Learn to Code Python For Beginners

Welcome to this blog, where we will Learn to code Python from scratch. This blog will contain tutorials in the form of lessons, notes, and videos.

Table of Contents

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Learn to Code Python – Part 1 – Basics

📘 Section 1: What is Python and Programming?

💡 Programming

Programming means giving step-by-step instructions to a computer to perform a task — like telling a robot what to do.

💡 Python

Python is a simple and powerful programming language used in web development, data science, automation, and more.

⚙️ Section 2: Setting Up Python

✅ Step 1: Install Python

You need Python installed to write and run your code. Download it from python.org.

✅ Step 2: Install VS Code

VS Code is a code editor where you write Python scripts. It makes coding easier with suggestions, error checks, and a terminal.

🧠 Section 3: Python Basics

📌 Data Types

Python has different types of data, just like real life:

Type	Example	Python Name
Text	`"hello"`	`str`
Number	`24`	`int`
Decimal	`59.7`	`float`
True/False	`True` or `False`	`bool`

📌 Variables

Variables are containers to store data.

name = "Kunal"
age = 24
is_married = False

Think of a cup holding water – the cup is the variable, and the water is the data.

🧪 Section 4: Type, Input, and Conversion

📌 `type()` Function

This shows what type of data a variable has.

print(type(name))  # str
print(type(age))   # int

📌 Getting User Input

We can ask users questions and store their answers.

name = input("What is your name? ")

🔁 Section 5: Data Conversion and F-Strings

📌 Type Conversion

Sometimes we need to change data from one type to another.

age = int(input("Enter your age: "))

📌 f-Strings

This is a cool way to write dynamic messages:

name = "Kunal"
age = 24
print(f"My name is {name} and I am {age} years old.")

🧮 Section 6: Lists and Operators

📌 Operators in Python

Operators are used to perform calculations:

Symbol	Meaning	Example
`+`	Addition	`2 + 3 = 5`
`-`	Subtraction	`5 - 2 = 3`
`*`	Multiplication	`3 * 2 = 6`
`/`	Division	`6 / 3 = 2.0`
`//`	Floor Division	`7 // 2 = 3`
`%`	Modulus	`7 % 2 = 1`

📌 Lists

A list is a collection that can store multiple values:

fruits = ["apple", "banana", "mango"]
print(fruits[0])  # apple

Lists can even store different data types.

🧠 Recap of What You’ve Learned in Part 1:

What programming is and why Python is a great choice
How to install Python and VS Code
Variables and Data Types
Checking data types using type()
Taking user input
Converting data types
Using f-strings for clean output
Working with lists and operators

Learn to Code Python – Part 2 – Operators

Welcome back, future Python masters! 👋 In Part 1 of our Python journey, we learned the basics — variables, data types, user input, and printing values. Now, it’s time to unlock the real power of Python by mastering operators.

In this post, we’ll break down operators step-by-step with simple examples, real-life analogies, and mini challenges to help you understand and apply them confidently.

🧠 What Are Operators?

Operators are symbols used to perform operations on values and variables.
Think of them like calculator buttons — they help you do math, compare values, and more.

🧮 Lesson 1: Arithmetic Operators

Operators Covered: +, -, *, /

These are basic math operations used in almost every program.

x = 20
y = 4

print(x + y)   # 24
print(x - y)   # 16
print(x * y)   # 80
print(x / y)   # 5.0

📌 Output:

🔢 Lesson 2: Floor Division and Modulus

Operators Covered: //, %

// → Returns only the whole number part (floor division)
% → Gives the remainder after division (modulus)

print(7 // 2)   # 3
print(7 % 2)    # 1

📌 Output:

3
1

🧃 Example: Sharing 7 chocolates with 2 friends — each gets 3, and 1 is left.

⚡ Lesson 3: Power Operator

Operator Covered: **

Used for exponentiation — multiplying a number by itself multiple times.

print(2 ** 3)   # 8 (2×2×2)
print(5 ** 2)   # 25 (5×5)

📌 Output:

8
25

🎯 Try It: Calculate the cube of a number using **.

🤝 Lesson 4: Comparison Operators

Operators: ==, !=, >, <, >=, <=

These return True or False when comparing values.

a = 10
b = 20

print(a == b)  # False
print(a < b)   # True

📌 Output:

False
True

Use them in conditions — like checking age for voting eligibility.

📦 Lesson 5: Assignment Operators

Operators: =, +=, -=, *=, /=

These update variable values in short form.

x = 10
x += 5
print(x)    # 15

x *= 2
print(x)    # 20

📌 Output:

15
20

🍜 Analogy: Like adding more food to your plate without changing plates.

🔐 Lesson 6: Logical Operators

Operators: and, or, not

Used for combining conditions in if statements or filters.

a = True
b = False

print(a and b)  # False
print(a or b)   # True
print(not a)    # False

📌 Output:

False
True
False

Logical AND (and)

condition_1	condition_2	condition_1 and condition_2
True	True	True
True	False	False
False	True	False
False	False	False

Logical OR (or)

condition_1	condition_2	condition_1 or condition_2
True	True	True
True	False	True
False	True	True
False	False	False

Logical NOT (not condition_1)

condition_1	not condition_1
True	False
False	True

Try this:

age = 18
is_student = True
print(age > 17 and is_student)  # True

🧬 Lesson 7: Identity & Membership Operators

Operators: is, is not, in, not in

is: Checks if two variables point to the same object in memory.
in: Checks if a value exists in a list, string, etc.

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

print(a is b)      # True
print(2 in a)      # True

📌 Output:

True
True

🧮 Lesson 8: Operator Precedence

Like BODMAS in math, Python follows an order of operations.

print(2 + 3 * 4)     # 14
print((2 + 3) * 4)   # 20

📌 Output:

14
20

🎯 Always use parentheses () to control execution order clearly.

✅ Python Equivalent of PEMDAS

Python uses the same basic math precedence rules:

PEMDAS	Python Equivalent	Example
P	`()` Parentheses	`(2 + 3) * 4 → 20`
E	`**` Exponentiation	`2 ** 3 → 8`
MD	`*`, `/`, `//`, `%`	`10 / 2 * 3 → 15.0`
AS	`+`, `-`	`10 - 2 + 1 → 9`

📌 Note: Multiplication/Division and Addition/Subtraction are evaluated left to right, depending on which comes first.

🧪 Lesson 9: Mini Python Calculator (Project)

Let’s combine all your knowledge and build a simple calculator.

a = 10
b = 5
op = '*'

if op == '+':
    print(a + b)
elif op == '-':
    print(a - b)
elif op == '*':
    print(a * b)
elif op == '/':
    print(a / b)

📌 Output:

🎯 Try replacing op = '*' with other operators and see the results.

Learn to Code Python – Part 3 – Conditions

Welcome back! In this chapter, we’ll explore how Python makes decisions using conditional statements. These are essential when your program needs to choose between actions — just like we do in real life.

🔹 Lesson 1: Introduction to Conditional Statements

Think of conditional statements like making decisions in real life. For example:

“If I study, I will pass.”

In Python, we write:

marks = 80
if marks > 35:
    print('You passed!')

🖥️ Output:

You passed!

🔹 Lesson 2: `if`, `else`, `elif` in Action

Use if for one condition, else when it’s false, and elif for more choices.

marks = 70
if marks >= 90:
    print('A Grade')
elif marks >= 60:
    print('B Grade')
else:
    print('Fail')

🖥️ Output:

B Grade

🔹 Lesson 3: Nested Conditions

You can place one if inside another. Useful when one condition depends on another.

age = 20
has_id = True

if age >= 18:
    if has_id:
        print('Eligible')

🖥️ Output:

Eligible

🔹 Lesson 4: Logical Operators in Conditions

Use and, or, not to combine conditions in one line.

age = 22
has_pass = True

if age > 18 and has_pass:
    print('Allowed')

🖥️ Output:

Allowed

🔹 Lesson 5: `assert` Statement

assert is like saying: “This better be true!” It’s used to catch bugs.

age = 25
assert age > 0, 'Age must be positive'
print('Age is valid')

🖥️ Output:

Age is valid

🔹 Lesson 6: `return` Statement

Used inside a function to send a value back.

def is_even(n):
    return n % 2 == 0

print(is_even(10))

🖥️ Output:

True

🔹 Lesson 7: `pass` Statement

pass means “do nothing.” It helps when writing code you’ll fill in later.

def check():
    pass

print('Function defined')

🖥️ Output:

Function defined

🔹 Lesson 8: `break` and `continue` in Conditions

break exits the loop
continue skips the current step

for i in range(5):
    if i == 3:
        break
    print(i)

🖥️ Output:

0
1
2

🔹 Lesson 9: `else` with Loop

Python allows else after loops. It runs only if loop ends normally (not broken).

for i in range(3):
    print(i)
else:
    print('Done!')

🖥️ Output:

0
1
2
Done!

🔹 Lesson 10: 🧪 Mini Project – Smart Checker System

Let’s bring everything together!

def check_user(name, age, has_id):
    assert age > 0, 'Invalid age'
    if age >= 18:
        if has_id:
            return f'{name} is eligible ✅'
        else:
            return 'ID required ❌'
    else:
        return 'Not eligible ❌'

print(check_user('Kunal', 20, True))

🖥️ Output:

Kunal is eligible ✅

🔁 Bonus Lessons for Part 3: Loops & Range (Python Flow Control)

Let’s now explore how Python repeats actions using loops — these go hand-in-hand with conditional logic!

🔹 While Loop

The while loop keeps running as long as a condition is True.

count = 1
while count <= 3:
    print("Count:", count)
    count += 1

🖥️ Output:

Count: 1  
Count: 2  
Count: 3

It stops when the condition becomes False (count > 3).

🔹 For Loop with Range

The for loop is used to run a block of code a certain number of times — often with range().

for i in range(5):
    print("i =", i)

🖥️ Output:

i = 0  
i = 1  
i = 2  
i = 3  
i = 4

🔸 range(5) means: start at 0, stop before 5.

🔹 For Loop with Custom Range

You can customize the start, stop, and step values:

for i in range(1, 10, 2):
    print(i)

🖥️ Output:

🔹 Infinite Loop (Be Careful!)

If you forget to change the condition in a while loop, it can go on forever!

# Uncommenting below will create an infinite loop
# while True:
#     print("I won't stop!")

Use Ctrl+C to stop in terminal or reboot kernel in notebooks.

🔹 While-Else Block

else runs only when the while condition becomes False.

x = 0
while x < 3:
    print(x)
    x += 1
else:
    print("Loop finished!")

🖥️ Output:

0  
1  
2  
Loop finished!

These loop concepts fit naturally under flow control and conditional logic. They help students understand repetition based on condition, not just one-time decisions.

Learn to Code Python – Part 4 – Strings

Welcome to Part 4 of your Python journey! In this part, we will master how Python handles text using strings — one of the most important data types.

📌 1. What is a String?

A string in Python is simply a collection of characters inside quotes. You can use:

Single quotes ' '
Double quotes " "
Triple quotes ''' ''' or """ """ (for multi-line text)

name1 = 'John'
name2 = "John"
name3 = '''John'''
print(name1, name2, name3)

🖥️ Output:

John John John

🔢 2. String Indexing (Positive & Negative)

Each character has a position (called an index):

Starts from 0 (left to right)
Starts from -1 (right to left)

text = 'Python'
print(text[0])   # First character
print(text[-1])  # Last character

🖥️ Output:

P
n

🔁 3. Iterating Through a String

You can loop through strings like a list!

word = 'Hi'
for letter in word:
    print(letter)

🖥️ Output:

H
i

📏 4. String Length & Slicing

len() gives number of characters
Slicing extracts part of the string using [start:stop]

text = 'Hello World'
print(len(text))       # Length
print(text[0:5])       # Slice from index 0 to 4

🖥️ Output:

11
Hello

🔂 5. Repeating and Concatenating Strings

You can:

Multiply strings using *
Add (join) strings using +

print('Hi' * 3)
print('Hello' + ' ' + 'World')

🖥️ Output:

HiHiHi
Hello World

🔍 6. Membership Operator in Strings

Check if a substring exists inside a string using in:

print('Py' in 'Python')
print('Java' in 'Python')

🖥️ Output:

True
False

🔠 7. Changing Case (Upper/Lower)

Python has many built-in functions to change text case:

text = 'hello PYTHON'
print(text.lower())      # All lowercase
print(text.upper())      # All uppercase
print(text.title())      # Title Case
print(text.capitalize()) # Capitalize first letter
print(text.swapcase())   # Swap cases

🖥️ Output:

hello python
HELLO PYTHON
Hello Python
Hello python
HELLO python

🔁 8. Finding and Replacing Text

Use find() to locate a word and replace() to change it:

text = 'hello python'
print(text.find('python'))
print(text.replace('python', 'world'))

🖥️ Output:

6
hello world

🔗 9. Splitting and Joining Strings

split() converts a string into a list
join() combines list elements into a string

text = 'a,b,c'
print(text.split(','))
print('-'.join(['a', 'b', 'c']))

🖥️ Output:

['a', 'b', 'c']
a-b-c

🧪 10. Checking What’s Inside (alpha/digit/alnum)

Python provides functions to check string content:

print('abc'.isalpha())    # Only alphabets?
print('123'.isdigit())    # Only numbers?
print('abc123'.isalnum()) # Alphabets + Numbers?

🖥️ Output:

True
True
True

🎯 11. Startswith and Endswith

Used to verify the start or end of a string:

text = 'hello world'
print(text.startswith('hello'))
print(text.endswith('world'))

🖥️ Output:

True
True

🏁 Final Words

You’ve just unlocked the power of text manipulation in Python! Strings help us build websites, apps, AI tools, and more.

Learn to Code Python – Part 5 – List, Tuple & Dictionary

Welcome to Part 5 of your Python journey! Today, we’ll explore the three essential data containers in Python: list, tuple, and dictionary. Each has its own superpower — and by the end of this post, you’ll know when and how to use them.

🔢 LISTS: `[]`

✅ Key Features of Lists

Ordered – elements keep their position.
Mutable – change items after creation.
Dynamic – grow or shrink as needed.
Heterogeneous – can store any data types.

✍️ How to Create a List

a = [1, 2, 3]           # Using square brackets
b = list("abc")         # Using list() constructor
empty = list()          # Empty list

🔧 Basic List Operations

a[1] = 99            # Update
c = a + [4, 5]       # Concatenate
d = [0] * 3          # Repeat
print(3 in a)        # Membership

👉 Aliasing vs Copy:

e = a                 # Aliased (both point to same)
f = a.copy()          # Independent copy

🔍 Useful List Functions

len(a), min(a), max(a), sum(a)

🛠️ Common List Methods

a.append(10)            # Add at end
a.extend([20, 30])      # Add multiple
a.insert(1, 'X')        # Insert at index
a.remove('X')           # Remove specific item
a.pop()                 # Remove last item
a.clear()               # Empty the list

🔎 Searching and Counting

a.index(10)             # First position of value
a.count(99)             # Number of times 99 appears

🪄 Sorting and Reversing

a.sort()                # Ascending order
a.reverse()             # Reverse in-place

📐 Slicing, Common Elements, Range, and List Comprehension

first3 = a[:3]
common = [x for x in a if x in b]
r = list(range(1, 6))   # [1, 2, 3, 4, 5]

🧩 Nested Lists & Flattening

matrix = [[1, 2], [3, 4]]
flat = [n for row in matrix for n in row]

🧊 TUPLES: `()`

✅ Key Features of Tuples

Immutable – can’t change after creation.
Ordered & Heterogeneous – just like lists, but read-only.

✍️ Creating Tuples

t = (1, 2, 3)          # Basic
t2 = 4, 5              # Parentheses optional
single = (42,)         # Note the comma!

📖 Accessing Tuple Elements

t[1]
len(t)
min(t), max(t)

🔧 Limited Tuple Methods

t.index(2)       # Position of 2
t.count(1)       # Count of 1

💡 Tip: To update a tuple, convert it to a list, edit it, then convert back:

lst = list(t)
lst.append(9)
t = tuple(lst)

🧠 DICTIONARIES: `{}`

✅ Key Features of Dictionaries

Ordered (Python 3.7+)
Mutable & Dynamic
Heterogeneous – flexible keys and values

✍️ Creating Dictionaries

person = {"name": "Ana", "age": 22}
d2 = dict(zip("abc", [1, 2, 3]))
empty = {}

➕ Adding and Updating

person["city"] = "Pune"             # Add new key
person.update(age=23)              # Update value

🔍 Reading Values

name = person["name"]              # Direct access
phone = person.get("phone")        # Safe access

🗝️ Getting Keys, Values, Items

keys = list(person.keys())
vals = list(person.values())
items = list(person.items())

❌ Deleting Items

person.pop("age")                  # Remove by key
pair = person.popitem()            # Last item
person.clear()                     # Empty dict

🔁 Looping Through a Dictionary

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

🧙 Dictionary Comprehension & Nesting

squares = {x: x**2 for x in range(4)}    # {0:0, 1:1, ...}
nested = {
    "emp1": {"name": "Ana"},
    "emp2": {"name": "Ben"}
}

🎯 Final Thoughts

Data Type	When to Use
List `[]`	Ordered collection of items, when you need to modify data
Tuple `()`	Fixed group of items, used for read-only or secure data
Dict `{}`	Key-value pairs for structured or labeled data

Mastering these 3 data types will help you solve 80% of Python problems in real-world scenarios.

Learn to Code Python – Part 6 – Functions

Welcome to Part 6 of your Python journey! This chapter introduces you to functions — the backbone of clean, modular, and reusable code.

🔄 1. What is a Function?

A function is a block of code that performs a specific task. You write it once and use it many times.

✅ Key Features:

Reusability – No need to repeat the same code.
Modularity – Break big programs into manageable parts.
Scoping – Controls which variables are visible inside/outside.

✅ Advantages:

Clean and organized code.
Easier to debug and maintain.
Reuse without retyping.

❌ Disadvantages:

Requires extra thinking to structure logic.
Too many functions can make simple scripts hard to trace.

🛠️ 2. How to Create a Function

Use the def keyword to define a function.

def greet():
    print("Hello from a function!")

greet()

🖥️ Output:

Hello from a function!

🎯 3. Parameters vs Arguments

Parameters are variable names used in the function definition.
Arguments are actual values passed when calling the function.

def welcome(name):   # name = parameter
    print(f"Hi {name}")

welcome("Ana")       # "Ana" = argument

🖥️ Output:

Hi Ana

🧩 4. Types of Arguments

✅ Positional Arguments (Default way)

Values are matched by position.

def add(x, y):
    print(x + y)

add(2, 3)  # 2 is x, 3 is y

✅ Keyword Arguments

You specify which value goes to which parameter.

add(x=5, y=10)

✅ Default Arguments

You assign default values in case none are passed.

def greet(name="Guest"):
    print("Hello", name)

greet()          # Uses default
greet("John")    # Overrides

🔁 5. `return` Statement

Use return to send back a value from the function.

def square(n):
    return n * n

result = square(4)
print(result)

🖥️ Output:

🔍 `return` vs `print`

`print()`	`return`
Displays the result	Gives the result
Temporary output	Used for further logic
For humans	For computers

🌍 6. Local vs Global Variables

🔹 Local Variable

Defined inside a function — not accessible outside.

def test():
    x = 10    # local
    print(x)

test()
# print(x)  ❌ Error

🔹 Global Variable

Defined outside and accessible inside functions.

x = 50

def show():
    print(x)   # accesses global x

show()

🔸 Modifying Global Variable

Use the global keyword:

count = 0

def increment():
    global count
    count += 1

increment()
print(count)

🎀 7. Decorators (Beginner View)

A decorator modifies the behavior of a function without changing its code.

def decorator_func(func):
    def wrapper():
        print("Before function")
        func()
        print("After function")
    return wrapper

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

say_hello()

🖥️ Output:

Before function
Hello!
After function

⚙️ 8. Generators (Efficient Loops)

Generators generate values one by one using yield instead of return.

def countdown(n):
    while n > 0:
        yield n
        n -= 1

for i in countdown(3):
    print(i)

🖥️ Output:

3
2
1

Generators save memory and are perfect for large datasets.

🏁 Conclusion

Functions make your code:

Reusable with def
Flexible with arguments
Powerful with decorators & generators

They are the heart of Python programming, and every great script uses them!

Learn to Code Python – Part 7 – Object-Oriented Programming (OOP)

Welcome to Part 7 of your Python journey! This chapter introduces you to Object-Oriented Programming (OOP) — a powerful way to structure and organize your code.

Let’s break it down in the most beginner-friendly way possible.

🧠 1. What is OOP and Why Do We Need It?

OOP (Object-Oriented Programming) is a method of programming where we build reusable code using classes and objects.

✅ Why Use OOP?

Modularity – Keep code clean and structured
Reusability – Use the same class multiple times
Encapsulation – Protect data inside objects
Scalability – Easier to maintain large codebases

⚙️ 2. Core OOP Concepts

Concept	Description
Class	Blueprint for creating objects
Object	Instance created from a class
Attributes	Variables inside the object
Methods	Functions inside the class that perform actions

🧩 3. Evergreen OOP Template in Python

Here is the general structure you can use for creating any class in Python:

# Step 1: Define the class
class ClassName:
    def __init__(self, param1, param2):
        self.param1 = param1       # Attribute 1
        self.param2 = param2       # Attribute 2

    def method1(self):
        # action or logic here
        pass

    def method2(self, arg):
        # action using argument
        return arg

📖 Understanding the Template

Part	What It Does
`class ClassName:`	Creates a new class (blueprint)
`__init__`	Constructor method to initialize attributes
`self.param1`	Instance variable (attribute)
`def method1()`	A method that belongs to the class
`pass` / `return`	Placeholder or actual logic
`self`	Refers to the current object itself

✅ How to Use the Template

# Step 2: Create objects from the class
obj1 = ClassName("value1", "value2")

# Step 3: Access methods and attributes
print(obj1.param1)
result = obj1.method2("input")

🎯 Summary

Now you know the basic structure of OOP in Python! You can check this Blog to Learn OOP in very Detail.

Object Oriented Programming Python Tutorial

You’ve learned:

What OOP is and why it’s useful
The meaning of class, object, attributes, and methods
A clean, evergreen OOP template you can reuse forever

🧪 In future parts, we’ll explore advanced OOP features like inheritance, encapsulation, and polymorphism. But for now, you’ve got the foundation.

Learn to Code Python – Part 1 – Basics

📘 Section 1: What is Python and Programming?

💡 Programming

💡 Python

⚙️ Section 2: Setting Up Python

✅ Step 1: Install Python

✅ Step 2: Install VS Code

🧠 Section 3: Python Basics

📌 Data Types

📌 Variables

🧪 Section 4: Type, Input, and Conversion

📌 type() Function

📌 Getting User Input

🔁 Section 5: Data Conversion and F-Strings

📌 Type Conversion

📌 f-Strings

🧮 Section 6: Lists and Operators

📌 Operators in Python

📌 Lists

🧠 Recap of What You’ve Learned in Part 1:

Learn to Code Python – Part 2 – Operators

🧠 What Are Operators?

🧮 Lesson 1: Arithmetic Operators

🔢 Lesson 2: Floor Division and Modulus

⚡ Lesson 3: Power Operator

🤝 Lesson 4: Comparison Operators

📦 Lesson 5: Assignment Operators

🔐 Lesson 6: Logical Operators

🧬 Lesson 7: Identity & Membership Operators

🧮 Lesson 8: Operator Precedence

🧪 Lesson 9: Mini Python Calculator (Project)

Learn to Code Python – Part 3 – Conditions

🔹 Lesson 1: Introduction to Conditional Statements

🔹 Lesson 2: if, else, elif in Action

🔹 Lesson 3: Nested Conditions

🔹 Lesson 4: Logical Operators in Conditions

🔹 Lesson 5: assert Statement

🔹 Lesson 6: return Statement

🔹 Lesson 7: pass Statement

🔹 Lesson 8: break and continue in Conditions

🔹 Lesson 9: else with Loop

🔹 Lesson 10: 🧪 Mini Project – Smart Checker System

🔹 While Loop

🔹 For Loop with Range

🔹 For Loop with Custom Range

🔹 Infinite Loop (Be Careful!)

🔹 While-Else Block

Learn to Code Python – Part 4 – Strings

📌 1. What is a String?

🔢 2. String Indexing (Positive & Negative)

🔁 3. Iterating Through a String

📏 4. String Length & Slicing

🔂 5. Repeating and Concatenating Strings

🔍 6. Membership Operator in Strings

🔠 7. Changing Case (Upper/Lower)

🔁 8. Finding and Replacing Text

🔗 9. Splitting and Joining Strings

🧪 10. Checking What’s Inside (alpha/digit/alnum)

🎯 11. Startswith and Endswith

🏁 Final Words

Learn to Code Python – Part 5 – List, Tuple & Dictionary

🔢 LISTS: []

✅ Key Features of Lists

✍️ How to Create a List

🔧 Basic List Operations

🔍 Useful List Functions

🛠️ Common List Methods

🔎 Searching and Counting

🪄 Sorting and Reversing

📐 Slicing, Common Elements, Range, and List Comprehension

🧩 Nested Lists & Flattening

🧊 TUPLES: ()

✅ Key Features of Tuples

✍️ Creating Tuples

📖 Accessing Tuple Elements

🔧 Limited Tuple Methods

🧠 DICTIONARIES: {}

✅ Key Features of Dictionaries

✍️ Creating Dictionaries

➕ Adding and Updating

📌 `type()` Function

🔹 Lesson 2: `if`, `else`, `elif` in Action

🔹 Lesson 5: `assert` Statement

🔹 Lesson 6: `return` Statement

🔹 Lesson 7: `pass` Statement

🔹 Lesson 8: `break` and `continue` in Conditions

🔹 Lesson 9: `else` with Loop

🔢 LISTS: `[]`

🧊 TUPLES: `()`

🧠 DICTIONARIES: `{}`

🔁 5. `return` Statement

🔍 `return` vs `print`