Python isn’t a new programming language. Invented in the 1990s, Python has however shot to prominence in the last decade. Today, you can hardly attend any major developer conference or participate in a programming forum without Python coming up in conversation. And once you learn it, it’s easy to see why Python is so popular.
Programming with Python is powerful, efficient, versatile, readable, easy to learn. It is platform-independent, free, logging-friendly and is backed by vast third-party libraries. Whereas Python has found application in numerous situations, there are certain areas where it has become the leading programming language used.
Below are some of the key ways of how developers can apply Python.
Python-based frameworks such as Flask and Django are increasingly popular with web developers. These frameworks enable you to build backend code in Python. This is the code running on the webserver (as opposed to the front-end code on user browsers and devices).
Without a web framework, it is much harder to build shared backend logic such as mapping multiple URLs to a section of Python code or generating the coherent well-structured HTML files on the end user's browser.
You’ll have to choose between using Flask and Django. Flask provides flexibility, simplicity, and detailed control. Django is user-friendly, more elaborate and comes with database interfaces, directory structure and an admin panel out of the box. That, however, means it’s more rigid than Flask.
If you are building a website or web application where you need extensive customization and deep control over the components, then Flask is the way to go. On the other hand, if you want to build an e-commerce store or news site that requires regular maintenance. Then Django is your best bet.
Machine learning (ML) is the future of the workplace. No longer will programming be about creating applications that are static and only respond to inputs that they have been preconfigured to act on. Rather, developers will design applications that can dynamically grow their knowledge based on learning experiences in a similar way that the human brain does.
For example, if an ML application is exposed to 1,000 dog images and 1,000 goat images, it should be able to distinguish between a dog and a goat when it’s present with an image of either one.
Some of the most popular ML frameworks and libraries today are built on and for Python. Tensorflow and Scikit-learn are two of the best known. Scikit-learn is great for people who are new to ML and trying to apply the basic principles. Tensor-flow is a low-level library and therefore best suited for persons who want to maximize their ML algorithm’s efficiency.
Data Analysis and Visualization
Data analysis isn’t really new. Humans have done it in some shape or form for at least thousands of years. What distinguishes data analysis today from what it was just 50 years ago is the volume of involved information.
Today’s businesses and consumers generate huge quantities of electronic information every day. This data often hides invaluable insights that the company could use to better its products and processes. In the absence of elaborate data analysis and data visualization tools, these insights would remain hidden.
Of course, the quality of the data tools you can build using Python will greatly depend on your knowledge of data analysis and visualization principles as well as the fundamentals of statistics.
These 3 may be the most prominent applications of Python programming but certainly not an exhaustive list. Since it’s fairly easy to grasp, there’s a growing army of non-techies who are exploring the different ways they can apply Python to everyday problems.
As a programming language, Python may still be trailing Java in terms of overall use. Nevertheless, Python’s dynamism means it’s going to be a long time before it stops being the go-to language for many beginner and expert coders. If you need Python skills for your project, feel free to acquire those from Diceus.