Learn Python Programming

Start with getting started, installation, and core basics. Clear explanations and practical examples to help you learn faster.

Python Multiple Inheritance

Multiple inheritance allows a class to inherit from more than one parent class, combining their attributes and methods. Python uses the Method Resolution Order (MRO) to determine which method is called.

Basic Multiple Inheritance

# A class can inherit from multiple parent classes
class Flyable:
    def fly(self):
        return f"{self.name} is flying!"

class Swimmable:
    def swim(self):
        return f"{self.name} is swimming!"

class Walkable:
    def walk(self):
        return f"{self.name} is walking!"

# Duck inherits from all three
class Duck(Flyable, Swimmable, Walkable):
    def __init__(self, name):
        self.name = name

duck = Duck("Donald")
print(duck.fly())   # Donald is flying!
print(duck.swim())  # Donald is swimming!
print(duck.walk())  # Donald is walking!

Method Resolution Order (MRO)

# When multiple parents have the same method, Python uses MRO
# MRO follows C3 linearization: left-to-right, depth-first

class A:
    def greet(self):
        return "Hello from A"

class B(A):
    def greet(self):
        return "Hello from B"

class C(A):
    def greet(self):
        return "Hello from C"

class D(B, C):  # B is listed before C
    pass

d = D()
print(d.greet())  # "Hello from B" (B comes first in MRO)

# View the MRO
print(D.__mro__)
# (D, B, C, A, object)
# Python searches: D → B → C → A → object

# Or use .mro() method
print(D.mro())

Mixins — The Practical Pattern

# Mixins are small classes that add specific functionality
# They are not meant to be instantiated alone

class JsonMixin:
    """Add JSON serialization to any class."""
    def to_json(self):
        import json
        return json.dumps(self.__dict__, indent=2)

    @classmethod
    def from_json(cls, json_str):
        import json
        data = json.loads(json_str)
        return cls(**data)

class LogMixin:
    """Add logging capability to any class."""
    def log(self, message):
        print(f"[{type(self).__name__}] {message}")

class TimestampMixin:
    """Add creation timestamp."""
    def __init_subclass__(cls, **kwargs):
        super().__init_subclass__(**kwargs)

    def set_created(self):
        from datetime import datetime
        self.created_at = datetime.now().isoformat()

# Combine mixins with a main class
class User(JsonMixin, LogMixin):
    def __init__(self, name, email):
        self.name = name
        self.email = email

user = User("Alice", "alice@example.com")
user.log("User created")      # [User] User created
print(user.to_json())          # {"name": "Alice", "email": "alice@..."}

# Recreate from JSON
json_str = user.to_json()
user2 = User.from_json(json_str)
print(user2.name)  # Alice

super() with Multiple Inheritance

# super() follows the MRO — it calls the NEXT class in line, not the parent
class Base:
    def __init__(self):
        print("Base.__init__")

class Left(Base):
    def __init__(self):
        print("Left.__init__")
        super().__init__()  # calls Right (next in MRO), not Base!

class Right(Base):
    def __init__(self):
        print("Right.__init__")
        super().__init__()  # calls Base

class Child(Left, Right):
    def __init__(self):
        print("Child.__init__")
        super().__init__()  # calls Left

child = Child()
# Output (follows MRO: Child → Left → Right → Base):
# Child.__init__
# Left.__init__
# Right.__init__
# Base.__init__

# Cooperative multiple inheritance pattern
class Serializable:
    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def serialize(self):
        return self.__dict__.copy()

class Validatable:
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.validate()

    def validate(self):
        pass  # override in subclass

class Product(Serializable, Validatable):
    def __init__(self, name, price, **kwargs):
        self.name = name
        self.price = price
        super().__init__(**kwargs)

    def validate(self):
        if self.price < 0:
            raise ValueError("Price cannot be negative")

Diamond Problem

# The diamond problem: D inherits B and C, both inherit A
#       A
#      / \
#     B   C
#      \ /
#       D

class A:
    def method(self):
        print("A.method")

class B(A):
    def method(self):
        print("B.method")
        super().method()

class C(A):
    def method(self):
        print("C.method")
        super().method()

class D(B, C):
    def method(self):
        print("D.method")
        super().method()

D().method()
# D.method → B.method → C.method → A.method
# Each class called only ONCE thanks to MRO

# Python solves the diamond problem cleanly with C3 linearization
print(D.__mro__)
# (D, B, C, A, object)
  • Python supports multiple inheritance — a class can have multiple parent classes.
  • MRO (Method Resolution Order) determines which method is called — check with Class.__mro__.
  • Use mixins (small, focused classes) as the primary pattern for multiple inheritance.
  • super() follows the MRO chain, not necessarily the direct parent class.
  • Python solves the diamond problem via C3 linearization — each class appears only once in the MRO.

Frequently Asked Questions

Answers to common Python getting-started questions

You can use an online Python editor that runs in your browser. It provides a Python interpreter so you can execute code instantly without setup. This is ideal for quick practice and learning.

Download the latest Python installer from the official Python website, run the installer, and select "Add python.exe to PATH" before clicking "Install Now". After installation, verify with the command: python --version.

Download the macOS installer from the Python website, run it, and follow the steps. Verify the installation with python3 --version in the Terminal. macOS often uses python3 to refer to Python 3.

Open your terminal or command prompt and run python --version (Windows) or python3 --version (macOS/Linux). If you see a version number, Python is installed correctly.

On macOS and Linux, python may refer to Python 2.x while python3 refers to Python 3.x. Use python3 to ensure you are running Python 3.

Yes. Python runs on Windows, macOS, and Linux. Code is generally portable across platforms, especially for beginner-level scripts.

Python Programming Tutorial — Learn Python from Scratch

Python is the world's most popular programming language for beginners, data science, AI/ML, web development, and automation. This tutorial teaches Python step-by-step with clear explanations and runnable code examples. You can try every example in our free Python Compiler without installing anything.

Each topic builds on the previous one, starting from installation and Hello World through advanced concepts like decorators, generators, and file I/O. Whether you are a complete beginner or refreshing specific skills, every page gives you immediately usable code.

What This Tutorial Covers

  • Getting Started: Install Python, run online, Hello World
  • Basics: Variables, data types, type conversion, input/output
  • Operators: Arithmetic, comparison, logical, assignment
  • Control Flow: if/elif/else, for loops, while, break/continue
  • Data Structures: Lists, tuples, sets, dictionaries
  • Strings: Methods, slicing, formatting, f-strings
  • Functions: Parameters, return values, *args, **kwargs, scope
  • OOP: Classes, objects, inheritance, polymorphism
  • File I/O: Reading, writing, CSV, JSON handling
  • Exceptions: try/except, custom exceptions, raise
  • Advanced: List comprehensions, lambda, generators, decorators
  • Modules: import, pip, packages, __name__ == "__main__"

Why Learn Python in 2026?

  • #1 most popular language: Ranked first on TIOBE, Stack Overflow, and GitHub for multiple years running.
  • AI and Data Science: The primary language for machine learning (TensorFlow, PyTorch, scikit-learn), data analysis (Pandas, NumPy), and AI development.
  • Web development: Django and Flask power backends at companies like Instagram, Spotify, and Pinterest.
  • Automation: Automate files, emails, web scraping, reports, and system administration tasks in minutes.
  • Beginner-friendly: Clean syntax with enforced indentation makes code readable from day one — no curly braces or semicolons.
  • Massive job market: Python developers are in high demand across tech, finance, healthcare, and research.

Python vs Other Languages

FeaturePythonJavaJavaScriptC++
SyntaxVery clean, readableVerboseModerateComplex
TypingDynamic, strongStatic, strongDynamic, weakStatic, strong
SpeedSlower (interpreted)Fast (JIT)Fast (V8 JIT)Fastest (native)
Best ForAI/ML, data, automationEnterprise, AndroidWeb frontend/backendSystems, games
Learning Time2–4 weeks basics4–6 weeks basics3–4 weeks basics8–12 weeks basics

How to Get Started

  1. Run Python online: Use our free Python Compiler — no installation needed.
  2. Install locally: Download Python 3 from python.org (Windows/Mac) or use apt install python3 (Linux).
  3. Verify: Run python3 --version in your terminal to confirm installation.
  4. Choose an editor: VS Code with Python extension (free), PyCharm Community (free), or Jupyter Notebook for data science.
  5. Follow this tutorial in order: Start from Introduction and work through each topic sequentially.

Frequently Asked Questions

Do I need prior programming experience?

No. Python is designed to be beginner-friendly. This tutorial starts from absolute zero and builds up gradually.

Which Python version should I use?

Python 3.10+ is recommended. Python 2 reached end-of-life in 2020. All examples in this tutorial use Python 3 syntax.

How long does it take to learn Python?

Basics (syntax, loops, functions) take 2–4 weeks. Intermediate (OOP, file I/O, modules) adds 3–4 weeks. Specialisation (Django, data science, ML) takes another 2–3 months.

Is this tutorial free?

Yes, completely free. No account, no sign-up. All topics and examples available without restriction.

Who Is This For?

Complete beginners choosing their first programming language. Students in CS courses needing a Python reference. Data analysts transitioning from Excel to Python (Pandas). Self-taught developers adding Python to their skill set. Professionals automating repetitive tasks. Anyone preparing for Python coding interviews.