History of Python

In 1989, Dutch software engineer Guido van Rossum created Python as personal side project. He was a junior programmer  at the Netherlands’ national research institute for mathematics and computer science (Centrum Wiskunde & Informatica). The first release in 1991 was Python 0.9.0 and it had features such as classes, exception handling, functions, and the core datatypes like list, dict, str, and so on. It was heavily inspired by ABC, a language that Guido spent some time implementing at CWI. While creating Python, his goal was to take the good parts of ABC while fixing the rest.
Contrary to popular belief, Python is not named after the snake species, but after the comedy group Monty Python.

Growth of Python

In the last few years, Python has experienced an incredible  growth as one of the most popular languages among developers and companies.
According to stackoverflow, Python passed SQL to become the third most popular programming language in 2021.
Forbes  has put Python seventh on the list, with a 456% growth rate last year. The reason behind the huge demand for Python is that it is easy to learn and understand. Also, its code takes less time to develop and maintain than any other programming language, speeding up the development process. With Python, developers can develop applications more quickly.
It is flexible and it can be used in any industry for most things, from building Raspberry Pi applications and data mining or Deep Learning to large-scale applications and scientific computing. Python is quite easy to understand and it is now the first choice for many programmers and  university students  since they always look forward to learning a language that is in high demand.
Many programmers and data science students are using Python  for their development projects. Learning python is one of the important sections in data science certification courses.  In this way, the Python language can provide plenty of fantastic career opportunities for students
Python has also democratized data science by opening it up to a much wider range of people. As this has happened, and the Python community has innovated to make the language a first-class option for data science, languages like R have declined.

Reasons of success

The wide and varied uses of the Python programming language are a key part of why it has become so popular since its inception. Python is robust, versatile, scalable and offers solutions to professionals from a huge range of industries, not just developers. This, combined with how simple and easy is to learn the language, helps to explain how Python became so dominant in so many different fields.

– Active and Supportive Community

For obvious reasons, programming languages that lack documentation and developer support just don’t fare well. Python has neither of those problems. It’s been around for quite some time, so there’s plenty of documentation, guides, tutorials and more. That means any time someone needs help or support, they can get it in a timely manner.
This active community helps ensure that developers of all skills levels — beginner to expert — always have somewhere to find support. And, as any experienced programmer or developer knows, when you run into development issues in the middle of crunch time, support can either make or break you.

– Libraries

Python  comes with a vast collection of libraries, which allow companies to add a lot of features without reducing the load time. It has an excellent selection of packages, from NumPy and Pandas for data wrangling, SciPy for scientific computing to Django or Flask for web development, etc. There are even a few libraries with a more specific focus, like scikit-learn for machine learning applications and nltk for natural language processing.

– Uses

Python can be used to develop different applications like web applications, graphic user interface-based applications, software development, scientific and numeric applications, network programming, Games and other business applications.

Web Applications

Python comes with multiple web development frameworks like Pyramid, Django, and Flask. These frameworks are packed with standard libraries that allow easy protocol integration and lead to a reduction in development time. Python allows you to create a web application with minimum problems. Python contains libraries for internet orders like JSON, XML, HTML, IMAP, e-mail processing, easy socket interface, and FTP. It also comes with libraries such as Requests (HTTP client library), BeautifulSoup (HTML parser).
Top websites and platforms like YouTube and Google rely heavily on Python for critical infrastructure.

GUI Desktop apps

Python may help develop desktop applications without a keen knowledge of programming. Python offers various frameworks (Kivy, PyQT, Tkinter, etc) for different purposes and solutions with integrated tools and functionalities. Frameworks make development focused on application logic rather than common elements; They automate the implementation of tasks. A quick way to start building your GUI applications is to use tkinter. This module comes in the Python standard library.

Data Analytics

Much like AI and machine learning, data analytics is another rapidly developing field that requires Python programming. At a time when we’re creating more data than ever before, there is a need for those who can collect, manipulate and organize the information. The Pandas library is the perfect fit while working with large amounts of information, it’s useful for manipulating data and carrying out repetitive tasks.

Data Science

Python also offers a wide set of libraries for data science. For data scientists, it’s important to be able to represent data in a human-legible format and Python can help with that. Libraries like Matplotlib help data scientists in building histograms, charts and plots for data visualization and effective comprehension. Some of the many Python libraries that support machine learning algorithms include Keras, TensorFlow, MatplotLib, and Scikit-learn.
Python is also preferred for artificial intelligence and machine learning because of the widespread libraries that it has. The language supports complex calculations and algorithms, including regression, vector machines, clustering k-means, and others. ML-based projects require in-depth mathematical knowledge, and Python makes it much easier with built-in commands and reusable libraries.


Python is an extremely useful programming language for cybersecurity professionals because it can perform a multitude of cybersecurity functions, including malware analysis, scanning, and penetration testing tasks. It is often recommended as the first language to people new to cybersecurity because of its wide use and minimal learning curve.


Python is easy to read, used everywhere, and has a vast community for support. In fact, of all the programming languages, Python is always considered the easiest to learn. That´s why it has been adopted not only by programmers but also by non-programmers professionals such as scientists, accountants, traders, financial analysts, project managers, etc. They use it for their daily tasks, to organize and manipulate large amounts of data and to automate boring and repetitive tasks so they can focus on the important part of their jobs.



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