The Python programming language has come a long way since its introduction in the 1990s. Guido Van Rossum hardly knew that Python would become one of the most popular languages in the world at the time he was developing it. Today Python is one of the most widely used programming languages on the planet and has many different uses. Whether it’s an enterprise-level application, machine learning, artificial intelligence models or work in the field of Data Science, Python is actively used in almost all prosperous industries and fields.
Current script for Python
There are over 8 million Python developers in the world who regularly use this language for a variety of purposes. Due to its flexibility and easy scalability, Python has already become the preferred language for many developers. This was the reason why Python was able to bypass Java, which has long been a favorite language among developers. But it can also be related to the natural process of language aging, with which Java is approaching its end. Most new languages are designed to solve modern problems. Although languages developed for a long time are most effective for solving the problems of their time, it becomes extremely difficult for them to remain relevant for changing industries and scenarios.
This article was translated with the support of EDISON Software, which gives practical advice to juniors, as well as designs software and writes TK in Russian and English.
However, Python, an open language with a large and supportive community, remains relevant and is at its peak even today. Rich libraries and built-in functions make it popular among organizations, enterprises, developers and experts in the field of Data Science. Despite the fact that Java is still used for corporate development, its relevance in other areas is close to zero. If you look around, you won't find a machine learning specialist who designs and trains Java models. But despite this, Java remains the second most popular language among developers around the world.
Python has successfully overtaken Java in most areas. In enterprise development, Google’s new Go programming language poses a real threat to Java. However, as you progress, the need for high-performance computing is growing more than ever. These are the modern requirements for Data Science and artificial intelligence models. Although you might think that using fast GPUs can help increase speed and efficiency, the reality is far from that. This approach does not satisfy the needs for information processing. Advanced applications should depend on other things for optimal performance and to help scientists and developers achieve their desired goals. Ultimately, this encourages organizations and research institutes to look for reliable programming languages. Designed for a niche task and providing fast work.
As already mentioned, the world is entering an era when the beloved Python is facing threats from a new player in the world of programming languages - Julia. Viral Shah, CEO of Julia Computing, notes that in the early 2000s, developers preferred to use C for system programming, Java for enterprise development, SaaS for analytics, and MATLAB for scientific calculations. However, modern developers use Rust for system programming, Go for enterprise applications, Python / R for analytics, and Julia for scientific calculations.
However, the scenario was not so few years earlier. When Julia was not on the horizon, people were going to switch from MATLAB to Python. Since machine learning has come to be used in almost every application we know, and the Python libraries provided a much simpler implementation of machine learning models, people switched to Python. Previously, MATLAB was the best option for solving these problems and helped both in analytics and in scientific calculations. But it was obvious that people were looking for easy-to-implement solutions that were clear, fast, efficient, and scalable. And Python was able to occupy both the Java niche and the MATLAB niche.
What is the place of Julia?
One of the key differences between Julia and Python is how these languages approach the same task. While Julia is specifically designed to solve the problems associated with high-performance computing, Python came to this in the process of its development. Despite the fact that Python has so far been able to meet the challenges of the industry, let's agree that it was not intended for this work. Developers and researchers were fortunate enough to let Python evolve and observe how it turns into a language for fast computing. Julia, on the other hand, is specifically designed for high-speed work. This language is only a few months old and has already begun to cause a stir among researchers and specialists in Data Science.
The stable version of Julia 1.2 was released just two months ago and has already been improved to work effectively with demanding projects in the field of Data Science. Right now, over 800 developers are contributing to Julia on Github and helping it become a popular language.
Being resource-intensive and demanding for speed, the two-month-old Julia is already challenging the thirty-year-old Python. Despite the fact that it is difficult to say whether Julia will overcome Python or not, this language will undoubtedly have an impact due to its features designed to work with complex calculations. Moreover, since tasks continue to be resource-intensive and require precise calculations, Julia can win universal love thanks to its high-performance capabilities. If Python does not want to repeat the fate of Java, it will have to develop and try to optimize its libraries for speed and performance. And this may be due not only to the launch of new updates, but also to a complete redesign of the engine to make the language more processor friendly. The advantage Python already has over Julia is its rich libraries. Since Julia is only at the beginning of her journey, it will take a long time to create efficient dynamic libraries and functions, like in Python. The struggle between the two languages has just begun, but it is already benefiting researchers and scientists who need fast and effective tools to achieve their goals.
As torgeek commented: “NVIDIA architects have added Julia to their solution stack.”