Python Tutorial – Learn Python

What is Python?

Python is a high-level, interpreted and general-purpose programming language. Python code is easy to write and does not require separate compilation to run. It was created by Guido van Rossum in the late 1980s.

Its biggest feature is its simplicity and readability. Python’s syntax (rules) is very clear and English-like, so even beginners can learn it quickly. Also, it has many ready-made libraries and frameworks, which can be used for web development, data science, artificial intelligence, machine learning, automation, game development—everything.

Today Python is one of the most popular programming languages ​​in the world and is being used everywhere from the IT industry to scientific research.

History of Python

• Late 1980s – Guido van Rossum started creating Python at CWI in the Netherlands. His aim was to create a language that was as easy as ABC and as powerful as C.
• 1991 – The first version of Python (0.9.0) came out. It already had features like functions, exceptions, strings, and lists.
• 1994 – Python 1.0 was released, it introduced modules and better error handling.
• 2000 – Python 2.0 came out. It added things like list comprehensions and garbage collection.
• 2008 – Python 3.0 was released. It fixed old bugs and made the language more consistent. But it was not backward compatible with Python 2.
• 2020 – Python 2’s official support was discontinued. Now Python 3 has become the standard.
• Today, Python is counted among the top programming languages ​​in the world. It is used extensively in AI, automation, web development, and many other fields.

Use Cases with Real-World Examples

1. Web development

Python excels in web development. Frameworks like Django and Flask build sites efficiently. For example, Instagram runs on Django. They handle millions of users daily with Python’s scalable code.

2. Data analysis

Data analysis relies on Python heavily. Libraries such as Pandas and NumPy crunch numbers. For example, Netflix uses Python to analyze viewer data. They predict shows you’ll like based on patterns in billions of streams. This cuts manual work from hours to minutes.

3. Automation

Automation scripts save time in daily tasks. Python scripts can easily automate tasks like file renaming or email sending. Google employs Python for system administration. Their engineers automate server checks across data centers.

4. Machine learning

Machine learning thrives with Python. Python libraries like TensorFlow and PyTorch build machine learning models. For example, NASA’s Jet Propulsion Laboratory applies Python for image recognition in Mars rovers. They train models on Earth data to identify rocks autonomously. This predicts outcomes from new data accurately.

5. Game development

Game development uses Python, too. The Pygame Python library creates 2D games. For example, Eve Online, a massive online game, incorporates Python for scripting.

6. Scientific computing

Python is a key tool in scientific computing. SciPy, for example, is used to solve complex equations. At CERN, researchers rely on Python to process vast amounts of particle collision data. They can simulate experiments with millions of variables and get results almost instantly.

Is Learning Python a Safe Bet for the Future?

Yes, learning Python secures a strong career path. Demand for Python skills grows steadily. In 2025, Stack Overflow’s survey shows Python as the most wanted language. Over 70% of developers use it. This popularity stems from its versatility across fields.

Data Science heavily relies on Python, and data science jobs pay well. Python roles average $120,000 yearly in the US, per Indeed data. Companies like Amazon hire Python devs for AI projects.

Automation rises, boosting Python’s role. Scripts replace repetitive jobs. Uber automates ride matching with Python. This efficiency creates demand for Python coders.

Risks exist if you stop learning.

In summary, Python offers a reliable future. Its widespread use in tech giants proves this.

Python Tutorial Lessons

Section 1: Getting Started

• Installing Python (Windows, Mac, Linux)
• Python IDEs (VS Code, IDLE, PyCharm)
• Writing First Program print()
• How Python Code Runs: Interpreter vs Compiler
• Keywords and Identifiers

SECTION 2: Python Basics

• Variables in Python
• Constants in Python
• Python Data Types (int, float, str, bool, complex)
• Type Casting and Type Checking
• User Input and input()
• print() formatting, f-strings
• Comments (#) and Docstrings

SECTION 3: Operators in Python

• Arithmetic Operators
• Assignment Operators (=, +=)
• Comparison Operators (==, !=)
• Logical Operators (and, or, not)
• Bitwise Operators (for pro logic lovers)
• Identity and Membership Operators (is, in)

SECTION 4: Control Flow (Conditions and Loops)

• if, elif, else Conditions
• Nested Conditions
• for loop with range()
• while loop
• Python loop Control Statements (break, continue, pass)
• Loop else clause

SECTION 5: Data Structures

• Strings slicing, methods, immutability
• Lists, indexing, methods, nested lists
• Tuples immutable lists
• Sets unique values, set operations
• Dictionaries keys, values, .get(), nested dicts
• List Comprehensions (basic to nested)

SECTION 6: Functions in Python

• Creating Functions def
• Parameters and Arguments
• Return values
• Default Arguments
• Variable-length Arguments (*args, **kwargs)
• Recursion
• Lambda (anonymous) functions
• map(), filter(), reduce(), zip()

SECTION 7: Error Handling

• Syntax vs Runtime Errors
• Try, Except
• Finally block
• Raising Exceptions manually
• Custom Exceptions

SECTION 8: File Handling

1. Reading and Writing Python Files (open())
2. Modes: r, w, a, x
3. File Methods: read(), readline(), write()
4. With statement and context manager
5. Working with CSV files

SECTION 9: Modules and Packages

1. Importing Modules
2. Built-in modules (math, random, os, sys)
3. Creating your own module
4. name == ‘main‘
5. Installing external packages using pip

SECTION 10: Object-Oriented Programming (OOPs)

1. What is OOP in Python? Real-life example
2. Class and Object
3. init constructor
4. Attributes and Methods
5. self keyword
6. Python Inheritance (single, multiple)
7. Method Overriding
8. Encapsulation, Abstraction, Polymorphism
9. super() function
10. Classmethods and Staticmethods
11. @property decorator

SECTION 11: Clean Coding + Best Practices

1. Naming conventions (snake_case)
2. Writing reusable functions
3. DRY principle
4. Basic unit testing with assert

SECTION 12: Python and the Real World

1. Using Python with JSON
2. Working with APIs using requests
3. Basic Web Scraping using BeautifulSoup
4. Date and Time module
5. Sending Emails with Python
6. Reading PDF or Excel files
7. Automating boring stuff (like file renaming)

SECTION 13: Projects Section

1. Calculator
2. Number Guessing Game
3. To-Do App (text-based)
4. Dice Roller
5. Rock Paper Scissors
6. Quiz App
7. Simple Chatbot
8. Password Generator
9. Contact Book CLI
10. File Organizer Script

SECTION 14: Advanced Python Starter Pack

1. Virtual Environments (venv)
2. Iterators and Generators
3. Decorators (Function & Class)
4. str vs repr
5. Regular Expressions (re)
6. Working with Databases (sqlite3)
7. Intro to Tkinter (GUI basics)

SECTION 15: Bonus / Wrap Up

1. Python Interview Questions
2. Practice Challenges (10 problems)
3. Python Quiz
4. How to Become a Python Developer
5. Python Cheat Sheet