Is Python the Future

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Simon Mclellan

Writer, developer.

Python is getting a lot of attention these days. It seems like everywhere you look in the software development world you find projects either based on, or making heavy use of this high-level programming language.

You might find it surprising that this humble little interpreted language, first released in 1994, is at the forefront today in some of the most cutting bleeding-edge areas of software development, but it’s true. Since it’s initial implementation, Python has evolved into a powerful, extremely flexible, and easy-to-learn language that is uniquely suited for its role today in machine learning, data science, and other bleeding-edge areas of tech.


If it’s so amazing for all of these modern technologies, does that mean that Python is the future of programming? A lot of tech soothsayers make a big deal of how Python is “taking over”, or infiltrating the tech sphere. That might be somewhat overly-dramatic, but it’s also sort of true.

The more pragmatic commentators will note that Python is extremely useful in some areas that are important to the most exciting new technology. This doesn’t mean that it’s the only programming language you’ll ever need, but it does mean that Python is worth your time and attention.

To understand where Python is going, you need to know what Python is, where it is used, and what it can do. Read on for the answers to those very questions.

What is Python

Python is a high-level, interpreted programming language that is easy to implement across multiple platforms. Python is well-suited for use in data science, analysis, statistics, and machine learning applications. The language’s flexible approach lets developers easily switch between object-oriented, structured, and functional programming paradigms and handle large amounts of unstructured data.

Where is Python Used

Python is widely adopted for use in enterprise-level applications where large-scale data handling is important. According to this Wikipedia article.

TechRepublic quotes the Netflix development team as saying, “The ability to drop into a bpython shell and improvise has saved the day more than once.“. You might also recognize some of the other big companies that make extensive use of Python. They include Google, Instagram, Spotify… The list goes on.

With such widespread adoption in the development space, it’s easy to see how some people might look at Python as a big part of the future of coding.

What Can You Do With Python

There are certain areas where Python programmers are in particularly high demand. Data science and analysis, machine learning, and AI applications are some of the bleeding edge areas of the tech industry where Python is most useful. These types of projects are leading the industry in innovation, and that’s a big part of why Python has gained a reputation as one of the programming languages of the future.

Big Data Applications

Big data is really big. Most of the really fun stuff happening in the tech world right now is somehow tied to working with ridiculously huge quantities of data.

Handling petabytes (1 billion megabytes) and exabytes (1 trillion megabytes) of largely unstructured data is tricky business. The tremendous scale of data management and analysis needed to carry out the processes driving big companies like Amazon and Facebook are Python’s bread and butter. The language is inherently capable of handling a large variety of data, and it is made even more capable by the large and extensible libraries created by the ever-growing developer community.

Data Science and Analysis

Collecting and storing data is one thing, but finding meaning in that data is a science all to its own. Data science is one of the fastest-growing industries within the tech sphere. Data scientists pore through the heaps of data that are raked in by Facebook, Amazon, Google, and all the others and build applications to parse it and find useful patterns.

The nature of big data analysis is ever-changing and deals inherently with unstructured and unpredictable data sets. To make matters even more interesting, a data analyst often has little idea of their goal when beginning a project. Since this science is about finding meaning in data, it requires a fair amount of flexibility and adaptability.

As an extensible and widely supported language, Python provides the flexible tools that data scientists need to perform their work.

IoT (Internet of things)

Internet of Things (IoT) as a concept is everywhere. From refrigerators with wifi to self-driving cars, the tech of the future runs on decentralized and self-sustaining networks. The going theory on how these networks can be effectively implemented is what’s behind all the buzz about IoT.

The idea is that each device that uses a network also serves as part of that network’s infrastructure. If a sufficient number of devices exist, internet connectivity is possible without traditional infrastructures like trunk lines, switching stations, and other heavy hardware. IoT is one of the most important concepts that will make AI useful and ubiquitous in the future.

Again, Python is a great tool for IoT development because of its rapid adaptability to new paradigms and the fact that it’s easily able to handle changing data structures. In emerging technologies like IoT, adaptability is key.

Python as an Intermediary

Perhaps a less glamorous function that Python serves very well is as an intermediary between mission-critical components of a large piece of software. With dynamic typing, high-level data structures, and a knack for cross-platform implementation, Python is an excellent tool for gluing together pieces of software programmed in other languages.

At first, this use of Python may sound like a hack, but consider the case of rapidly changing big data sets. A project may start out tracking one very well-defined set of data and pivot midstream to something unexpected. Because development cycles don’t always keep up with the pace of innovation, large portions of a project might already be implemented using the old data. Rather than throw out that data and all the software that goes along with it, you can use Python to glue the components together.

Gluing of data sets is such an established practice that python has a library dedicated explicitly to this practice. It’s called Glue. Amazon AWS and other cloud services offer all kinds of gluing-related stuff, too.

As big data gets bigger, and data scientists get faster at finding stuff to do with it, gluing will continue to be an important part of building data-driven software. This means that Python will continue to be a relevant language in the future.

Limitations of Python

Even though it’s useful in a ton of exciting areas of software development and data science, Python can’t do everything.

Some of Python’s main strengths are also its major challenges. The things that make it flexible also strangely make it less compatible. The things that allow it to handle flexible data types also cause more workload for testers.

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  • As an interpreted language, Python depends on OS-specific interpreters to make applications work across platforms. This makes Python super flexible for developers as they move from one system to the next, but it also means that Python developers aren’t able to create standalone cross-platform applications.
  • Speed is an issue with Python. It’s well-known that Python struggles to compete with C++ and other languages in terms of speed and performance. There are workarounds from the developer community that help Python achieve faster speeds, but it’s inherently not the best for high-performance applications.
  • Dynamically typed languages require more intensive testing across various platforms. This is because variable typing occurs at compiling time and the developer has less control at run-time. Since data types are not defined statically in Python, developers need to test their applications on every different platform where they will be implemented.


These are only a few of the limitations that Python faces when comparing it to other modern programming languages, but these shortcomings are enough to make it clear that Python is not a standalone solution for every situation.

The good news is that the large and active developer community working with Python is constantly innovating and creating new libraries that extend its capabilities.

What to Expect from Python in the Future

Based on its usefulness in bleeding-edge techs like AI and machine learning, you can expect Python to be around for a long time. It’s got an enthusiastic developer community. It is ideally suited to working the big data. It’s easy to learn and dynamically typed.

For the things that Python does well, there’s currently no better alternative. As long as there is big data to work with, and a need for flexible data analysis solutions, Python will continue to be a valuable skill for software developers.

Python is used in enough big projects right now that, even if no further Python applications were started, there would continue to be plenty of work for Python developers for years to come. Just maintaining and supporting all those apps will keep the Python community busy well into the future.

In all likelihood, tech innovation will continue to generate more need for flexible data-driven software, and that simple fact makes Python a tool for the future.