Lesson 45: Python How-Tos, Examples, Exercises, Challenges, Syllabus, Study Plan & Interview Prep

📖 Lesson Introduction

Welcome to Lesson 45 — the grand consolidation lesson of your Python journey.

By now you have learned individual Python building blocks: variables, loops, functions, lists, dictionaries, and more. This lesson brings everything together.

Here is what you will accomplish in this lesson:

Learn how to solve common real-world Python problems (remove duplicates, reverse a string, add numbers)
Work through practical Python examples drawn from real usage
Complete beginner exercises to test your understanding
Take a quiz to identify what you truly know
Tackle challenges that stretch your thinking
Follow a structured Python syllabus — a complete map of Python topics
Use a study plan to learn Python in an organised schedule
Prepare for Python interview questions so you are job-ready
Understand bootcamp and training pathways so you know how to continue growing

💡 This lesson assumes you know nothing. Every concept is taught from scratch. Even if you have seen some of these before, this lesson deepens your understanding through new examples and exercises.

🔑 Prerequisite Concepts

Before diving in, make sure you understand these terms. They are explained simply below.

Term	Simple Meaning
Variable	A named box that stores a value, like `name = "Alice"`
List	An ordered collection of items, like `[1, 2, 3]`
String	A piece of text surrounded by quotes, like `"hello"`
Loop	A set of instructions that repeats, like `for x in list:`
Function	A reusable block of code given a name, like `def greet():`
Integer	A whole number like `5` or `100`
Print	The command that displays output on screen: `print("hello")`

If any of these feel shaky, don’t worry — each one is demonstrated freshly throughout this lesson.

Part 1: Python How-To Guides

These “how-to” sections answer the most common practical Python questions beginners ask.

🔷 How-To 1: Remove Duplicates from a List

What is a duplicate?

A duplicate is an item that appears more than once in a list.

Example:

fruits = ["apple", "banana", "apple", "cherry", "banana"]

Here, "apple" appears twice and "banana" appears twice. Those are duplicates.

Why remove duplicates?

In real-world data — like survey responses, sensor readings, or product lists — duplicates often appear by mistake. Removing them gives you a clean, unique set of values.

Real-world analogy: Imagine a sign-up sheet where the same person signed their name three times. You only need to count them once. Removing duplicates is like counting each person only once.

Method 1: Using a `set()`

A set in Python is a collection that automatically removes duplicates. Sets do not allow repeated values.

# Step 1: Start with a list that has duplicates
my_list = [1, 2, 3, 2, 1, 4, 5, 3]

# Step 2: Convert it to a set (duplicates disappear automatically)
unique_set = set(my_list)

# Step 3: Convert it back to a list if you need a list
unique_list = list(unique_set)

print(unique_list)

Expected Output:

[1, 2, 3, 4, 5]

⚠️ Important: Sets are unordered. The output may appear in a different sequence each time you run the program. If order matters, use Method 2 below.

Breaking Down the Code Line by Line

Line	What it does
`my_list = [1, 2, 3, 2, 1, 4, 5, 3]`	Creates a list with some duplicate numbers
`unique_set = set(my_list)`	Converts the list to a set — Python automatically discards duplicates
`unique_list = list(unique_set)`	Converts the set back to a list so you can use list features again
`print(unique_list)`	Displays the result on screen

Method 2: Using a Loop (Preserves Order)

If the order of items matters, use a loop to manually build a new list without duplicates.

# Original list with duplicates
my_list = ["banana", "apple", "banana", "cherry", "apple"]

# New empty list to collect unique items
unique_list = []

# Go through each item in the original list
for item in my_list:
    # Only add the item if it's NOT already in unique_list
    if item not in unique_list:
        unique_list.append(item)

print(unique_list)

Expected Output:

['banana', 'apple', 'cherry']

Notice the order is preserved — "banana" comes first because it appeared first.

Line-by-Line Explanation

Line	What it does
`unique_list = []`	Creates an empty list that will hold results
`for item in my_list:`	Loops through every item in the original list one by one
`if item not in unique_list:`	Checks if this item has already been added
`unique_list.append(item)`	If not already added, add it now
`print(unique_list)`	Shows the final deduplicated list

Method 3: Using `dict.fromkeys()` (Preserves Order, Very Compact)

my_list = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]

unique_list = list(dict.fromkeys(my_list))

print(unique_list)

Expected Output:

[3, 1, 4, 5, 9, 2, 6]

dict.fromkeys() creates a dictionary from the list (dictionaries can’t have duplicate keys), then list() converts it back. This is a clean, Pythonic one-liner.

🤔 Thinking Prompt

What happens if you run set(["a", "a", "b"]) — how many items will be in the result? Why?

🔁 Quick Recap — Remove Duplicates

Method	Preserves Order?	Best For
`set()`	❌ No	When order doesn’t matter
Loop with `not in`	✅ Yes	When order matters
`dict.fromkeys()`	✅ Yes	Compact, readable code

🔷 How-To 2: Reverse a String

What does “reverse a string” mean?

It means flipping the text so the last character becomes the first.

Original: "hello"
Reversed: "olleh"

Why would you reverse a string?

Checking if a word is a palindrome (reads the same forwards and backwards, like “racecar”)
Text processing and data manipulation
Puzzle and interview challenges
Game mechanics (secret codes, encryption basics)

Method 1: Slicing (The Pythonic Way)

Python’s slice notation lets you extract parts of a string using [start:stop:step].

To reverse: use [::-1] — which means “start from end, go backwards, step by -1.”

# Original string
text = "Hello, World!"

# Reverse using slicing
reversed_text = text[::-1]

print(reversed_text)

Expected Output:

!dlroW ,olleH

Understanding `[::-1]` Step by Step

Think of a string as a row of numbered seats:

H  e  l  l  o
0  1  2  3  4

[start:stop:step] = [::-1]
No start → begin from wherever the step takes you (the end, since step is negative)
No stop → go all the way to the other end
-1 step → move one position backwards each time

So Python visits: o, l, l, e, H → producing "olleH".

Method 2: Using the `reversed()` Function

text = "Python"

# reversed() returns an iterator, join() combines the characters
reversed_text = "".join(reversed(text))

print(reversed_text)

Expected Output:

nohtyP

reversed(text) — creates a reverse iterator over the characters
"".join(...) — joins all the characters together with no separator between them

Method 3: Using a Loop (Good for Understanding)

text = "science"
reversed_text = ""

for char in text:
    reversed_text = char + reversed_text  # each new char goes to the FRONT

print(reversed_text)

Expected Output:

ecneics

Each loop iteration prepends the current character to the front of reversed_text.

Palindrome Check — Practical Application

word = "racecar"

if word == word[::-1]:
    print(word, "is a palindrome!")
else:
    print(word, "is NOT a palindrome.")

Expected Output:

racecar is a palindrome!

Try it with "python" — what do you get?

🤔 Thinking Prompt

What is "12345"[::-1]? Write it out by hand first, then test it.

🔷 How-To 3: Add Two Numbers

What does “add two numbers” mean in Python?

This is the simplest form of arithmetic in Python. You use the + operator.

x = 5
y = 3
result = x + y
print(result)

Expected Output:

Simple! But let’s go deeper.

Adding Numbers Entered by the User

In real programs, you usually don’t know the numbers in advance — the user types them in.

# Ask the user to type two numbers
num1 = input("Enter first number: ")
num2 = input("Enter second number: ")

# IMPORTANT: input() always returns a STRING, not a number
# We must convert to int (whole number) or float (decimal)
sum_result = int(num1) + int(num2)

print("The sum is:", sum_result)

Example Interaction:

Enter first number: 12
Enter second number: 7
The sum is: 19

Why Must We Convert with `int()`?

When a user types "5" on the keyboard, Python stores it as the text "5", not the number 5.

Text + Text in Python does concatenation (joining), not addition:

print("5" + "3")   # Output: 53  ← wrong! This joins strings
print(5 + 3)       # Output: 8   ← correct! This adds numbers

So int("5") converts the text "5" into the number 5.

Adding Floating-Point (Decimal) Numbers

a = 4.5
b = 2.3

total = a + b
print("Total:", total)

Expected Output:

Total: 6.8

Use float() instead of int() when decimals are needed:

num1 = float(input("Enter first decimal: "))
num2 = float(input("Enter second decimal: "))
print("Sum:", num1 + num2)

Adding Numbers in a List

Sometimes you want to add all numbers in a collection:

numbers = [10, 20, 30, 40, 50]
total = sum(numbers)
print("Total:", total)

Expected Output:

Total: 150

sum() is a built-in Python function that adds all values in a list.

Part 2: Python Examples — Core Concepts Demonstrated

This section covers the most important Python examples that every beginner must know. These are drawn directly from real Python practice and professional usage.

🔷 Example Category 1: Print and Variables

Printing Text

print("Hello, World!")

Output:

Hello, World!

Storing and Using Variables

name = "Alice"
age = 25
city = "London"

print(name, "is", age, "years old and lives in", city)

Output:

Alice is 25 years old and lives in London

Multiple Data Types

text = "Python"          # String
whole_number = 42        # Integer
decimal = 3.14           # Float
is_fun = True            # Boolean

print(type(text))
print(type(whole_number))
print(type(decimal))
print(type(is_fun))

Output:

<class 'str'>
<class 'int'>
<class 'float'>
<class 'bool'>

🔷 Example Category 2: String Operations

String Length

sentence = "Hello, Python!"
print(len(sentence))

Output:

Uppercase and Lowercase

word = "Python"
print(word.upper())
print(word.lower())

Output:

PYTHON
python

String Slicing

text = "Programming"
print(text[0:4])    # First 4 characters
print(text[-3:])    # Last 3 characters

Output:

Prog
ing

String Replacement

sentence = "I love Java"
new_sentence = sentence.replace("Java", "Python")
print(new_sentence)

Output:

I love Python

String Split

data = "Alice,Bob,Charlie,Diana"
names = data.split(",")
print(names)

Output:

['Alice', 'Bob', 'Charlie', 'Diana']

🔷 Example Category 3: Lists

Create and Access a List

fruits = ["apple", "banana", "cherry"]
print(fruits[0])    # First item
print(fruits[-1])   # Last item
print(fruits[1:3])  # Slice: items at index 1 and 2

Output:

apple
cherry
['banana', 'cherry']

Add, Remove, Sort

numbers = [3, 1, 4, 1, 5, 9, 2, 6]

numbers.append(7)        # Add to end
numbers.remove(1)        # Remove first occurrence of 1
numbers.sort()           # Sort ascending

print(numbers)

Output:

[1, 2, 3, 4, 5, 6, 7, 9]

Loop Through a List

colours = ["red", "green", "blue"]

for colour in colours:
    print("Colour:", colour)

Output:

Colour: red
Colour: green
Colour: blue

🔷 Example Category 4: Dictionaries

Create and Access

student = {
    "name": "Bob",
    "age": 20,
    "grade": "A"
}

print(student["name"])
print(student["grade"])

Output:

Bob
A

Loop Through a Dictionary

person = {"name": "Carol", "city": "Paris", "job": "Engineer"}

for key, value in person.items():
    print(key, "→", value)

Output:

name → Carol
city → Paris
job → Engineer

🔷 Example Category 5: Conditionals (if/elif/else)

Basic If-Else

temperature = 35

if temperature > 30:
    print("It's hot outside!")
elif temperature > 20:
    print("It's warm.")
else:
    print("It's cool.")

Output:

It's hot outside!

Grade Calculator

score = 78

if score >= 90:
    grade = "A"
elif score >= 80:
    grade = "B"
elif score >= 70:
    grade = "C"
elif score >= 60:
    grade = "D"
else:
    grade = "F"

print("Grade:", grade)

Output:

Grade: C

🔷 Example Category 6: Loops

`for` Loop with `range()`

for i in range(1, 6):
    print("Step", i)

Output:

Step 1
Step 2
Step 3
Step 4
Step 5

`while` Loop

count = 0

while count < 5:
    print("Count:", count)
    count += 1  # count = count + 1

Output:

Count: 0
Count: 1
Count: 2
Count: 3
Count: 4

Loop with `break` and `continue`

for num in range(1, 11):
    if num == 5:
        continue       # Skip number 5
    if num == 8:
        break          # Stop at number 8
    print(num)

Output:

🔷 Example Category 7: Functions

Basic Function

def greet(name):
    print("Hello,", name + "!")

greet("Alice")
greet("Bob")

Output:

Hello, Alice!
Hello, Bob!

Function with Return Value

def add(a, b):
    return a + b

result = add(10, 25)
print("Sum:", result)

Output:

Sum: 35

Function with Default Parameter

def greet(name, message="Welcome!"):
    print(name + ":", message)

greet("Alice")
greet("Bob", "Nice to meet you!")

Output:

Alice: Welcome!
Bob: Nice to meet you!

🔷 Example Category 8: Lambda Functions

A lambda is a small, anonymous (nameless) one-line function.

# Regular function
def square(x):
    return x * x

# Equivalent lambda
square_lambda = lambda x: x * x

print(square(5))         # Output: 25
print(square_lambda(5))  # Output: 25

Lambdas are useful when you need a quick, short function without defining a full def block.

🔷 Example Category 9: Classes and Objects

Define a Class

class Dog:
    def __init__(self, name, breed):
        self.name = name
        self.breed = breed

    def bark(self):
        print(self.name, "says: Woof!")

# Create objects from the class
dog1 = Dog("Rex", "Labrador")
dog2 = Dog("Bella", "Poodle")

dog1.bark()
dog2.bark()
print(dog1.breed)

Output:

Rex says: Woof!
Bella says: Woof!
Labrador

🔷 Example Category 10: File Handling

Write to a File

with open("notes.txt", "w") as file:
    file.write("Hello, this is a note.\n")
    file.write("Python file handling is easy!")

Read from a File

with open("notes.txt", "r") as file:
    content = file.read()
    print(content)

Output:

Hello, this is a note.
Python file handling is easy!

Part 3: Python Exercises

Exercises are how you truly learn. Reading is not enough — you must write code yourself.

The following exercises are arranged by difficulty: beginner → intermediate → applied.

✏️ Exercise Set 1: Variables and Print

Exercise 1.1 — Store and Display Your Info

Objective: Create variables for your name, age, and favourite colour and print them in a sentence.

Expected Output:

My name is Sam, I am 22 years old, and my favourite colour is blue.

Starter Template:

name = ___
age = ___
colour = ___

print("My name is", ___, "I am", ___, "years old, and my favourite colour is", ___ + ".")

Solution:

name = "Sam"
age = 22
colour = "blue"

print("My name is", name + ", I am", age, "years old, and my favourite colour is", colour + ".")

Exercise 1.2 — Calculate Your Birth Year

current_year = 2025
age = int(input("How old are you? "))
birth_year = current_year - age
print("You were probably born in", birth_year)

Example Interaction:

How old are you? 20
You were probably born in 2005

✏️ Exercise Set 2: String Exercises

Exercise 2.1 — Reverse and Check Palindrome

Task: Ask the user to enter a word. Check if it is a palindrome.

word = input("Enter a word: ").lower()

if word == word[::-1]:
    print(word, "is a palindrome.")
else:
    print(word, "is not a palindrome.")

Example 1:

Enter a word: level
level is a palindrome.

Example 2:

Enter a word: hello
hello is not a palindrome.

Exercise 2.2 — Count Vowels in a String

text = input("Enter a sentence: ")
vowels = "aeiouAEIOU"
count = 0

for char in text:
    if char in vowels:
        count += 1

print("Number of vowels:", count)

Example:

Enter a sentence: Hello World
Number of vowels: 3

✏️ Exercise Set 3: List Exercises

Exercise 3.1 — Find the Largest Number

numbers = [45, 12, 78, 34, 90, 23]

largest = numbers[0]

for num in numbers:
    if num > largest:
        largest = num

print("Largest number:", largest)

Output:

Largest number: 90

Exercise 3.2 — Remove Duplicates and Sort

data = [5, 3, 8, 3, 1, 5, 9, 1, 7]

unique_data = list(set(data))
unique_data.sort()

print("Cleaned and sorted:", unique_data)

Output:

Cleaned and sorted: [1, 3, 5, 7, 8, 9]

Exercise 3.3 — Sum and Average of a List

scores = [85, 92, 76, 88, 91, 67, 73]

total = sum(scores)
average = total / len(scores)

print("Total:", total)
print("Average:", round(average, 2))

Output:

Total: 572
Average: 81.71

✏️ Exercise Set 4: Loop Exercises

Exercise 4.1 — Multiplication Table

number = int(input("Enter a number for its multiplication table: "))

for i in range(1, 11):
    print(number, "x", i, "=", number * i)

Example (number = 7):

x 1 = 7
x 2 = 14
...
x 10 = 70

Exercise 4.2 — FizzBuzz (Classic Interview Challenge)

Rules:

For multiples of 3, print "Fizz"
For multiples of 5, print "Buzz"
For multiples of both, print "FizzBuzz"
Otherwise, print the number

for i in range(1, 31):
    if i % 3 == 0 and i % 5 == 0:
        print("FizzBuzz")
    elif i % 3 == 0:
        print("Fizz")
    elif i % 5 == 0:
        print("Buzz")
    else:
        print(i)

Output (first 15 lines):

1
2
Fizz
4
Buzz
Fizz
7
8
Fizz
Buzz
11
Fizz
13
14
FizzBuzz

✏️ Exercise Set 5: Function Exercises

Exercise 5.1 — Temperature Converter

def celsius_to_fahrenheit(celsius):
    return (celsius * 9/5) + 32

def fahrenheit_to_celsius(fahrenheit):
    return (fahrenheit - 32) * 5/9

print(celsius_to_fahrenheit(100))   # Expected: 212.0
print(fahrenheit_to_celsius(32))    # Expected: 0.0

Exercise 5.2 — Simple Calculator Function

def calculate(a, b, operation):
    if operation == "add":
        return a + b
    elif operation == "subtract":
        return a - b
    elif operation == "multiply":
        return a * b
    elif operation == "divide":
        if b != 0:
            return a / b
        else:
            return "Error: Cannot divide by zero"
    else:
        return "Unknown operation"

print(calculate(10, 5, "add"))       # 15
print(calculate(10, 5, "subtract"))  # 5
print(calculate(10, 5, "multiply"))  # 50
print(calculate(10, 5, "divide"))    # 2.0
print(calculate(10, 0, "divide"))    # Error: Cannot divide by zero

Part 4: Python Quiz — Test Your Knowledge

This quiz tests key Python concepts. Try answering each question before revealing the answer.

Quiz Question 1

What is the output of this code?

x = 10
y = 3
print(x % y)

Click to reveal answer

**Answer: `1`** The `%` operator is the **modulus** (remainder) operator. `10 ÷ 3 = 3 remainder 1`. So `10 % 3 = 1`.

Quiz Question 2

What does this code print?

text = "Hello"
print(text[1])

Click to reveal answer

**Answer: `e`** Python uses **zero-based indexing**. Index `0` = `H`, index `1` = `e`.

Quiz Question 3

What is the output?

numbers = [1, 2, 3, 4, 5]
print(numbers[-1])

Click to reveal answer

**Answer: `5`** Negative indexing counts from the end. `-1` refers to the **last** element, which is `5`.

Quiz Question 4

What does len("Python") return?

Click to reveal answer

**Answer: `6`** `len()` counts characters. `P-y-t-h-o-n` = 6 characters.

Quiz Question 5

What is the output?

for i in range(0, 10, 2):
    print(i)

Click to reveal answer

**Answer:** ``` 0 2 4 6 8 ``` `range(0, 10, 2)` starts at `0`, ends before `10`, and steps by `2` (every other number).

Quiz Question 6

True or False: In Python, a list can hold items of different data types.

Click to reveal answer

**Answer: TRUE** A Python list can contain strings, integers, floats, booleans, and even other lists: ```python mixed = [1, "hello", 3.14, True] ```

Quiz Question 7

What is the output?

def mystery(n):
    return n * 2 + 1

print(mystery(4))

Click to reveal answer

**Answer: `9`** `mystery(4)` = `4 * 2 + 1` = `8 + 1` = `9`.

Quiz Question 8

What does type(3.14) return?

Click to reveal answer

**Answer: `<class 'float'>`** `3.14` has a decimal point, making it a floating-point number (`float`).

Quiz Question 9

What is the output?

my_dict = {"a": 1, "b": 2, "c": 3}
print(my_dict["b"])

Click to reveal answer

**Answer: `2`** Dictionary values are accessed using their **key**. The key `"b"` maps to the value `2`.

Quiz Question 10

Which of these correctly defines a Python function?

A) function greet():
B) def greet():
C) define greet():
D) func greet():

Click to reveal answer

**Answer: B) `def greet():`** In Python, functions are defined using the keyword `def`.

Part 5: Python Challenges

Challenges are harder problems that require you to combine multiple concepts. These simulate real interview and problem-solving scenarios.

🏆 Challenge 1: Count Word Frequency

Problem: Given a sentence, count how many times each word appears.

sentence = "the cat sat on the mat the cat"

words = sentence.split()
frequency = {}

for word in words:
    if word in frequency:
        frequency[word] += 1
    else:
        frequency[word] = 1

for word, count in frequency.items():
    print(word, ":", count)

Output:

the : 3
cat : 2
sat : 1
on : 1
mat : 1

🏆 Challenge 2: Fibonacci Sequence

Problem: Print the first n numbers of the Fibonacci sequence.

What is Fibonacci? Each number is the sum of the two before it: 0, 1, 1, 2, 3, 5, 8, 13, 21…

def fibonacci(n):
    a, b = 0, 1
    result = []
    for _ in range(n):
        result.append(a)
        a, b = b, a + b
    return result

print(fibonacci(10))

Output:

[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

🏆 Challenge 3: Check Prime Number

Problem: Write a function that returns True if a number is prime, otherwise False.

Prime number: A number divisible only by 1 and itself (e.g., 2, 3, 5, 7, 11).

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

# Test it
for num in range(2, 20):
    if is_prime(num):
        print(num, "is prime")

Output:

is prime
is prime
is prime
is prime
is prime
is prime
is prime
is prime

🏆 Challenge 4: Flatten a Nested List

Problem: Given a list containing sublists, create a single flat list.

nested = [[1, 2, 3], [4, 5], [6, 7, 8, 9]]

flat = []
for sublist in nested:
    for item in sublist:
        flat.append(item)

print(flat)

Output:

[1, 2, 3, 4, 5, 6, 7, 8, 9]

One-liner using list comprehension:

flat = [item for sublist in nested for item in sublist]
print(flat)

🏆 Challenge 5: Anagram Checker

Problem: Check if two words are anagrams (contain the same letters).

def are_anagrams(word1, word2):
    return sorted(word1.lower()) == sorted(word2.lower())

print(are_anagrams("listen", "silent"))   # True
print(are_anagrams("hello", "world"))     # False
print(are_anagrams("Triangle", "Integral"))  # True

Output:

True
False
True

sorted() converts a string to a sorted list of characters. If two words have the same characters in the same frequency, their sorted forms are identical.

🏆 Challenge 6: Find Missing Number

Problem: Given a list of numbers from 1 to n with one missing, find the missing number.

def find_missing(numbers, n):
    expected_sum = n * (n + 1) // 2
    actual_sum = sum(numbers)
    return expected_sum - actual_sum

nums = [1, 2, 4, 5, 6, 7, 8, 9, 10]
print("Missing number:", find_missing(nums, 10))

Output:

Missing number: 3

How it works: The formula n*(n+1)/2 gives the sum of all numbers from 1 to n. Subtract the actual sum to find the gap.

Part 6: Python Practice — Building Real Skills

Practice is deliberate repetition with feedback. This section gives you structured practice tasks.

📝 Practice Task 1: Student Grade Tracker

Scenario: You are building a simple grade-tracking tool for a teacher.

students = {}

# Add students and their scores
students["Alice"] = [85, 92, 78, 90]
students["Bob"] = [70, 65, 80, 75]
students["Carol"] = [95, 98, 92, 97]

# Calculate and display averages
for name, scores in students.items():
    average = sum(scores) / len(scores)
    print(name + ":", round(average, 1), "average")

Output:

Alice: 86.2 average
Bob: 72.5 average
Carol: 95.5 average

Enhancement: Add a grade letter (A/B/C/D/F) based on the average.

📝 Practice Task 2: Shopping Cart Calculator

cart = {
    "Milk": 1.50,
    "Bread": 2.00,
    "Eggs": 3.25,
    "Butter": 4.75,
    "Coffee": 6.99
}

total = 0
print("=== Shopping Cart ===")

for item, price in cart.items():
    print(f"  {item}: £{price:.2f}")
    total += price

tax = total * 0.20  # 20% VAT
grand_total = total + tax

print("-------------------")
print(f"Subtotal: £{total:.2f}")
print(f"Tax (20%): £{tax:.2f}")
print(f"Total: £{grand_total:.2f}")

Output:

=== Shopping Cart ===
  Milk: £1.50
  Bread: £2.00
  Eggs: £3.25
  Butter: £4.75
  Coffee: £6.99
-------------------
Subtotal: £18.49
Tax (20%): £3.70
Total: £22.19

📝 Practice Task 3: Number Guessing Game

import random

secret = random.randint(1, 100)
attempts = 0

print("Guess the number between 1 and 100!")

while True:
    guess = int(input("Your guess: "))
    attempts += 1

    if guess < secret:
        print("Too low! Try higher.")
    elif guess > secret:
        print("Too high! Try lower.")
    else:
        print(f"Correct! You got it in {attempts} attempts.")
        break

Part 7: Python Syllabus — Your Complete Learning Map

This is the official map of all Python topics, organised from beginner to advanced. Use this as your learning checklist.

📚 Module 1: Python Basics (Beginner)

#	Topic	Description
1.1	Python Introduction	What Python is, where it’s used, how to install
1.2	Python Syntax	Indentation, comments, basic structure
1.3	Variables	Creating and naming variables
1.4	Data Types	str, int, float, bool, list, dict, tuple, set
1.5	Numbers	Arithmetic operators: +, -, , /, //, %, *
1.6	Strings	Creating, slicing, formatting, methods
1.7	Booleans	True/False, comparison operators
1.8	Operators	Arithmetic, assignment, comparison, logical
1.9	Type Casting	Converting between types: int(), str(), float()
1.10	User Input	`input()`, reading from the keyboard

📚 Module 2: Control Flow

#	Topic	Description
2.1	If…Else	Conditional branching
2.2	elif	Multiple condition branches
2.3	Nested If	If inside if
2.4	While Loop	Repeat while a condition is True
2.5	For Loop	Iterate over sequences
2.6	range()	Generating number sequences
2.7	break	Exit a loop early
2.8	continue	Skip to next iteration
2.9	pass	Placeholder for empty code blocks

📚 Module 3: Data Structures

#	Topic	Description
3.1	Lists	Ordered, mutable sequences
3.2	Tuples	Ordered, immutable sequences
3.3	Sets	Unordered, unique collections
3.4	Dictionaries	Key-value pairs
3.5	List Comprehension	Compact list creation
3.6	Dictionary Comprehension	Compact dictionary creation

📚 Module 4: Functions

#	Topic	Description
4.1	Functions	Defining and calling functions
4.2	Arguments	Positional, keyword, default parameters
4.3	*args	Variable number of positional arguments
4.4	**kwargs	Variable number of keyword arguments
4.5	Return	Returning values from functions
4.6	Lambda	Anonymous one-line functions
4.7	Recursion	Functions that call themselves
4.8	Scope	Local vs global variables

📚 Module 5: Object-Oriented Programming (OOP)

#	Topic	Description
5.1	Classes	Blueprints for objects
5.2	Objects	Instances of a class
5.3	`__init__`	The constructor method
5.4	Methods	Functions inside a class
5.5	Inheritance	One class extending another
5.6	Polymorphism	Same method, different behaviour
5.7	Encapsulation	Hiding internal data
5.8	Abstraction	Hiding complexity
5.9	Iterators	Objects with `__iter__` and `__next__`

📚 Module 6: Working with Files and Errors

#	Topic	Description
6.1	File Open/Read	Reading files with `open()`
6.2	File Write	Writing to files
6.3	File Delete	Removing files with `os` module
6.4	Try/Except	Handling errors gracefully
6.5	Raise	Triggering custom errors
6.6	Finally	Code that always runs

📚 Module 7: Advanced Python

#	Topic	Description
7.1	Modules	Importing standard and third-party modules
7.2	Dates	Working with `datetime` module
7.3	Math	Using the `math` module
7.4	JSON	Parsing and generating JSON data
7.5	RegEx	Pattern matching with `re` module
7.6	PIP	Installing external packages
7.7	Virtual Environments	Isolated project environments
7.8	NumPy	Numerical computing library
7.9	Pandas	Data analysis library
7.10	Matplotlib	Data visualisation library

Part 8: Python Study Plan — Learn Python in 8 Weeks

This structured plan takes you from zero to confident Python programmer in 8 weeks, studying approximately 1–2 hours per day.

🗓️ Week 1: Python Foundations

Day	Topic	Goal
Day 1	Install Python + Write First Program	Print “Hello, World!”
Day 2	Variables & Data Types	Store and display different types of data
Day 3	Strings & String Methods	Manipulate text
Day 4	Numbers & Math Operations	Perform calculations
Day 5	User Input & Type Casting	Build interactive programs
Day 6	Review + Practice Exercises	Reinforce Week 1 topics
Day 7	Mini-Project: Personal Profile Printer	Apply all Week 1 concepts

Week 1 Mini-Project:

name = input("Enter your name: ")
age = int(input("Enter your age: "))
city = input("Enter your city: ")
hobby = input("Enter your hobby: ")

print("\n=== Your Profile ===")
print("Name:", name)
print("Age:", age)
print("City:", city)
print("Hobby:", hobby)
print("In 10 years you'll be:", age + 10)

🗓️ Week 2: Control Flow

Day	Topic	Goal
Day 8	If/Elif/Else	Make decisions in code
Day 9	Comparison & Logical Operators	Combine conditions
Day 10	For Loops + range()	Repeat actions
Day 11	While Loops	Loop with conditions
Day 12	Break, Continue, Pass	Control loop flow
Day 13	Review + Exercises	FizzBuzz, grade calculator
Day 14	Mini-Project: Number Guessing Game	Apply control flow

🗓️ Week 3: Data Structures

Day	Topic	Goal
Day 15	Lists (Create, Access, Modify)	Store ordered data
Day 16	List Methods	Sort, append, remove
Day 17	Tuples & Sets	Immutable and unique collections
Day 18	Dictionaries	Key-value data storage
Day 19	Nested Data Structures	Dicts inside lists, etc.
Day 20	Review + Exercises	Remove duplicates, word count
Day 21	Mini-Project: Student Grade Tracker	Use lists and dicts

🗓️ Week 4: Functions

Day	Topic	Goal
Day 22	Defining Functions	Create reusable code blocks
Day 23	Arguments & Return	Pass data in and out
Day 24	Default & Keyword Args	Flexible function calls
Day 25	args and *kwargs	Variable arguments
Day 26	Lambda Functions	Quick anonymous functions
Day 27	Scope & Recursion	Understand variable visibility
Day 28	Mini-Project: Calculator App	Build a functional tool

🗓️ Week 5: Object-Oriented Programming

Day	Topic	Goal
Day 29	Classes and Objects	Create blueprints and instances
Day 30	`__init__` and Methods	Object initialisation
Day 31	Inheritance	Extend existing classes
Day 32	Polymorphism	Flexible method behaviour
Day 33	Encapsulation & Properties	Protect data
Day 34	Review + Exercises	Build a Bank Account class
Day 35	Mini-Project: Library System	Books, Members, Borrowing

🗓️ Week 6: Files, Errors & Modules

Day	Topic	Goal
Day 36	Reading & Writing Files	Persist data to disk
Day 37	Try/Except Error Handling	Prevent crashes
Day 38	Standard Library Modules	math, random, datetime, os
Day 39	JSON Data	Read/write structured data
Day 40	RegEx Basics	Pattern matching in strings
Day 41	Review + Practice	File-based programs
Day 42	Mini-Project: CSV Contact Book	Store contacts in a file

🗓️ Week 7: Intermediate Python

Day	Topic	Goal
Day 43	List Comprehensions	Compact list operations
Day 44	Dictionary Comprehensions	Compact dictionary operations
Day 45	Generators & Iterators	Memory-efficient sequences
Day 46	Decorators	Modify function behaviour
Day 47	Context Managers (with)	Resource management
Day 48	Virtual Environments & PIP	Manage packages
Day 49	Mini-Project: Data Filter Tool	Process and clean data

🗓️ Week 8: Real-World Python Applications

Day	Topic	Goal
Day 50	NumPy Basics	Arrays and maths
Day 51	Pandas Basics	DataFrames and data analysis
Day 52	Matplotlib Basics	Charts and visualisations
Day 53	Web Scraping (requests + BeautifulSoup)	Collect web data
Day 54	APIs (requests library)	Connect to external services
Day 55	Introduction to Flask	Build a simple web app
Day 56	Final Capstone Project	Build a complete data-driven app

Part 9: Python Interview Questions — Be Job-Ready

These are the most commonly asked Python questions in technical job interviews. Each includes a clear answer and code where relevant.

🎯 Beginner-Level Interview Questions

Q1: What is Python and what are its main uses?

Answer: Python is a high-level, interpreted, general-purpose programming language created by Guido van Rossum in 1991. It is known for its clean, readable syntax.

Main uses:

Web development (Django, Flask)
Data analysis (Pandas, NumPy)
Machine learning (TensorFlow, scikit-learn)
Automation and scripting
Scientific computing
Game development
Cybersecurity

Q2: What are Python’s key features?

Answer:

Readable syntax — looks almost like plain English
Interpreted — runs line by line, no need to compile
Dynamically typed — no need to declare variable types
Object-oriented — supports classes and objects
Large standard library — hundreds of built-in modules
Cross-platform — runs on Windows, Mac, Linux
Open-source — free to use and distribute

Q3: What is the difference between a list and a tuple?

Feature	List	Tuple
Syntax	`[1, 2, 3]`	`(1, 2, 3)`
Mutable?	✅ Yes (can change)	❌ No (fixed)
Speed	Slower	Faster
Use case	Data that changes	Fixed data (coordinates, config)

my_list = [1, 2, 3]
my_list[0] = 99    # Works fine

my_tuple = (1, 2, 3)
my_tuple[0] = 99   # TypeError! Tuples are immutable

Q4: What is the difference between `==` and `is`?

== checks if two values are equal
is checks if two variables point to the same object in memory

a = [1, 2, 3]
b = [1, 2, 3]

print(a == b)   # True  (same values)
print(a is b)   # False (different objects in memory)

c = a
print(a is c)   # True  (same object)

Q5: What are Python’s built-in data types?

Category	Types
Text	`str`
Numeric	`int`, `float`, `complex`
Sequence	`list`, `tuple`, `range`
Mapping	`dict`
Set	`set`, `frozenset`
Boolean	`bool`
Binary	`bytes`, `bytearray`
None	`NoneType`

Q6: What is `None` in Python?

None is Python’s way of representing nothing or no value. It is similar to null in other languages.

result = None

if result is None:
    print("No result yet")

Q7: What is a dictionary in Python?

A dictionary stores key-value pairs. Keys must be unique and immutable. Values can be anything.

person = {
    "name": "Alice",
    "age": 30,
    "job": "Engineer"
}

print(person["name"])     # Alice
print(person.get("age"))  # 30
person["city"] = "London" # Add a new key

Q8: Explain the difference between `append()` and `extend()`

append() adds one item to the end of a list
extend() adds all items from another iterable

list1 = [1, 2, 3]
list1.append([4, 5])
print(list1)   # [1, 2, 3, [4, 5]] ← nested list added

list2 = [1, 2, 3]
list2.extend([4, 5])
print(list2)   # [1, 2, 3, 4, 5] ← items added individually

Q9: What is a list comprehension?

A compact way to create a new list from an existing one.

Traditional loop:

squares = []
for x in range(1, 6):
    squares.append(x ** 2)

List comprehension:

squares = [x ** 2 for x in range(1, 6)]
print(squares)   # [1, 4, 9, 16, 25]

Q10: What is the difference between `break`, `continue`, and `pass`?

# break: exits the loop entirely
for i in range(10):
    if i == 5:
        break
    print(i)        # Prints 0 1 2 3 4

# continue: skips the current iteration, continues loop
for i in range(10):
    if i == 5:
        continue
    print(i)        # Prints 0 1 2 3 4 6 7 8 9

# pass: does nothing, placeholder
for i in range(3):
    pass            # No output, but no error either

🎯 Intermediate Interview Questions

Q11: What is a lambda function?

A lambda is a small, unnamed function defined with the lambda keyword.

double = lambda x: x * 2
print(double(5))    # 10

# Common use: sort a list of tuples by second element
pairs = [(1, 'b'), (3, 'a'), (2, 'c')]
pairs.sort(key=lambda pair: pair[1])
print(pairs)   # [(3, 'a'), (1, 'b'), (2, 'c')]

Q12: What is the difference between `*args` and `**kwargs`?

*args — collects extra positional arguments into a tuple
**kwargs — collects extra keyword arguments into a dictionary

def show_args(*args):
    for item in args:
        print(item)

show_args(1, 2, 3)   # Prints 1, 2, 3 each on a new line

def show_kwargs(**kwargs):
    for key, value in kwargs.items():
        print(key, "=", value)

show_kwargs(name="Alice", age=25)
# name = Alice
# age = 25

Q13: What is a Python decorator?

A decorator is a function that wraps another function to modify its behaviour without changing its code.

def my_decorator(func):
    def wrapper():
        print("Before the function runs")
        func()
        print("After the function runs")
    return wrapper

@my_decorator
def say_hello():
    print("Hello!")

say_hello()

Output:

Before the function runs
Hello!
After the function runs

Q14: What is the difference between shallow copy and deep copy?

Shallow copy: Copies the outer object but shares inner objects
Deep copy: Copies everything, including nested objects

import copy

original = [[1, 2], [3, 4]]

shallow = copy.copy(original)
deep = copy.deepcopy(original)

original[0][0] = 99

print(original)   # [[99, 2], [3, 4]]
print(shallow)    # [[99, 2], [3, 4]] ← affected!
print(deep)       # [[1, 2], [3, 4]]  ← NOT affected

Q15: What is a Python generator?

A generator produces values one at a time using yield instead of return. It is memory-efficient for large datasets.

def count_up(n):
    for i in range(n):
        yield i

gen = count_up(5)

for value in gen:
    print(value)

Output:

Unlike a list, which creates all values at once, a generator creates each value only when needed.

Q16: Explain inheritance in Python.

Inheritance allows a child class to inherit attributes and methods from a parent class.

class Animal:
    def __init__(self, name):
        self.name = name

    def speak(self):
        print(self.name, "makes a sound")

class Dog(Animal):    # Dog inherits from Animal
    def speak(self):  # Override parent method
        print(self.name, "says: Woof!")

class Cat(Animal):
    def speak(self):
        print(self.name, "says: Meow!")

dog = Dog("Rex")
cat = Cat("Whiskers")

dog.speak()   # Rex says: Woof!
cat.speak()   # Whiskers says: Meow!

Q17: What is the GIL (Global Interpreter Lock)?

The GIL is a mutex (lock) in CPython (the standard Python implementation) that allows only one thread to execute Python bytecode at a time. This means Python threads do not run truly in parallel for CPU-bound tasks.

Impact:

Good for I/O-bound tasks (file reading, web requests)
Limiting for CPU-heavy parallel computing
Solution: Use multiprocessing module for true parallelism

Q18: What is the difference between `range()` and `xrange()`?

In Python 3, xrange() no longer exists. range() behaves like Python 2’s xrange().
range() in Python 3 is lazy — it generates numbers on demand without creating a full list in memory.

r = range(1_000_000)  # Does NOT create 1 million numbers in memory immediately
print(r[0])   # 0 — only computed when accessed

Q19: How does Python manage memory?

Python uses:

Reference counting — tracks how many variables point to an object
Garbage collection — cleans up objects with circular references
Memory pools — reuses freed memory for efficiency

When no variable references an object, Python automatically frees its memory.

Q20: What is `init` in a class?

__init__ is the constructor method — it runs automatically when an object is created from a class.

class Car:
    def __init__(self, brand, model, year):
        self.brand = brand
        self.model = model
        self.year = year

    def describe(self):
        print(f"{self.year} {self.brand} {self.model}")

car1 = Car("Toyota", "Corolla", 2022)
car1.describe()   # 2022 Toyota Corolla

Part 10: Mini-Project — Python Practice Consolidator

This mini-project combines everything from this lesson into one working program.

🛠️ Project: Student Data Processor

Goal: Build a program that:

Stores student names and their test scores
Removes any duplicate names
Calculates averages
Assigns letter grades
Displays a formatted report
Finds the top student

Stage 1: Setup Data

# Stage 1: Create student data
students_raw = [
    {"name": "Alice", "scores": [85, 92, 78]},
    {"name": "Bob", "scores": [70, 65, 80]},
    {"name": "Carol", "scores": [95, 98, 91]},
    {"name": "Alice", "scores": [85, 92, 78]},   # duplicate
    {"name": "Dave", "scores": [60, 55, 70]},
]

print("Raw student count:", len(students_raw))

Output:

Raw student count: 5

Stage 2: Remove Duplicate Names

# Stage 2: Remove duplicate students by name
seen_names = []
students = []

for student in students_raw:
    if student["name"] not in seen_names:
        seen_names.append(student["name"])
        students.append(student)

print("After removing duplicates:", len(students), "students")

Output:

After removing duplicates: 4 students

Stage 3: Calculate Averages and Assign Grades

# Stage 3: Add average and grade to each student
def get_grade(average):
    if average >= 90:
        return "A"
    elif average >= 80:
        return "B"
    elif average >= 70:
        return "C"
    elif average >= 60:
        return "D"
    else:
        return "F"

for student in students:
    avg = sum(student["scores"]) / len(student["scores"])
    student["average"] = round(avg, 1)
    student["grade"] = get_grade(avg)

Stage 4: Display Report

# Stage 4: Print formatted report
print("\n" + "="*40)
print("      STUDENT PERFORMANCE REPORT")
print("="*40)

for student in students:
    print(f"Name:    {student['name']}")
    print(f"Scores:  {student['scores']}")
    print(f"Average: {student['average']}")
    print(f"Grade:   {student['grade']}")
    print("-"*40)

Stage 5: Find Top Student

# Stage 5: Find top performer
top_student = max(students, key=lambda s: s["average"])

print(f"\n🏆 Top Student: {top_student['name']}")
print(f"   Average: {top_student['average']} | Grade: {top_student['grade']}")

Complete Output:

Raw student count: 5
After removing duplicates: 4 students

========================================
      STUDENT PERFORMANCE REPORT
========================================
Name:    Alice
Scores:  [85, 92, 78]
Average: 85.0
Grade:   B
----------------------------------------
Name:    Bob
Scores:  [70, 65, 80]
Average: 71.7
Grade:   C
----------------------------------------
Name:    Carol
Scores:  [95, 98, 91]
Average: 94.7
Grade:   A
----------------------------------------
Name:    Dave
Scores:  [60, 55, 70]
Average: 61.7
Grade:   D
----------------------------------------

🏆 Top Student: Carol
   Average: 94.7 | Grade: A

Optional Enhancements

Save the report to a .txt file
Add a function to reverse each student’s name (practice string reversal)
Sort students by average (highest first)
Calculate class average across all students
Count how many students passed (grade C or above)

Part 11: Common Beginner Mistakes

These are the most frequent errors Python beginners make. Learn them now to avoid frustration later.

Mistake 1: Forgetting to Convert Input

# ❌ Wrong
age = input("Enter age: ")
next_year = age + 1        # TypeError: can only concatenate str (not "int") to str

# ✅ Correct
age = int(input("Enter age: "))
next_year = age + 1
print(next_year)

Mistake 2: Off-by-One in range()

# ❌ Wrong — wants to print 1 to 10 but...
for i in range(10):
    print(i)    # Prints 0 to 9! Not 1 to 10.

# ✅ Correct
for i in range(1, 11):
    print(i)    # Prints 1 to 10

Mistake 3: Modifying a List While Iterating

# ❌ Wrong — skips items
nums = [1, 2, 3, 4, 5]
for n in nums:
    if n % 2 == 0:
        nums.remove(n)   # Unexpected behaviour

# ✅ Correct — iterate over a copy
nums = [1, 2, 3, 4, 5]
for n in nums[:]:
    if n % 2 == 0:
        nums.remove(n)

print(nums)   # [1, 3, 5]

Mistake 4: Using `=` Instead of `==` in Conditions

# ❌ Wrong — assignment, not comparison
if x = 5:     # SyntaxError

# ✅ Correct — comparison
if x == 5:
    print("x is 5")

Mistake 5: Forgetting Indentation

# ❌ Wrong
def greet():
print("Hello")   # IndentationError

# ✅ Correct
def greet():
    print("Hello")

Python uses indentation (spaces or tabs) to define code blocks. This is mandatory, not optional.

Mistake 6: Confusing Append vs Extend

# ❌ If you want to add items individually
my_list = [1, 2, 3]
my_list.append([4, 5])     # Adds a nested list: [1, 2, 3, [4, 5]]

# ✅ Use extend to add items individually
my_list = [1, 2, 3]
my_list.extend([4, 5])     # Adds items: [1, 2, 3, 4, 5]

Mistake 7: Mutable Default Arguments in Functions

# ❌ Wrong — the list persists between calls!
def add_item(item, lst=[]):
    lst.append(item)
    return lst

print(add_item("a"))   # ['a']
print(add_item("b"))   # ['a', 'b'] ← Bug! Expected ['b']

# ✅ Correct — use None as default
def add_item(item, lst=None):
    if lst is None:
        lst = []
    lst.append(item)
    return lst

Part 12: Reflection Questions

Use these questions to deepen your understanding. Write your answers in a notebook or try them in code.

What are three different ways to remove duplicates from a list? When would you choose one over another?
What is the difference between text[::-1] and reversed(text)? When might you prefer each?
Why does Python’s input() always return a string? What would happen if you forgot to convert it?
You have a list [1, 2, 3, 4, 5]. What does my_list[-2] return? Why?
What is the difference between a for loop and a while loop? Give a real-world example where each is more appropriate.
Why might a set be faster than a list for checking if an item exists?
In an interview, you are asked to write FizzBuzz. What is your approach? Write it out.
What is the purpose of __init__ in a class? What would happen if you left it out?
If you were building a data analysis pipeline, which Python libraries would you use and why?
Looking at the 8-week study plan: which week do you think will be the hardest for you? Why? What can you do to prepare?

Part 13: Completion Checklist

Use this checklist to confirm you have mastered Lesson 45.

Lesson Summary

Congratulations! You have completed Lesson 45 — the most comprehensive consolidation lesson of your Python journey.

Here is everything you covered:

Section	What You Learned
How-To: Remove Duplicates	`set()`, loops, `dict.fromkeys()`
How-To: Reverse a String	`[::-1]`, `reversed()`, loops, palindrome check
How-To: Add Two Numbers	`input()`, `int()`, `float()`, `sum()`
Python Examples	Strings, lists, dicts, loops, functions, classes, files
Exercises	Variables, strings, lists, loops, functions
Quiz	10 knowledge-check questions with answers
Challenges	Word frequency, Fibonacci, prime check, anagram, missing number
Practice Tasks	Grade tracker, shopping cart, number game
Syllabus	Complete 7-module Python learning map
Study Plan	8-week schedule from beginner to advanced
Interview Questions	20 beginner-to-intermediate Q&As
Mini-Project	Student Data Processor combining all concepts
Common Mistakes	7 critical beginner errors with fixes

🎓 Next Steps:

Follow the 8-week study plan systematically

Practice on real coding platforms (HackerRank, LeetCode, CodeWars)

Build personal projects that solve real problems you care about

Review Python documentation at docs.python.org

Keep this lesson as a reference — return to it whenever you need a reminder

You now have the knowledge, the practice, the roadmap, and the interview preparation to move forward as a confident Python programmer. The rest is practice. Write code every day, even if it’s just 15 minutes. Small daily actions build unstoppable skill.

Lesson 45: Python How-Tos, Examples, Exercises, Challenges, Syllabus, Study Plan & Interview Prep

📖 Lesson Introduction

🔑 Prerequisite Concepts

Part 1: Python How-To Guides

🔷 How-To 1: Remove Duplicates from a List

What is a duplicate?

Why remove duplicates?

Method 1: Using a set()

Breaking Down the Code Line by Line

Method 2: Using a Loop (Preserves Order)

Line-by-Line Explanation

Method 3: Using dict.fromkeys() (Preserves Order, Very Compact)

🤔 Thinking Prompt

🔁 Quick Recap — Remove Duplicates

🔷 How-To 2: Reverse a String

What does “reverse a string” mean?

Why would you reverse a string?

Method 1: Slicing (The Pythonic Way)

Understanding [::-1] Step by Step

Method 2: Using the reversed() Function

Method 3: Using a Loop (Good for Understanding)

Palindrome Check — Practical Application

🤔 Thinking Prompt

🔷 How-To 3: Add Two Numbers

What does “add two numbers” mean in Python?

Adding Numbers Entered by the User

Why Must We Convert with int()?

Adding Floating-Point (Decimal) Numbers

Adding Numbers in a List

Part 2: Python Examples — Core Concepts Demonstrated

🔷 Example Category 1: Print and Variables

Printing Text

Storing and Using Variables

Multiple Data Types

🔷 Example Category 2: String Operations

String Length

Uppercase and Lowercase

String Slicing

String Replacement

String Split

🔷 Example Category 3: Lists

Create and Access a List

Add, Remove, Sort

Loop Through a List

🔷 Example Category 4: Dictionaries

Create and Access

Loop Through a Dictionary

🔷 Example Category 5: Conditionals (if/elif/else)

Basic If-Else

Grade Calculator

🔷 Example Category 6: Loops

for Loop with range()

while Loop

Loop with break and continue

🔷 Example Category 7: Functions

Basic Function

Function with Return Value

Function with Default Parameter

🔷 Example Category 8: Lambda Functions

🔷 Example Category 9: Classes and Objects

Define a Class

🔷 Example Category 10: File Handling

Write to a File

Read from a File

Part 3: Python Exercises

✏️ Exercise Set 1: Variables and Print

Exercise 1.1 — Store and Display Your Info

Exercise 1.2 — Calculate Your Birth Year

✏️ Exercise Set 2: String Exercises

Exercise 2.1 — Reverse and Check Palindrome

Exercise 2.2 — Count Vowels in a String

✏️ Exercise Set 3: List Exercises

Exercise 3.1 — Find the Largest Number

Exercise 3.2 — Remove Duplicates and Sort

Exercise 3.3 — Sum and Average of a List

✏️ Exercise Set 4: Loop Exercises

Exercise 4.1 — Multiplication Table

Exercise 4.2 — FizzBuzz (Classic Interview Challenge)

✏️ Exercise Set 5: Function Exercises

Exercise 5.1 — Temperature Converter

Method 1: Using a `set()`

Method 3: Using `dict.fromkeys()` (Preserves Order, Very Compact)

Understanding `[::-1]` Step by Step

Method 2: Using the `reversed()` Function

Why Must We Convert with `int()`?

`for` Loop with `range()`

`while` Loop

Loop with `break` and `continue`

Q4: What is the difference between `==` and `is`?

Q6: What is `None` in Python?

Q8: Explain the difference between `append()` and `extend()`

Q10: What is the difference between `break`, `continue`, and `pass`?

Q12: What is the difference between `*args` and `**kwargs`?

Q18: What is the difference between `range()` and `xrange()`?

Q20: What is `init` in a class?