Python Tutorials For Beginners and Professionals
Hello Everyone! Here, we are starting a Python Tutorials Series For Beginners and Professionals. In these Python Tutorials, we will cover all the features of Python. You will learn from basic to advanced-level features of Python as we progress in these Python Tutorials.
This Python Tutorial is for Whom?
This Python Tutorial is designed for students, beginners, and professional developers who want to learn Python step by step, from the very basic to the advanced concepts, using real-time examples. These tutorials provide a hands-on approach to the subject with step-by-step program examples that will assist you in learning and putting the acquired knowledge into practice.
What is Python?
Python is a high-level, interpreted, general-purpose programming language emphasizing on readability and simplicity. It was created by Guido van Rossum and first released in 1991. Python’s design philosophy revolves around code readability, notably using significant whitespace. It provides constructs that enable clear programming on both small and large scales.
Key Features of Python:
- Easy to Read and Write: Python is often compared to executable pseudocode due to its clear syntax and readability, which makes it an excellent choice for beginners in programming.
- Interpreted Language: Python is processed at runtime by the interpreter. You do not need to compile your program before executing it, unlike languages like C or Java.
- Dynamic Typing: Python is dynamically typed, which means that you don’t have to declare the type of a variable when you create one. The type is determined at runtime.
- Multi-Paradigm: Python supports various programming paradigms, including procedural, object-oriented, and functional programming.
- Extensive Standard Library: Python comes with a large library, providing tools suited to many tasks and reducing the need for external libraries.
- Cross-Platform Compatibility: Python interpreters are available for many operating systems, making Python programs cross-platform.
- Open Source with a Vibrant Community: Python is an open-source language with an active community contributing to its development and offering extensive support.
- Wide Range of Applications: Python is used in various domains, such as web development (Django, Flask), scientific and numeric computing (SciPy, NumPy), desktop graphical user interfaces (Tkinter, PyQt), software development, and system automation.
- Extensible and Embeddable: Python can be extended with code written in other languages like C or C++, and it can be embedded in applications written in languages that can call C libraries.
- High-Level Language: Python handles a lot of complexity for the user, so it is very user-friendly because you do not need to manage the memory yourself, unlike lower-level languages.
Why the Name Python?
There was a TV show named Monty Python’s Flying Circus, which was a very popular fun show in the 1970s. While creating Python, Guido also used to read this show’s published scripts. Guido needed a short and a different name for this language. Hence, he named it Python.
Why You Should Learn Python?
Learning Python offers numerous advantages and opportunities, making it a highly recommended language for beginners and experienced developers alike:
- Ease of Learning: Python is renowned for its simplicity and readability. Its syntax is clear and concise, making it an ideal first language for beginners. It’s also powerful enough for advanced programming, making it a great choice for all levels of expertise.
- Versatility: Python can be used for various applications, from web development and data analysis to artificial intelligence (AI) and scientific computing. This versatility makes it a valuable tool in many fields.
- Strong Demand in the Job Market: Python’s popularity in various sectors, including data science, machine learning, and web development, has led to a strong demand for Python developers in the job market.
- Rich Libraries and Frameworks: Python has a vast ecosystem of libraries and frameworks. Libraries like NumPy and Pandas are essential for data analysis, while frameworks like Django and Flask are great for web development. This rich ecosystem makes Python suitable for many tasks without starting from scratch.
- Data Science and Machine Learning: Python is a leading language in data science, machine learning, and AI, thanks to libraries like TensorFlow, Keras, and SciKit-Learn. These fields are rapidly growing and offer exciting career opportunities.
- Community and Support: Python has a large and active community, which means plenty of resources, tutorials, and forums for learning and troubleshooting. This community support is invaluable for both beginners and experienced programmers.
- Integration Capabilities: Python can be integrated with other languages and platforms, allowing it to work seamlessly with existing infrastructure and applications. This makes it a practical choice for diverse development environments.
- Automation and Scripting: Python is an excellent tool for automating repetitive tasks and scripting to increase efficiency and reduce the time spent on routine tasks.
- Academic and Research Applications: In academia and research, Python is widely used for scientific computing and research. Its simplicity allows researchers to focus on solving problems, not language complexities.
- Web Development: With frameworks like Django and Flask, Python is also a strong candidate for building web applications, offering robustness and scalability.
- Rapid Prototyping: Python’s simplicity and the vast array of libraries available make it an excellent choice for rapid prototyping and experimentation.
Python, being a dynamic and evolving programming language, has seen several versions over the years, each introducing new features and improvements. Here’s a brief overview of some major Python versions and their key features:
- Python 1.0 (January 1994): This was the first official version of Python, released by Guido van Rossum. It laid the foundation of the language with essential features like exceptions, functions, and core datatypes.
- Python 2.0 (October 2000): Introduced list comprehensions and a garbage collection system capable of collecting reference cycles.
- Python 2.2 (December 2001): Brought in unified classes and types, making everything an object.
- Python 2.3 (July 2003): Added support for DateTime module, enumerated types, and new import hooks.
- Python 2.4 (November 2004): Decorators for functions and methods were a major new feature.
- Python 2.5 (September 2006): Included conditional expressions, the with statement, and the functools module.
- Python 2.6 (October 2008): Backported many Python 3.0 features for easier transition.
- Python 2.7 (July 2010): This was the last major version in the 2.x series. It included an ordered dictionary type, new syntax for set literals, dictionary and set comprehensions, and a string formatting method.
- Python 3.0 (December 2008): This version was a major revision of the language, not backward compatible. Key changes included print becoming a function, views and iterators instead of lists for dict methods, and explicit relative imports.
- Python 3.1 (June 2009): Added an ordered dictionary and various optimizations.
- Python 3.2 (February 2011): Featured new syntax for nested with statements and a new stable ABI.
- Python 3.3 (September 2012): Introduced yield from for generator delegation, a new virtual environment (venv), and namespace packages.
- Python 3.4 (March 2014): Brought in pip as a default package manager, asyncio for asynchronous IO, and ensured PEP 484 (Type Hints) compatibility.
- Python 3.5 (September 2015): Added async/await syntax, type hinting, and unpacking generalizations.
- Python 3.6 (December 2016): Introduced formatted string literals, variable annotations, and asynchronous generators.
- Python 3.7 (June 2018): Added built-in breakpoint(), data classes, and improved asyncio module.
- Python 3.8 (October 2019): Brought in the Walrus operator (:=), positional-only arguments, and more robust typing extensions.
- Python 3.9 (October 2020): Introduced dictionary merge & update operators, type hinting generics in standard collections, and new string methods (removeprefix and removesuffix).
- Python 3.10 (October 2021): Added pattern matching, parenthesized context managers, and precise types.
Python 3.11 and Beyond
- Python 3.11 (Expected in 2022): This version is anticipated to include faster execution, exception groups, and more pattern-matching features.
Each new release of Python aims to make the language more powerful and easier to use while also improving performance and developer productivity. As Python continues to evolve, it is important for Python programmers to stay updated with the latest changes and improvements.
Prerequisites to Learn Python:
To learn Python effectively, especially if you are new to programming or coming from a different programming background, several key areas and skills can help you get started:
- Basic Understanding of Computers and Programming: Familiarity with basic computer operations and the general idea of what programming is about is helpful. This doesn’t require in-depth technical knowledge, but a general understanding of how computers work and what programming languages do is beneficial.
- Problem-Solving Skills: Programming is fundamentally about solving problems and implementing solutions. The ability to think logically, break down problems into smaller parts, and approach them systematically is crucial, regardless of your learning programming language.
- Basic Mathematics: While advanced mathematics is not required to start with Python, having a good grasp of basic arithmetic, algebra, and logical reasoning can be beneficial. Python is often used for data analysis, scientific computing, and machine learning, where more advanced mathematical knowledge may become relevant.
- Access to a Computer: Python is a versatile language used across different platforms. Having access to a computer where you can install Python and practice writing and running scripts is essential. Python can be run on Windows, macOS, and Linux operating systems.
- Understanding of Basic Computer Science Concepts (Optional): While not a prerequisite, having a basic understanding of computer science concepts like algorithms, data structures, and computational thinking can enhance your learning experience. These concepts become more relevant as you advance in your Python programming journey.
Note: If we missed any topics in this Python Tutorials For Beginners and Professionals Course, then please let us know by giving a comment in the Comment Box, and we promise as soon as possible, we will publish articles on that topic in this Python Tutorials For Beginners and Professionals course.
Python – Basics
Python – Functions, Modules & Packages
Python – Data Structure
Python – Object Oriented Programming
Python – Exception Handling & Files
Regular Expressions & Logging
Python – Multithreading
Python – Database Connectivity
Popular Python Books
About the Author: Pranaya Rout
Pranaya Rout has published more than 3,000 articles in his 11-year career. Pranaya Rout has very good experience with Microsoft Technologies, Including C#, VB, ASP.NET MVC, ASP.NET Web API, EF, EF Core, ADO.NET, LINQ, SQL Server, MYSQL, Oracle, ASP.NET Core, Cloud Computing, Microservices, Design Patterns and still learning new technologies.