism).

Runtime (Dynamic Polymorphism).

When Not to Override?

1. When the existing method from the parent class is sufficient.

2. When you need to preserve the parent’s behavior as it is.

3. When overriding could lead to confusion or unwanted results.

Real-Life Analogy to Remember:

Think of your favorite app on your phone. The app might come with default settings, but

you, as the user, can override those settings to make the app work exactly how you like.

Similarly, in programming, child classes override methods from parent classes to “customize

the app” or make it behave differently.

Conclusion:

Overriding is one of the most powerful features in Object-Oriented Programming because it

allows programmers to reuse, enhance, and adapt functionality. It’s a bit like giving your

favorite characters in a story their own unique twist—customizing the magic to suit the plot!

SECTION-D

7.(a) How is table created using Python? Illustrate how to search a table in Python?

(b) Explain data modelling with examples.

Ans.(a). Imagine you’re a chef at the Python Café, and your job is to set up tables for the

customers. These tables aren’t for dining—they hold data! Python is your toolkit to create,

decorate, and search these tables. Let's dive into this fun story to understand how to create

and search a table in Python!

Step 1: Meet the Table-Creating Tools

At the Python Café, you have several ways to create tables. Here are the most popular ones:

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1. Using Lists: Think of a table as a list of lists, where each inner list represents a row.

2. Using Dictionaries: Dictionaries let you label your columns with headings, making

them easy to read.

3. Using Pandas: The superstar of the café, Pandas, can create professional-grade

tables (called DataFrames) that are clean and stylish.

Step 2: Creating a Table (Setting the Table)

Let’s set up a table step by step. Here are three methods:

1. Table with Lists

Think of each row as a list. Combine these rows into one big list to create the table.

Python

Output:

2. Table with Dictionaries

Dictionaries are like labeled drawers. Each key represents a column, and the values are the

rows.

Easy2Siksha

Python

Output:

3. Table with Pandas (The Pro Way)

Pandas makes your table look elegant and ready for serious business. You need to install

Pandas first (if you haven’t already):

Here’s how to use Pandas:

Easy2Siksha

Output:

Step 3: Searching the Table (Finding the Perfect Dish)

Now, let’s explore how to search for data in your table.

1. Searching in a List Table

If your table is a list of lists, you can loop through it to find what you need.

Python

Output:

2. Searching in a Dictionary Table

Dictionaries make searching easy by using column names.

Python

Output:

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3. Searching in a Pandas Table

With Pandas, searching is as simple as magic. Use conditions to filter rows.

Python

Output:

Copy code

Name Age City

1 Bob 30 Los Angeles

Step 4: Why Use Pandas?

At the Python Café, Pandas is like the master chef. It’s fast, efficient, and handles large

tables with ease. You can:

• Filter data based on conditions.

• Sort and organize the table.

• Perform calculations like averages or totals.

Here’s an example of sorting:

Python

Output:

Easy2Siksha

Recap: "The Python Café Rules"

• Lists: Great for small tables, like handwritten notes.

• Dictionaries: Best for organized data with labels.

• Pandas: The professional choice for big, clean, and searchable tables.

Exam-Worthy Explanation

Python offers multiple ways to create and search tables:

1. Lists: Simplest way to organize rows of data.

o Use nested lists to represent rows and columns.

o Searching involves looping through rows.

2. Dictionaries: Label your columns for better clarity.

o Use column names as keys and row values as lists.

o Searching involves using keys and indexes.

3. Pandas: The most powerful and user-friendly method.

o Create tables using pd.DataFrame.

o Search with conditions and filters.

Choose the method based on the complexity of your data. For small, quick tables, lists or

dictionaries work. For real-world applications, Pandas is the way to go!

(b) Explain data modelling with examples.

Ans: Understanding Data Modelling:

Imagine you’re about to open a restaurant. Before you serve your first customer, you need

to organize everything. What’s on the menu? How many tables are there? How will

customers place orders? Where will you store the ingredients? Organizing all this

information is like creating a plan for your restaurant, and that’s what data modelling

does—but for computer systems.

Now, let’s dive into the concept of data modelling step by step.

What is Data Modelling?

Data modelling is the process of organizing and structuring data so that it can be stored,

retrieved, and used efficiently in databases. It’s like creating a blueprint for how data will be

handled in a system. Without a good data model, the system can become chaotic, much like

a restaurant that doesn’t know how to manage its menu or seating.

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Why is Data Modelling Important?

• Clarity: It helps developers understand how data is connected and how it flows.

• Efficiency: A well-designed data model ensures that the database works faster and

smoother.

• Flexibility: It makes it easier to update or expand the system in the future.

• Accuracy: It reduces errors by defining clear rules for storing and retrieving data.

The Key Components of Data Modelling

To make it simple, think of data modelling like organizing your restaurant's information into

three main levels:

1. Conceptual Data Model

This is the “big picture” plan. You’re only thinking about what you need without worrying

about the details.

Example:

• Entities: These are the main objects. For your restaurant, these could be:

o Customers

o Orders

o Menu Items

• Relationships: How are these entities connected?

o A customer places an order.

o An order includes one or more menu items.

This stage doesn’t worry about the technology or how it will work; it’s just about identifying

the key elements.

2. Logical Data Model

Now, you’re adding more details. Imagine you’re deciding where to place tables and how

orders will move between the kitchen and the dining area.

Example:

• Attributes: Each entity gets specific characteristics.

o Customer: Name, Contact Number, Email Address

o Menu Item: Name, Price, Category

o Order: Order ID, Order Date, Total Amount

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• Primary Keys: These uniquely identify each entity.

o Customer ID for customers

o Order ID for orders

o Menu Item ID for menu items

This stage still doesn’t focus on the technical implementation but lays out the structure

more clearly.

3. Physical Data Model

This is where the plan becomes reality. You decide the exact layout of the tables, the

kitchen appliances, and the storage.

Example:

• Database Tables: Entities like "Customer," "Order," and "Menu Item" are now turned

into tables in a database.

• Columns and Data Types: Each attribute is assigned a data type.

o Customer Name: Text

o Price: Decimal

o Order Date: Date/Time

• Relationships in Action: These are implemented using foreign keys.

o The Order table has a foreign key linking it to the Customer table.

o The Order Details table connects each order to the menu items included in it.

An Example of Data Modelling in Action

Let’s bring this restaurant to life!

Step 1: Conceptual Model

• Entities: Customer, Menu Item, Order

• Relationships:

o A customer places orders.

o An order contains multiple menu items.

Step 2: Logical Model

• Attributes for Customer: ID, Name, Contact Number

• Attributes for Menu Item: ID, Name, Price

• Attributes for Order: Order ID, Customer ID (foreign key), Total Amount

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• Relationships:

o Customer (ID) → Order (Customer ID)

o Order (Order ID) → Menu Items (Order ID)

Step 3: Physical Model

• Create database tables:

1. Customer Table

▪ Columns: Customer ID (Primary Key), Name, Contact Number

2. Menu Item Table

▪ Columns: Item ID (Primary Key), Name, Price

3. Order Table

▪ Columns: Order ID (Primary Key), Customer ID (Foreign Key), Total

Amount

Now, the data is ready to be stored, retrieved, and processed!

Types of Data Models

To help you understand the variety, let’s look at some common types of data models:

1. Hierarchical Model

• Like a tree, data is organized in a hierarchy.

• Example: A library system where a “Category” contains multiple “Books,” and each

book has “Chapters.”

2. Network Model

• Data is connected through multiple relationships, forming a graph.

• Example: A transport system where cities are nodes and routes are connections.

3. Relational Model

• Data is stored in tables, and relationships are defined using keys.

• Example: Our restaurant system is a relational model with tables for customers,

orders, and menu items.

4. Object-Oriented Model

• Data is stored as objects, much like programming languages.

• Example: A design system where shapes like circles and squares have attributes like

size and color.

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Challenges in Data Modelling

Even though it sounds simple, data modelling can be tricky:

1. Complex Requirements: Real-world systems often have many interconnected parts.

2. Changing Needs: Businesses evolve, and data models need to adapt.

3. Balancing Simplicity and Detail: Models must be detailed enough to work but not so

complex that they’re hard to understand.

Benefits of Good Data Modelling

• Clear Communication: It acts as a shared language between developers and business

stakeholders.

• Better Performance: Optimized models ensure the system runs efficiently.

• Ease of Maintenance: Changes are easier to make when the data structure is well-

organized.

Conclusion

Think of data modelling as the secret recipe for making complex systems easy to manage.

Just like organizing a restaurant ensures happy customers and smooth operations, a good

data model keeps computer systems efficient and error-free.

Whether it’s a small project or a large enterprise system, data modelling is the first step to

building something reliable. By remembering the restaurant analogy, you’ll always have a

fun and clear way to understand and explain data modelling.

8. Explain the following commands with examples:

(a) Modify Command

Ans: Imagine a magical classroom where a librarian named Data is in charge of a huge

spellbook called the "Database." In this spellbook, there are many pages called "Tables,"

filled with rows of magical items called "Records" and columns of magic properties called

"Fields."

Now, two wizard students, Modify and Delete, are learning how to use their magical wands

to change or erase these records in the spellbook. Let’s see how they do it and how you can

remember their spells for your exams.

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Explanation of Commands

(a) Modify Command

The Modify Command, also known as the UPDATE command, is like the wand that wizard

Modify uses to change or update the magical properties (fields) of items (records) in the

spellbook (table).

Syntax:

WHERE condition;

• UPDATE table_name: Tells which table (or page) in the spellbook you want to

modify.

• SET column1 = value1: Indicates which magical property (field) should be changed

and what the new value will be.

• WHERE condition: Specifies which records (items) will be updated. Without this, the

spell affects all records.

Example:

Imagine a table called Students with the following information:

Name

Age

Marks

Harry

Hermione

Ron

Now, the wizard Modify wants to update Ron's marks from 75 to 80. He uses the following

spell:

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After this spell, the table looks like this:

Name

Age

Marks

Harry

Hermione

Ron

Key Points for Exams:

1. Always use the WHERE clause unless you want to update all records.

2. You can update one or multiple columns at the same time.

3. Without SET, the command won’t work.

The Delete Command, also called DELETE, is wizard Delete’s wand that erases unwanted

magical items (records) from the spellbook (table).

Syntax:

WHERE condition;

• DELETE FROM table_name: Tells which table (or page) in the spellbook you want to

erase records from.

• WHERE condition: Specifies which records to delete. Without this, all records vanish!

Example:

Using the same Students table:

Name

Age

Marks

Harry

Hermione

Ron

Now, wizard Delete wants to remove Harry’s record. He uses this spell:

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After this spell, the table becomes:

Name

Age

Marks

Hermione

Ron

Key Points for Exams:

1. The WHERE clause is crucial to delete specific records. Without it, the entire table is

cleared.

2. Use DELETE carefully; once data is gone, it cannot be restored (unless backups exist).

3. If you want to delete all records but keep the table, use:

Final Magical Tips for the Exam:

1. Remember the Syntax:

o Modify (Update): Think of it as changing a record’s details.

o Delete: Think of it as removing an unwanted record.

2. Example Usage: Always back up explanations with simple examples like the Students

table.

3. Key Exam Words:

o Use terms like SET, WHERE, DELETE, and UPDATE clearly in your answers.

o Mention how these commands affect the data in tables.

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