Python has been around since the late 1980s, but it’s never been more popular than today. It can be challenging for a new programmer or programmer looking to learn Python to determine the best way to get started with the language.
To get up and running quickly, you’ll need a solid understanding of what the language does, what it’s useful for, and how to use it to meet your programming needs.
This beginner’s guide to using Python will help you understand how Python compares to other languages, where you can use it, and some of the situations where Python isn’t ideal so that you can decide whether or not it will work best for your programming needs.
Python is a general-purpose, high-level programming language that supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles.
Python has been extensively used in academia and industry, and some of its qualities include a very clean syntax and its use of whitespace indentation instead of curly braces to delimit blocks, both of which make it easy to learn and read code.
There are a lot of uses for it! Its most common use is as a scripting language, and it works great in smaller projects with limited features and power. It’s also relatively simple to learn, so even those without coding experience can also use it.
These factors make it ideal for quick or medium-sized tasks that don’t require a lot of time investment. If you need something done quickly, Python might be your best bet.
Another widespread use of Python in data analysis. Its ability to parse text and manipulate data makes it very useful in these situations. If you need more information about specific parts of your project, Python can help find answers by collecting data on them automatically (if needed).
Some other uses include
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Python has also become a hugely popular data science tool, with an enormous number of libraries dedicated to solving any number of numerical and statistical problems.
This makes it ideal for creating and analyzing datasets, so Python could be a great choice if you’re interested in data analysis or artificial intelligence.
It’s also worth noting that many popular machine learning and deep learning frameworks have been built using Python. Python may be a good place to start if you’re looking to get into these fields.
Additonal Read: How to Fix ValueError?
At its core, Python can be used as a general-purpose programming language, allowing you to do almost anything you want with it. You might choose to use it in a traditional development role, like writing scripts or tools that automate processes within your organization.
You might even decide to build a full-fledged application on top of it. But beyond that, some specific industries have latched onto Python in recent years, and here are some of them and how they use Python.
There’s been a rise in enterprise software being written using Python. It’s easy for companies to get behind because it provides scalability, flexibility, and speed to market at an attractive price point. Since it allows for so much value at scale, big players like Google and Amazon have adopted it into their stacks (Google uses their variant Go).
As data science matures as an industry, we see more businesses adopting open-source frameworks, essentially where Python shines!
For Designing Web Apps
Because of its simple syntax and readability, it’s not surprising that developers consider Python ideal for creating websites and web apps. Learning how to code in Python is easy. Compared to other programming languages like C or C++, there are very few exceptions to the rules.
Moreover, you don’t need a lot of specialized knowledge because it has fewer features than other coding languages do. This makes it easier to pick up, especially if you’re new to coding.
It also means that once you know how to program in Python, you can use your skills on any platform
While Python is perhaps most widely known as a general-purpose programming language (GPPL), it’s also commonly used in more specialized fields. In particular, data scientists increasingly turn to Python when building AI systems.
Several critical aspects of Python appeal to data scientists: Besides providing programmers with immense flexibility, modern versions of Python are highly portable; many developers agree that no other GPPL comes close to matching its combination of portability and readability.
And then there’s speed which makes sense, given that Guido van Rossum developed Python as an extension of his earlier project, ABC. Finally, Python offers excellent support for machine learning, including several frameworks designed specifically for machine learning tasks.
If you have a passion for building games—from puzzles to role-playing to first-person shooters—you might find yourself at home with Python. This language is great if you need to write simple game engines or tools that help you build better games.
And it’s also been around long enough to have libraries available for just about any game genre under the sun. Whether you want to make something like Super Mario Bros., Pac-Man, or an MMORPG (massively-multiplayer online role-playing game).
There are plenty of open-source frameworks that can help you out. One of these frameworks even has its own graphics engine based on OpenGL called PyGame.
Many other gaming languages are C/C++ based but use Python as their scripting language due to its flexibility and portability across platforms.
Conclusion
Python is a universal language. If you’re starting in data science or web development, certain aspects of Python will make your journey more accessible and more fun.
At its core, though, it’s a powerful general-purpose programming language capable of accomplishing just about anything you can dream up.
By following along with these tutorials on applications for Python, I hope to inspire you to learn more about coding and Python itself. Happy learning!
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