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Python is one of the most popular programming languages in the world today. With use cases in  virtually every  avenue of technology, it is easy to understand the reasoning. Offering simple, readable code, it is often implemented by – and for – many non-technical and technical people alike. From web development to banking to everything in-between, Python has grown exponentially since its appearance on the development scene in the late 1980s.

Here are some tech industries where Python is heavily relied upon and areas of potential growth for Python developers in the coming years.

Python for Web Development

Web Development and Python are fairly synonymous today, with frameworks (such as Django and Flask) and well maintained libraries supporting the language and web development; it’s easy to see why. Python is heavily used in comparison to similar languages and  frameworks like Javascript and Node.Js, Ruby on Rails, C# and .NET, and even Java and Spring.

Today, probably most commonly, Python is used on the web in conjunction with frameworks like Flask and Django. Of course, there are far more frameworks available, including  CherryPy, FastAPI, and even web2py, but Flask and Django dominate the market – and for good reason. Contrary to the domination of Express in Javascript development, Flask and Django offer two different use cases within Pythonic Web Development. Django offers a more opinionated boilerplate of code out of the box, including an ORM for interactions with datastores. On the other hand, Flask is more minimally opinionated, relying on the developer to make more decisions on their preferred ORM, amongst other ‘no-batteries’ feature sets. For a more full bodied list of differences, consider this post discussing “Flask vs Django”.

Python for Data Science

The field of Data Science in 2021 is composed of developers, statisticians, and the like, traditionally specializing in Python and R. With R being used for more deep-rooted mathematical problem solving and numerical analysis, Python pairs quite well to access datasets and perform minimally invasive operations on that data before transitioning to R workloads. With popular libraries that are actively supported, like Pandas, SciKit-Learn, Tensorflow, and Numpy, Python certainly has its place in the Data Science community.

Currently, with sub-fields such as machine learning and Artificial Intelligence (AI) relying upon much of what Python and so many libraries offer, the language is much of a defacto-standard for anyone in the ‘Data’ field today. Check out a more comprehensive review of each of Python’s Data Science libraries.

Python and Systems Development/Testing

Systems development can be an incredibly varied field, covering everything  from defense systems to logistics systems, the spread of use cases can span vastly. Python supplies a great paradigm to not only build those systems but also to use for testing of those systems and any integrations that might come alongside it. Commonly organizations of larger size will even abstract their systems down from those built on programming languages like  C or C++ using Python for testing to achieve cheaper labor/development costs and easier recruiting.

With implementations like CPython and Jython, the integrations with larger  systems and modules become even easier to conform to, thanks to Python’s extensibility. In addition, Python is commonly used in place of the earlier Bash scripting in terms of developing utilities, jobs for Cron scheduling, and similar system built-ins. With infrastructure varying greatly today and becoming increasingly skewed across hybridized hosting, it is highly sought after to be nimble in cross-platform development. With Python being used for local scripting, the skillset is already in place for cloud hosted problems of similar source, making it a breeze for the developer to adapt to those situations.

Python for IT Operations

Similar to Systems Development and Testing, IT Operations benefit greatly from using Python in normal, everyday practices. With a large number of IT offerings, services, and so on either having Python SDKs or agnostic APIs, IT teams can wield Python to test and determine uptimes, status’, or even reachability. All of this is highly utilized in terms of IT monitoring and visibility. With system utilities such as Cron and Task Scheduler, Python can be implemented on a schedule occurring once a minute, once a month, or once a year. Combined with an easily readable design by nature, IT Admins typically select Python over other programming languages to develop their utilities when functionality greater than Bash is necessary.

Python for Infrastructure

Infrastructure is one area where Python particularly shines. With implementations like boto3 and SDKs for Azure and GCP, Python offers a swift implementation of cloud resources for organizations to utilize with great speed. Today, there are even products like Pulumi, which makes it even easier to work with cloud resources with more  structure than ad-hoc or regimented script libraries. With tools like Terraform and other IaC libraries for each cloud available, Python is still heavily sought after for the durability of its scripting, which offers more use than just cloud provisioning.

Leveraging cloud monitoring tools can be dynamically configured to accommodate new resources upon deployment jobs or infrastructure versioning with Python as well. This proves highly valuable in organizations that greatly value constant monitoring and reliability. For further reading on Pulumi, visit:

For further reading on boto3, see:

Other Python Uses

Python is no not merely limited to the uses described above. Python is heavily used today in businesses of all shapes and sizes. It is used to automate rudimentary tasks, to test and correct data ingestion, to make systematic phone calls, and even grade schoolwork. With so many use cases in Python’s arsenal  today, it is increasingly difficult to argue the viability of learning how to use it as a common trade; it is certainly a great addition to any developers resume and programming toolkit.

Python is used in schools, professional organizations, enterprise grade businesses, and non-profit projects. With such a heavy adoption and such easily readable code, it can also be argued that Python is one of the easiest prototyping languages. Developing new tools and applications can greatly be hastened by Python’s simplistic development approach. In addition, it offers an easy to remember and recall ecosystem in whiteboard activities (a common interviewing technique for programmers).

So what are you waiting for? Go out and write your own Python “Hello World” program and get started!

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