Discover what Python is, its history, pros and cons, and how it compares to other programming languages. Perfect for beginners, this guide will help you understand why Python is a popular choice for developers.

What is Python? A Beginner’s Guide to Understanding Python Programming.

If you’re new to the world of programming, you’ve likely heard the term “Python” thrown around. But what exactly is Python, and why is it so popular among developers? In this article, we’ll explore Python, its history, and how it stacks up against other programming languages. By the end, you’ll have a solid understanding of what Python is and why it might be the perfect choice for your coding journey.

What is Python?

Python is a high-level, interpreted programming language known for its simplicity and readability. Created by Guido van Rossum and first released in 1991, Python emphasizes code readability and allows developers to write clear and logical code for small and large-scale projects.

Brief History of Python

Python’s development began in the late 1980s and was first released in 1991. Guido van Rossum started the project as a successor to the ABC programming language, aiming to address some of ABC’s shortcomings while retaining its strengths. Python’s name was inspired by the British comedy series “Monty Python’s Flying Circus,” reflecting the language’s focus on making coding fun and accessible.

Over the years, Python has grown significantly in popularity and functionality. Key milestones include the introduction of Python 2.0 in 2000, which brought new features like list comprehensions and a garbage collection system. In 2008, Python 3.0 was released, offering improvements and breaking changes to enhance the language’s consistency and performance.

Pros and Cons of Python

Like any programming language, Python has its strengths and weaknesses. Let’s take a closer look at the pros and cons of using Python:

Pros of python;

1. Smooth learning curve

Python is extremely beginner-friendly and relatively easy to learn. No surprise that most universities teach Python as an introductory language. Thanks to its simplified, English-like syntax, it allows newbies to concentrate on programming fundamentals, concepts, and good coding practices rather than on the intricacies of the language structure. As a result, students quickly adopt a specific developer mindset.

2. High speed of development

The clear, concise syntax simplifies and speeds up not only learning Python but building software with it. Additionally, its standard library grants a lot of pre-built features that allow programmers to work with Internet protocols, manage operating systems, manipulate data, or integrate web services with less effort.

As a result, the same program will likely take fewer lines of code in Python than in more verbose Java or C++. This makes it a sought-after technology for software prototyping: You can test and prove your idea quicker (and cheaper) than with other languages.

Development performance is boosted even more by available Python frameworks that provide pre-coded components. Instead of creating everything from the ground up, programmers may capitalize on ready-to-use building blocks.

Among the most popular Python frameworks for rapid web app development are

• Django. It offers nearly 10,000 packages, covering almost every aspect of web app design — database connections, HTML templates, user authentication, CAPTCHA protection, APIs, content management systems, and many more. Facebook and Instagram use Django for their backends.
• Flask. This lightweight, easy-to-set-up technology is categorized as a microframework. Flask lacks many in-built features provided by full-fledged enterprise frameworks like Django. Instead, it gives developers the
• freedom to structure an app as they like and use third-party libraries of their choice. Flask is supposed to be more suitable for small web apps. At the same time, it fuels large projects such as LinkedIn and Pinterest.
• Falcon. Another minimalistic framework focuses on building high-performance web APIs and app backends. The list of Falcon users includes PayPal and Opera Software.
• Python is also a component of the LAMP stack, which stands for Linux, Apache, MySQL, and Python, PHP, or Perl (all dynamically-typed languages.) It provides a standard and mature way of building web applications, saving development time.

3. Portability and extensibility to other languages

Python is platform-agnostic: You can run the same source code across operating systems, be it macOS, Windows, or Linux. The portability is achieved due to bytecode and a Python Virtual Machine (PVM) that serve as mediators between a developer and an actual CPU executing the program.

Besides that, Python easily teams up with other languages using extensions like Cython for C, Gython for Go, Jython for Java, and IronPython for .Net. They allow developers to mix languages, borrow functionality their main technology lacks, and run the ‘foreign’ code inside their applications.

4. Versatility plus extensive toolset for almost everything

Python can be applied to a wide range of tasks beyond software development. In particular, it facilitates the work of researchers, data scientists, data engineers, QA engineers, and DevOps specialists. It’s also one of the most popular instruments for financial analysis. The number of Python libraries fueling the above-mentioned fileds and others hits 137,000. Below, we’ll look through the most popular use cases of the language and the tools behind them.
Deep learning and AI. Python leads the pack in deep learning thanks to the vast collection of dedicated libraries. Among them are

• TensorFlow, created by the Google Brain team to train and run deep neural networks (DNNs);
• Keras, working on top of TensorFlow, focuses on user experience and simplifies DNN construction; and
• PyTorch, Facebook’s AI Research lab (FAIR) introduction used to drive computer vision and natural language processing projects.
  
  Scientific programming and data science. Scientists, engineers, financial analysts, mathematicians, and other experts involved in data-driven research are the key users of the Python-based SciPy ecosystem. It contains such powerful tools for data analysis and visualization as pandas, NumPy, scikit-learn, and matplotlib.
  
  
  Robotic process automation. Python is a go-to language for writing lightweight programs — scripts — for automating mundane business tasks (robotic process automation, or RPA). Examples of such operations are filling out online forms, reading and writing files, creating and removing folders, sending emails and notifications, web scraping, and requesting data via APIs.

Some tools commonly used to build RPA applications with Python are

• Beautiful Soup for extracting specific elements out of XML and HTML files,
• RPA Framework, with its collection of libraries for most common repetitive tasks,
• Scrapy, one of the most popular frameworks to build web crawlers — bots that systematically browse web pages and scrape them; and
• PyAutoGUI to automate repeated mouse clicks and keystrokes.

Test automation. Python has a reputation for a plethora of test frameworks that speed up checking the quality of your code at every stage of the software development life cycle. QA engineers can take advantage of

• PyUnit, part of Python’s standard library;
• pytest, a preferred tool for databases, UI, and especially API testing;
• Locust for load testing;
• Splinter for testing web apps;
• Hypothesis for property-based testing when your code is checked against hundreds of different scenarios; and
• many other tools.

Test automation is a critical element of DevOps methodology, based on the idea of merging development, quality assurance, and IT operations into a single, repetitive cycle to speed up software delivery.

DevOps. DevOps is all about optimization and automation across the software development life cycle, and this is where structured and readable scripts written in Python shine. Many DevOps tools are Python-based, including

• SCons and PyBuilder for build automation (converting the source code into an executable program);
• Ansible for configuration management; and
• Buildbot for continuous integration (CI).

Other popular DevOps instruments feature out-of-the-box support for Python. Among them are a Gradle build tool and platforms for continuous delivery and integration (CI/CD)such as Travis CI, Strider CD  (continuous deployment), and TeamCity.

5. Free technology with a large talent pool and vast global community

Python is open-source and free of charge for everybody, even when it comes to commercial use. Given all other strong points, it’s no wonder that the language enjoys the largest talent pool. As of 2019, there were 8.2 million Python developers while programmers using Java numbered only 7.6 million. Since then, the community has increased even more, with Python keeping the title of the most wanted language among programmers for the fifth year in a row.

There are more than 1,600 official Python user groups across 37 countries, with the total number of members in excess of 860 thousand. You can also get help from peers and enhance your programming skills via Stack Overflow, boasting over 1.8 million Python-tagged questions, a Reddit community of 859,000 Pythonistas, or Real Python Slack chat.

Cons of Python

As great as it is, Python doesn’t fit every project. Here are its key disadvantages to keep in mind when choosing the right technology for your case.

1. Speed limitations

You already know that Python is a champion of development speed. Yet, it can’t boast the same results in speed of execution, lagging behind C ++ and Java.

You most likely remember that the Python program is interpreted at runtime, line by line, instead of being compiled to the machine code in one piece. While this brings advantages in terms of debugging, it comes at a price of the runtime performance.Another inherent delay factor is dynamic semantics. It simplifies things for programmers, as they don’t need to declare types of variables and can do with fewer lines of code. Instead of the developer, the interpreter has to check and assign types as a program runs, making it slower.

2. No multithreading

Python uses a mechanism called a Global Interpreter Lock, or GIL for short, that allows only one sequence of bytecode instructions (thread) to be executed at a time. GIL brings important benefits as it

• ensures thread safety,
• enhances the performance of single-threaded programs, and
• simplifies the integration of non-thread-safe C libraries with Python.
  
  

At the same time, the GIL creates a bottleneck for multithreaded programs designed to run multiple workflows simultaneously. This prevents you from taking full advantage of modern multicore processors, which can execute several tasks concurrently.

There has been a lot of debate about removing the GIL. However, no feasible solution has been detailed because too many Python features, modules, and packages are tied to it. Besides that, throwing out the GIL would degrade the performance of software with single-threaded architecture.

To overcome the GIL’s limitation, you can use Python’s multiprocessing package that enables the use of numerous subprocesses instead of threads. In this scenario, each process uses its own interpreter and memory space while the operating system schedules the parallel execution of jobs across different CPU cores. In fact, we still use separate threads for every process; while in multithreading, all processes executed in parallel share the same memory space.

But parallelism comes with one big disadvantage: It devours a lot of memory resources.

High memory consumption

Python is often criticized for high usage of resources, even when multiprocessing isn’t involved. Its objects come with a huge overhead and can gobble up ten times as much memory as is needed to store information we actually require.

While Python has a garbage collector to manage memory, it doesn’t return resources to the system immediately after the object becomes unnecessary. Besides that, if your code holds any references on this outdated object, it can’t be deleted at all.

Due to these factors, Python programs tend to run out of memory. Developers have to make an extra effort to pinpoint the sources of the problem and enhance memory management, especially when it comes to processing large amounts of data.

Hassles with mobile and front-end development

No smartphone natively runs Python: native Android app development is done with Java or Kotlin while iOS gets along with Objective C and Swift. Developers can still build for mobile in Python with Kivy and BeeWare cross-platform frameworks. However, these tools are less popular than Microsoft-owned Xamarin, React Native by Facebook, and Google’s Flutter.

Despite all the advantages, Python fails to get traction in the growing mobile market. Why? It consumes too much memory and energy compared to what mobile hardware and operating systems can afford.

Python vs. Other Programming Languages

Python stands out in the programming world for its simplicity and versatility. But how does it compare to other popular languages?

Python vs. JavaScript:

Ease of Learning: Both languages are beginner-friendly, but Python’s syntax is often considered more straightforward
.Use Cases: Python is widely used in data science, machine learning, and web development, while JavaScript is essential for front-end web development and is increasingly used on the server side with Node.js
.Performance: JavaScript generally performs better in web environments, while Python excels in data processing and backend development.

Python vs. Java:

Ease of Learning: Python’s concise syntax makes it easier for beginners, while Java’s syntax is more verbose and complex.
Use Cases: Java is prevalent in enterprise-level applications, Android development, and large systems. Python is favored in academia, startups, and fields like data science and AI
.Performance: Java’s performance is typically better due to its compiled nature, while Python’s interpreted nature makes it slower but more flexible.

Python vs. C++:

Ease of Learning: Python is much easier to learn compared to C++’s complex syntax and manual memory management.
.Use Cases: C++ is used in system/software development, game development, and applications requiring high performance. Python is used for rapid development, scripting, and fields like AI and data science.
.Performance: C++ offers superior performance due to its compiled nature and low-level programming capabilities, while Python trades some performance for ease of use and development speed.

Python is a powerful and versatile programming language that has earned

its place as a favorite among beginners and experts alike. Its simplicity, extensive community support, and wide range of applications make it an excellent choice for anyone looking to start their coding journey. Whether you’re interested in web development, data science, or artificial intelligence, Python provides a solid foundation to build your skills and projects.

By understanding Python’s history, pros and cons, and how it compares to other languages, you’re better equipped to decide if Python is the right language for you. So, why not give Python a try and see where it takes you on your programming adventure?