Python Tutorial | Learn Python Programming Language
Last Updated :
03 May, 2025
Python Tutorial – Python is one of the most popular programming languages. It’s simple to use, packed with features and supported by a wide range of libraries and frameworks. Its clean syntax makes it beginner-friendly.
Python is:
- A high-level language, used in web development, data science, automation, AI and more.
- Known for its readability, which means code is easier to write, understand and maintain.
- Backed by library support, so we don’t have to build everything from scratch, there’s probably a library that already does what we need.
Why to Learn Python?
- Python requires fewer lines of code compared to other programming languages.
- Python is in high demand as it provides many job opportunities in Software Development, Data Science and AI/ML.
- Python provides popular Web Development, AI/ML, Data Science and Data Analysis Libraries like Django, Flask, Pandas, Tensorflow, Scikit-learn and many more.
- Python is an object oriented programming language which encapsulates code within object.
- Python is cross-platform which works on Windows, Mac and Linux without major changes.
- Python is used by big companies like Google, Netflix and NASA.
First Python Program
Here is a simple Python code, printing a string. We recommend you to edit the code and try to print your own name.
Python
1. Python Basics
In this section, we’ll cover the basics of Python programming, including installing Python, writing first program, understanding comments and working with variables, keywords and operators. These are essential building blocks to get started with Python coding.
Before starting to learn python we need to install python on our system.
2. Python Functions
Python Functions are the backbone of organized and efficient code in Python. Here, in this section of Python 3 tutorial we'll explore their syntax, parameter handling, return values and variable scope. From basic concepts to advanced techniques like closures and decorators. Along the way, we'll also introduce versatile functions like range(), map, filter and lambda functions.
3. Python Data Structures
Python offers versatile collections of data types, including lists, string, tuples, sets, dictionaries and arrays. In this section, we will learn about each data types in detail.
Python's collections module offers essential data structures, including the following:
To learn data structure and algorithm with python in detail, you can refer to our DSA with Python Tutorial.
4. Python OOPs Concepts
In this section of Python OOPs, we'll explore the core principles of object-oriented programming (OOP) in Python. From encapsulation to inheritance, polymorphism, abstract classes and iterators, we'll cover the essential concepts that helps you to build modular, reusable and scalable code.
5. Python Exception Handling
In this section of Python Tutorial, we'll explore Python Exception Handling that how Python deals with unexpected errors, enabling us to write fault-tolerant code. We'll cover file handling, including reading from and writing to files.
6. File Handling
In this section, we will cover file handling, including reading from and writing to files.
7. Python Database Handling
In this section we will learn how to access and work with MySQL and MongoDB databases
8. Python Packages or Libraries
Python is a huge collection of Python Packages standard libraries that make development easier. These libraries help with a wide range of tasks and can save you a lot of time by providing ready-to-use tools.
Some commonly used types of libraries in Python include:
9. Data Science with Python
1. Foundational Libraries: These are the libraries that form the base for all data science work. Start here to build a strong foundation.
2. Advanced Visualization and Statistical Tools: Once you’re comfortable with basic data handling and visualization, move to creating cleaner visuals and performing statistical analysis.
3. Machine Learning Libraries: After data manipulation and visualization, learn machine learning, starting with simpler models and moving to advanced ones.
4. Deep Learning Frameworks: If you’re interested in AI and deep learning, these libraries will allow you to build and train neural networks.
To learn more, you can refer to Python for Data Science.
10. Web Development with Python
1. Core Web Frameworks (Backend Development with Python): These are the tools for building Python-based web applications.
2. Database Integration: Learn how to connect Python web frameworks to databases for storing and retrieving data.
3. Front-End and Backend Integration: Learn how to connect Python backends with front-end technologies to create dynamic, full-stack web applications.
4. API Development: Learn to build APIs (Application Programming Interfaces) for connecting your backend with front-end apps or other services.
To learn more, you can refer to Python for Web Development.
Python Quizzes
Python quiz page covers topics including variables, data types and how to manage output effectively. You'll explore operators and control flow to structure our code, along with loops (for and while) for repetitive tasks. Additionally, you'll gain knowledge with Python data structures such as lists, tuples, dictionaries and sets.
Python Practice
The Python Coding Practice Problems page offers exercises for all skill levels, covering basics, loops, functions and OOP. You'll work with lists, strings, dictionaries, sets and advanced structures like heaps and deques. These problems help build a strong foundation and boost confidence in solving real-world coding challenges.
Features of Python
Python stands out because of its simplicity and versatility, making it a top choice for both beginners and professionals. Here are some key features or characteristics:
- Easy to Read and Write: Python’s syntax is clean and simple, making the code easy to understand and write. It is suitable for beginners.
- Interpreted Language: Python executes code line by line, which helps in easy debugging and testing during development.
- Object-Oriented and Functional: Python supports both object-oriented and functional programming, giving developers flexibility in how they structure their code.
- Dynamically Typed: You don’t need to specify data types when declaring variables; Python figures it out automatically.
- Extensive Libraries: Python has a rich collection of libraries for tasks like web development, data analysis, machine learning and more.
- Cross-Platform: Python can run on different operating systems like Windows, macOS and Linux without modification.
- Community Support: Python has a large, active community that continuously contributes resources, libraries and tools, making it easier to find help or solutions.
This Python tutorial is updated based on latest Python 3.13.1 version.
Applications of Python
- Web Development: Frameworks like Django and Flask can be used to create dynamic websites and web applications quickly and efficiently.
- Data Science & Analysis: Python is most preferred language for data analysis, visualization and handling large datasets. Because of extensive libraries like Pandas, NumPy and Matplotlib.
- Machine Learning & AI: Python is popular in AI and machine learning because of its powerful libraries like TensorFlow, Keras and Scikit-learn.
- Scripting & Automation: Python’s simplicity makes it ideal for writing scripts that automate tasks in different systems, from server management to file handling. Python is commonly used to automate repetitive tasks, making processes faster and more efficient.
- Web Scraping: Libraries like Beautiful Soup and Scrapy.
- Desktop App Development: Python can be used to build desktop applications using frameworks like Tkinter and PyQt. Python is also used for game development, with libraries like Pygame to create simple games.
Python vs. Other Programming Languages
Below is the comparison of Python with C, C++ and Java:
| Feature | Python | C/C++ | JavaScript | Java |
|---|
| Type | Interpreted | Compiled | Interpreted | Compiled and Interpreted |
|---|
| Paradigm | Multi-paradigm (object-oriented, procedural, functional) | Procedural (C), Multi-paradigm (C++: object-oriented, generic) | Multi-paradigm (event-driven, functional, object-oriented) | Object-oriented, structured |
|---|
| Memory Management | Automatic | Manual | Automatic | Automatic |
|---|
| Syntax | Simple | Complex | Simple | Complex |
|---|
| Use Cases | Web development, data analysis, machine learning | System programming, embedded systems, game/high-performance development | Large-scale applications, enterprise software | Large-scale applications, enterprise software |
|---|
| Notable Frameworks/Libraries | Django, Flask | Standard Library, Boost | Spring, Hibernate | Spring, Hibernate |
|---|
| Community Support | Strong | Strong | Strong | Strong |
|---|
| Job Market | Abundant | Abundant | Abundant | Abundant |
|---|
List of Companies Using Python
These are some Popular companies that use Python in their workflow:
| Company | Description |
|---|
| Google | Uses Python for various applications, including their search engine and machine learning projects. |
|---|
| Instagram | The backend of Instagram is built using Python, enabling it to handle millions of users efficiently. |
|---|
| Spotify | Python is used for data analysis and backend services, helping improve user recommendations. |
|---|
| Dropbox | Python powers the desktop client of Dropbox, making it easy to sync files across devices. |
|---|
| Netflix | Python helps Netflix with data analysis and managing its content recommendation algorithms. |
|---|
| Reddit | One of the largest online communities, Reddit, uses Python for its core functionalities. |
|---|
| Uber | Uber uses Python for various features, including dynamic pricing and data analysis. |
|---|
| Pinterest | Python plays a key role in the backend of Pinterest, helping scale and manage user content. |
|---|
Career & Jobs in Python
Python offer diverse opportunities across industries, here we have listed all the best career opportunity that anyone can pursue after learning Python.
| Career | Average Salary (INR) Per Annum | Average Salary (USD) Per Annum |
|---|
| Python Developer | ₹500,000 – ₹1,200,000 | $60,000 – $110,000 |
|---|
| Data Scientist | ₹600,000 – ₹1,500,000 | $70,000 – $130,000 |
|---|
| Machine Learning Engineer | ₹700,000 – ₹1,800,000 | $75,000 – $140,000 |
|---|
| Full Stack Developer | ₹600,000 – ₹1,300,000 | $65,000 – $120,000 |
|---|
| DevOps Engineer | ₹800,000 – ₹2,000,000 | $80,000 – $140,000 |
|---|
| Automation Engineer | ₹500,000 – ₹1,200,000 | $55,000 – $100,000 |
|---|
| Data Analyst | ₹400,000 – ₹900,000 | $50,000 – $90,000 |
|---|
| Software Engineer | ₹500,000 – ₹1,500,000 | $65,000 – $120,000 |
|---|
| Backend Developer | ₹600,000 – ₹1,300,000 | $70,000 – $125,000 |
|---|
| AI Engineer | ₹900,000 – ₹2,500,000 | $90,000 – $160,000 |
|---|
Python Latest & Upcoming Features
Python 3.13 is the most recent stable release of the programming language, featuring a blend of updates to its syntax, implementation and standard library. Key enhancements include the introduction of a revamped interactive interpreter, experimental capabilities for operating in a free-threaded mode (as outlined in PEP 703) and the addition of a Just-In-Time (JIT) compiler (detailed in PEP 744).
The upcoming version of Python is Python 3.14 and it is expected to include the following notable updates: UTF-8 Mode Default (PEP 686), Shorthand Syntax for Keyword Arguments, Enhanced f-strings, JSON-based Simple API (PEP 691).
--> In this tutorial, we've provided the latest Python 3.13 version compiler where you can edit and compile your written code directly with just one click of the RUN Button. So test yourself with Python first exercises.
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