Imagine Python as a bustling city, and data types are the citizens living inside it.
Each citizen behaves differently:

Some are simple and predictable 🤓
Some store many things at once 🧺
Some are strict, others flexible
Some don’t like duplicates
Some come in pairs

In this chapter, you’ll meet Python’s most important data types — the true stars of your code.

1. Introduction to Data Types

A data type tells Python what kind of value you’re working with.

Is it a number?
Text?
A list of items?
A yes/no answer?

Every value in Python has a type, even if you don’t explicitly specify it.

x = 10
print(type(x))

Python replies:

<class 'int'>

2. Why Are Data Types Important?

Great question!

Because they help Python:

✔ Understand what operations are allowed

You can add numbers but not mix numbers and paragraphs:

10 + 5      # Works
"hello" + 3  # Nope

✔ Use memory efficiently

An integer takes less space than a list.

✔ Prevent confusion

It’s like labeling boxes in storage — otherwise chaos.

✔ Build predictable programs

If data types are wrong, bugs appear like unexpected guests at a party.

3. Basic Data Types in Python

Time to meet the simplest personalities!

3.1 Integers (The Whole Number Squad)

Integers are whole numbers — no decimals, no fractions.

score = 250
temperature = -5
year = 2025

They’re great for counting, indexing, or doing math.

3.2 Floating-Point Numbers (The Decimal Gang)

Floats are numbers with a decimal point.

rating = 4.9
pi = 3.14159
height = 5.8

Python handles decimals quite well, though sometimes floating-point math can get quirky:

0.1 + 0.2   # Surprise!

3.3 Strings (The Text Storytellers)

Strings store text — anything inside quotes:

name = "Amit Sharma"
message = "Hello Python!"

You can:

Combine strings
Slice them
Repeat them
Measure them

Strings are fun and incredibly powerful.

3.4 Booleans (True or False Heroes)

Booleans answer simple questions:

is_logged_in = True
is_admin = False

Python also treats things like this:

0 → False
"" → False
[] → False
Everything else → True

Booleans power conditions, decisions, and flow control.

4. Advanced Data Types (The Superheroes!)

These types can hold multiple values at once — like containers or collections.

4.1 Lists — The Flexible Backpack 🎒

Lists can store multiple items, and they can be of mixed types:

shopping_list = ["Milk", "Eggs", 10, True]

You can:

Add
Remove
Change
Sort

Lists are mutable: you can modify them anytime.

4.2 Tuples — The Locked Suitcase 🔒

Tuples look like lists, but once created, you can’t change them.

coordinates = (28.61, 77.23)

Why use tuples?

They’re faster
They’re more secure
Useful for fixed data

4.3 Sets — The Unique Collection 🟢

Sets store unique items — no duplicates allowed.

unique_colors = {"red", "blue", "green", "red"}
print(unique_colors)   # 'red' appears only once

Great for:

Removing duplicates
Membership checks
Math operations (union, intersection)

4.4 Dictionaries — The Smart Phonebook 📒

Dictionaries store things in key-value pairs:

person = {
    "name": "Amit Sharma",
    "age": 30,
    "email": "amit@example.com"
}

Access values using keys:

print(person["name"])

Dictionaries are incredibly useful for real-world data structures.

5. Type Conversion (Changing Personalities!)

Python lets you convert from one type to another — but only when it makes sense.

✔ String → Integer

age = "30"
age_number = int(age)

✔ Integer → String

msg = "You are " + str(age_number)

✔ Float → Integer

value = 9.8
whole = int(value)     # 9

✔ List → Set

items = [1, 2, 2, 3]
unique_items = set(items)

✔ Integer → Float

x = 5
y = float(x)   # 5.0

⚠️ Beware: converting an invalid value causes an error:

x = "hello"
int(x)   # ValueError

6. Practical Examples

Here are some fun, real-world examples:

Example 1: Calculate Expenses

food = 120.50
transport = 50
misc = 30.25

total = food + transport + misc
print("Total expenses:", total)

Example 2: Count Unique Visitors

visitors = ["Amit", "Sunita", "Amit", "Raj"]
unique_visitors = set(visitors)

print("Unique visitors:", len(unique_visitors))

Example 3: Store User Profile

user = {
    "name": "Amit Sharma",
    "age": 30,
    "is_verified": True,
}
print(user["name"])

Example 4: Convert String Input

num = "42"
result = int(num) * 2
print(result)     # 84

Example 5: Tuple for Coordinates

location = (19.07, 72.87)
print("Latitude:", location[0])

🎉 Wrap-up

By now, you understand:

✔ What data types are
✔ Why they matter
✔ Basic types: int, float, string, bool
✔ Advanced types: list, tuple, set, dict
✔ How to convert between them
✔ Practical examples

You’re now ready for the next chapter:

👉 1.3 — Operators in Python!

Data Types in Python (Meet the Personalities of Your Code!)