Python Tutorial

Python is a high-level, general-purpose programming language known for its simple, readable syntax and its use across web development, data science, machine learning, and automation. This Python tutorial covers everything needed to start coding in Python, from installation and basic syntax to variables, data types, control flow, functions, and object-oriented programming. As of 2026, Python 3.14 is the current stable release, and Python 2 has been unsupported since January 2020, so all new learners should use Python 3. Because of its beginner-friendly design, Python is one of the most recommended first languages for students, career switchers, and working professionals in India. This Python programming tutorial also introduces practical use cases such as automation scripts, REST APIs, and AI/ML workflows using libraries like Pandas, NumPy, and PyTorch. Most learners can grasp Python basics within 2–4 weeks of consistent practice and reach job-ready proficiency in 3–6 months with structured, project-based learning.
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Python Tutorial

Python is a high-level, object-oriented programming language created by Guido van Rossum, widely used across web development, data science, automation, and Machine Learning. This Python tutorial walks you through Python step by step, from installation and basic syntax to advanced concepts like OOP and file handling, whether you're starting from zero or brushing up before a technical interview.

What is Python?

Python is a modern programming language that fits all your purposes (general purpose), and is a high-level programming language. The Python programming language is interpreted and supports object-oriented programming. It was developed by Guido van Rossum.

If you are an aspiring data scientist, or Machine learning engineer, Web developer, Scripting and automation enthusiast, Python is the language for you! It is a highly versatile language capable of almost everything you would like to perform with it. It has a plethora of libraries and packages that aid you with your tasks related to data manipulation, statistical calculations, web development and so many more.

This python tutorial for beginners is designed to take you from zero to confident, whether you follow it here or through Scaler's Learn Python App, which offers free downloadable lessons for offline learning.

Why Learn Python Programming?

Before diving into this Python programming tutorial, it's worth understanding why Python remains one of the most widely adopted languages in the world, consistently ranking at or near the top of indices like TIOBE and PYPL year after year.

The Need to Learn Python Programming

We figured out that the python programming language is definitely a very widely used versatile language, but what is the need?

Python's versatility is only part of the story, the real driver is career opportunity. Python continues to top global programming language rankings and remains one of the most in-demand skills on Indian job boards.

According to 2026 salary data, Python developers in India typically earn between ₹4.5 LPA and ₹12 LPA, with freshers starting around ₹3.5–5 LPA. Developers who pair Python with data science, AI/ML, or modern backend skills (FastAPI, LangChain, async Python) command a significant premium, often ₹20 LPA and above at product companies. (Note for review: salary figures vary meaningfully by source- Glassdoor, AmbitionBox, and Naukri, recommend confirming a single reference source before this goes live.)

Beyond salary, Python's vast open-source community has built frameworks for nearly everything, web development (Django, Flask, FastAPI), data science (Pandas, NumPy), and Machine Learning (TensorFlow, PyTorch, OpenCV).

Surely that's enough motivation needed to learn the python programming language! Not convinced? Let's talk about the importance of Python.

Importance of Python Programming

Python's importance goes beyond popularity. Here's what makes this Python basic tutorial worth your time:

• A broad standard library: One of Python's greatest strengths, portable and cross-platform on Windows, Mac, and UNIX.
• Interactive: You can sit at a Python prompt and interact directly with the interpreter.
• Beginner-friendly: Python is widely regarded as a beginner's language.
• Maintainable: Easy to read and maintain code.
• Portable: Runs on a wide variety of hardware platforms with a consistent interface.
• Scalable: Better code structure and support for large programs compared to shell scripting.

Python Programming Jobs

Python programming is widely used in a variety of industries due to its simplicity, versatility, and readability. Python's flexibility makes it an ideal choice for various tasks, such as data analysis, web development, artificial intelligence, and automation. Here are some of the most common Python programming jobs:

Python Developer

Python developers use Python to design and develop software applications such as web applications, desktop applications, mobile applications, and games.

Data Scientist

Data scientists use Python to analyze and extract insights from large data sets. They may work on projects such as customer segmentation, sentiment analysis, recommendation systems, and fraud detection.

Machine Learning Engineer

Machine learning engineers use Python to develop algorithms and models that enable machines to learn from data and make predictions. They may work on projects such as image recognition, speech recognition, natural language processing, and autonomous vehicles.

Automation Engineer

Automation engineers use Python to automate various processes and tasks, such as testing, deployment, and monitoring. They may work in industries such as software development, manufacturing, and healthcare.

Web Developer

Web developers use Python to develop web applications such as e-commerce sites, social networks, and content management systems. They may work on projects such as designing web interfaces, testing and debugging code, and collaborating with other developers and stakeholders to deliver high-quality products.

Python Basic Syntax

Python syntax is the set of rules that define how Python code is written and structured, statements, comments, variables, data types, operators, and control structures. Following these basics is essential to writing effective Python code. Learn more here.

Your First Python Program

Every Python online tutorial should start with a working example. Here's the classic first program:

Python

print("Hello, World!")

Save this as hello.py and run it with python hello.py in your terminal. That's it, you've written and executed your first Python program.

Python History

Python was created by Guido van Rossum in the late 1980s and released in 1991. Python 1.0 arrived in 1994, Python 2.0 in 2000, and Python 3.0 in 2008. Python 2 officially reached end-of-life on January 1, 2020. It no longer receives security updates, so all new learners and projects should use Python 3.

What's New in Python in 2026?

As of August 2026, Python 3.14 is the current stable release (with maintenance releases arriving roughly every 4–6 weeks), and Python 3.15 is in release-candidate testing ahead of its planned October 2026 launch. Recent Python releases have focused on:

• Faster startup and interpreter performance
• Improved error messages and REPL experience
• Template string literals (t-strings) for safer string processing
• Experimental free-threading and JIT builds for better concurrency

If you're starting this Python tutorial for beginners today, install the latest Python 3.x release rather than an older version, it gives you better performance, security, and compatibility with modern libraries.

Career Opportunities in Python

Data Scientist

Python is the go-to language for data scientists to analyze and interpret large datasets.

Software Developer

Python can be used for web development, game development, and software development. It is also used in automation, testing, and scientific computing.

DevOps Engineer

Python is often used for scripting and automation, making it a useful skill for DevOps engineers who manage infrastructure and deploy software.

Cybersecurity Analyst

Python can be used for cybersecurity purposes such as penetration testing, network scanning, and analyzing malware.

Technical Writer

Python's popularity and versatility create opportunities for technical writers with expertise in Python to create documentation, tutorials, and training materials for developers. These are just a few examples of the many career opportunities available for those with Python expertise. Python's popularity in various fields ensures that there are many career paths available, making it a valuable skill to learn.

AI/ML Engineer

Python remains the primary language for building and deploying machine learning and generative AI systems.

Python's versatility across these paths makes it one of the most valuable long-term skills to learn, and one reason it consistently ranks among the highest-paying specializations in tech.

How to Learn Python Programming?

You are surely now convinced that Python is an irreplaceable language, and are motivated to study it. The best way to start is by reading on! Go through all the modules in this Python course hub, and you'll be a pro at Python by the end of it.

How Long Does It Take to Learn Python for Beginners?

Most learners can grasp Python fundamentals, variables, loops, functions, in 2 to 4 weeks of consistent daily practice. Reaching a job-ready level, including OOP, data structures, and real projects, typically takes 3 to 6 months when paired with structured, project-based learning. This Python basic tutorial is built to move you through that path in order, module by module, rather than leaving you to piece it together from scattered resources.

About this Python Tutorial

This Python online tutorial is divided into ten modules that cover the basics of Python programming, including installation on various operating systems and the differences between Python 2 and 3. The course covers essential constructs such as variables, keywords, literals, and control flow statements like if-else, while and for loops. It also teaches handling exceptions and file handling. Additionally, the course covers more advanced topics like object-oriented programming, data structures, functions, and advanced constructs such as modules, packages, collections, regular expressions, and datetime.

Applications of Python Programming

After you have mastered the programming language with this python programming tutorial, here is the broad application set of the language:

• Data Science
• Creating Desktop applications
• Creating console based applications
• Creating Mobile apps
• Software Development
• Artificial Intelligence
• Web applications
• Enterprise Applications
• Machine learning
• Computer Vision

The above are just some of the plethora of applications of the Python programming language. Use the tutorial wisely and master the language!

Python for AI, Data Science & Machine Learning

Python's biggest growth area in 2026 isn't web development, it's AI. Python is the default language behind most modern AI/ML workflows because of its readable syntax and mature ecosystem: NumPy and Pandas for data handling, Scikit-learn for classical ML, TensorFlow and PyTorch for deep learning, and LangChain or Hugging Face Transformers for building applications on top of large language models.

If you're working through this python tutorial with an eye on a data science or AI career, the fundamentals here, functions, OOP, data structures, are exactly what you'll build on. Scaler's Data Science and ML and AI/ML programs pick up from this same foundation with mentor-led, project-based learning.

Popular Python Frameworks and Libraries

Python Installation

Python installation involves downloading and installing the Python interpreter and associated tools on your computer. You can download Python from the official Python website and select the appropriate installer for your operating system. Once the installer is downloaded, you can run it to begin the installation process. The installer will guide you through the installation process and ask you to select the installation location and any additional components you want to install. After the installation is complete, you can run Python from the command line or an integrated development environment (IDE) to start writing and running Python code.

To learn more -:

• Install Python on Windows
• How to Install Python on MacOS?
• How to Install Python on Linux?

Python 2 Vs. Python 3

Python 2 reached end-of-life on January 1, 2020 and no longer receives security updates, all new learners and projects should use Python 3. Understanding the differences is still useful for reading legacy codebases or in technical interviews:

Syntax

Python 2 and Python 3 have slightly different syntax. Python 3 requires parentheses around the print statement, while Python 2 does not.

Unicode Support

Python 3 provides better support for Unicode, whereas Python 2 requires additional modules to support Unicode.

Print Function

In Python 2, the print statement is used to print output, while in Python 3, the print function is used.

Division

In Python 2, dividing two integers results in an integer, while in Python 3, dividing two integers results in a float.

Library Support

Python 3 has a larger and more active community, and many libraries are now available only for Python 3.

Backward Compatibility

Python 3 is not backward compatible with Python 2. This means that code written in Python 2 may need to be modified to work in Python 3.
Refer here to learn more.

Variables in Python

In Python, variables are used to store data values. They are created when a value is assigned to them using the assignment operator "=" and can be of any data type. Python uses dynamic typing, which means that the type of a variable is determined at runtime based on the value assigned to it. Variables can be reassigned to new values at any time, and their values can be printed or used in operations within a Python program.
Read here to know more.

Data Types in Python

In Python, data types refer to the types of values that can be stored in variables. The common data types in Python include integer, float, boolean, string, list, tuple, and dictionary. Each data type has its own properties and methods, and Python automatically assigns the appropriate data type based on the value assigned to a variable. For example, if a variable is assigned a whole number value, it is automatically assigned an integer data type. Similarly, if a variable is assigned a value in quotes, it is assigned a string data type.
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OOPs Concepts in Python

OOPs in Python is a programming paradigm based on the concept of objects. It provides the features of classes, objects, methods, and attributes. Encapsulation, inheritance, and polymorphism are some of the key features of OOP in Python that allow for more organized, reusable, and maintainable code. Encapsulation hides the implementation details of a class from other parts of the program, while inheritance and polymorphism help to reduce code duplication and make the code more flexible. OOP in Python is a powerful tool that can make programming easier and more efficient by promoting modularity, reusability, and maintainability of code.

Numbers in Python

Python provides three main types of numbers: integers, floating-point numbers, and complex numbers. Integers are whole numbers with no decimal points, while floating-point numbers include decimal points or scientific notation. Complex numbers consist of real and imaginary parts.
Read here to know more

Python supports all basic arithmetic operations and includes many built-in mathematical functions in its math module. It also supports various number systems, such as binary, octal, and hexadecimal.

Python is a popular choice for scientific and numerical computing because of its powerful capabilities for handling large datasets, machine learning, and artificial intelligence.

User-Defined Functions

User-defined functions are created using the "def" keyword in Python, allowing for the creation of reusable code. Functions can accept input parameters, return values, or modify global variables. Properly designed functions improve code organization and readability, reduce code duplication, and simplify debugging. Python also supports passing functions as arguments to other functions and returning functions, enabling the creation of more flexible and reusable code.

Exception Handling

Exception handling in Python allows for handling runtime errors that may occur during program execution. The try-except block catches and handles exceptions that are created when an error occurs. Multiple except blocks can be used to handle different types of exceptions. The finally block executes regardless of whether an exception occurred or not and is typically used for cleanup tasks. The raise keyword allows for manually raising exceptions for indicating error conditions or triggering specific types of exceptions. Exception handling is crucial for writing reliable and error-free Python code, and it greatly enhances the overall usability and reliability of software applications.
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Prerequisite

There are no strict prerequisites for learning this Python online tutorial, as it is a beginner-friendly language. However, having some basic programming knowledge, such as familiarity with programming concepts and logic, can be helpful.

It is also recommended to have some understanding of computer science fundamentals, including algorithms and data structures. Additionally, having knowledge of basic mathematics, such as algebra and calculus, can be useful for numerical and scientific computing applications.

Audience

• This tutorial will be helpful to computer science and IT students who are curious about learning basic to advanced concepts of Python.
• It will also act as a handbook for graduates and professionals (application programmers, database administrators, software engineers, product managers and end-users) who would like to delve deep into the domain.

Start Learning Python with Scaler

Once you've worked through this python tutorial, the natural next step is applying Python to real projects. Scaler's Data Science and ML and AI/ML programs build directly on these fundamentals with mentor-led, hands-on learning.

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