Data Types

Introduction

Python's built-in types cover most needs: int, float, bool, str, list, tuple, dict, set, None. Everything is an object — even numbers and functions.

Understanding the topic

Core concepts to understand:

• Numeric: int, float, complex.
• Text: str (immutable, Unicode).
• Collections: list, tuple, dict, set.
• Logical: bool (subclass of int), None.

Syntax reference

Visual flow / code:

# Built-in types
num: int = 42
pi: float = 3.14159
name: str = "Bob"
flag: bool = True
items: list = [1, 2, 3]
person: dict = {"name": "Bob", "age": 25}
unique: set = {1, 2, 3}
empty = None

print(type(num))  # <class 'int'>
print(isinstance(num, int))  # True

Execution workflow

Real-world use

Most production Python uses list, dict, and str heavily — master these and you've covered 80% of day-to-day code.

Best practices

• Prefer isinstance() over type() ==.
• Use immutable types (tuple) when data won't change.

Hands-on exercise

Interview preparation — practice these questions:

• List vs tuple — when to use which?
• Is None the same as False?
• What's a set good for?

Summary

In summary: Everything is an object. Choose the right collection per use case.