Python is one of the world’s most popular programming languages, and it’s for many reasons. Its versatility, compact syntax, flexibility, and ease of learning are only some factors that make this language high in popularity. But what is Python used for?
If you’re interested in what this programming language can do – specifically, can you use Python in the real world and if it’s true that this language finds its purpose in all industries – his article will try to explain just that.
Advantages of Python
Before we dive into the fields where Python is used, it’s worth outlining its advantages. Knowing what makes this language popular will help you better understand its wide usage.
In fact, thanks to its numerous advantages, Python is an all-time favorite for both new and experienced programmers. Its simplicity makes it easily accessible and not overwhelming to learn. In addition, the syntax resembles the English language, so it’s not hard to read like other languages.
Also, Python uses line breaks to define its code blocks instead of symbols, which makes for a more organized and eye-friendly environment.
The built-in classes and functions also make development faster. The vast number of libraries allows professionals to code much easier and quicker. Also, since Python is an interpreted language, programmers can execute and text their codes immediately after writing. The compilation process doesn’t exist.
Finally, Python is super versatile. It’s a cross-platform language, meaning anyone can write it on any operating system – Windows, macOS, Linux, and others. All you need is a dedicated Python interpreter. Other languages require programmers to change the code for different platforms.
What Is Python Used For?
Thanks to the abovementioned advantages, Python can be used in various applications in different industries. Here are some of the most common uses of this programming language.
Python is most commonly used in data science. Data analysts, data scientists, and other professionals working with large data sets mostly rely on R and Python to manipulate, sort, analyze, and present data. Professionals can perform complex calculations, data visualization, create machine learning algorithms, and complete data-related tasks without much effort.
Data scientists work in Python to help organizations grow and learn more about themselves. This language also allows managers to identify weaknesses and react to them to achieve better results.
Python offers plenty of data visualization options, including charts, bar graphs, line graphs, 3D plots, histograms, and more.
It’s never simple to harvest valuable information from data. To avoid complicated syntax and compiling, professionals rely on Python’s easy syntax and a range of third-party libraries like Keras or TensorFlow.
So, if you consider a career in data science or data analytics, you’ll certainly be encouraged to get at least basic knowledge of the programming language to answer the question “What is Python used for?” This is mostly because some of the most popular data mining and analytics tools are written in this language.
Even though machine learning is a data science subcategory, its unique algorithms make it count as a separate field. Machine learning is about teaching a system to learn on its own with the help of algorithms. These algorithms are programmed to update themselves all the time depending on the input data.
Machine learning systems can deal with new situations thanks to the past datasets that allow them to generate output. Then, depending on the new outcome, they update the system to let new variables in. This action and reaction process evolves constantly, allowing the machine learning system to become more sophisticated.
To implement all machine learning aspirations, it’s essential to use a stable, readily available, and flexible language. Python has all that and more, as it covers all parts of machine learning from development, deployment, and maintenance.
Also, library access is an important part of why Python is suitable for this field – plenty of related libraries are written in this language. Some of the most popular examples include Keras, TensorFlow, NumPy, SciPy, Theano, Pandas, and others.
The back-end part of web development deals with the servers of web applications (the part the user doesn’t see). The servers also interact with APIs after users request data from the front-end. This part requires plenty of database work. Luckily, most back-end functions can be operated with Python. Python can send data to and from the server, communicate with databases, process data, perform URL routing, ensure security, and more.
For the most part, Python’s popularity in this field is thanks to Flask and Django, two popular libraries and frameworks that make this programming language especially useful for such applications.
Some popular web development jobs that require Python include full-stack engineers, back-end engineers, software engineers, Python developers, and DevOps engineers.
Developers can also rely on Python to create desktop applications. Popular GUI libraries such as wxWidgets, Tk, or Qt allow the use of Python to create cross-platform applications on macOS, Windows, Linux, and other operating systems.
Financial technology companies often rely on Python to perform financial analysis. Most of the financial industry relies on Python professionals thanks to the complex data processing capabilities and libraries built specifically for financial analysis.
Applications in financial services are often super complex. The simplicity of Python’s syntax helps organizations create software or bring market innovations faster. Python also reduces the error rate in these applications, which is crucial in one such sector.
Finally, the financial sector needs to be more responsive and agile to meet customer demands. This means that personalized experiences and additional services are an everyday part of the financial specialist’s efforts. Thanks to its scalability and versatility, but also the Django framework, Python developers can create robust MVPs to make product or market finding easier and faster.
Python is at the core of many business operations and applications. More and more startups start to include Python as part of their tech stack for its scalability and ease of use.
Some of the most popular tools for enterprise development such as Tryton or Odoo rely on Python for performing inventory, accounting, customer relationship management, and many other business-related tasks.
Python doesn’t necessarily have to be used by data scientists and programmers exclusively. This language offers a range of possibilities in professions that don’t rely on data as much.
Small business owners, journalists, or marketers can use Python to simplify daily tasks in their everyday life. For example, the following tasks can be automated thanks to this robust programming language:
- Sending yourself reminders
- Updating grocery shopping lists
- Keeping track of crypto prices
- Converting text documents to spreadsheets
- Search for files or texts across multiple files
- Update, create, rename, or move folders
- Renaming large file batches
- Filling out forms automatically
- Search Engine Optimization (SEO)
SEO experts rely on Python to automate tasks and extract and analyze large data sets. This programming language allows marketers to locate issues on the websites, find broken links, automate small tasks, and alleviate the risk of human error.
Since SEO consists of plenty of repetitive tasks, Python helps automate them and leaves the professionals with more time to deal with other, more dynamic SEO aspects.
Programmers can code blockchains without writing a lot of code. You can even write a basic blockchain with less than 50 code lines with this programming language. Also, Python allows blockchain developers to perform many tasks with a single command.
Finally, since Python doesn’t require compilation for machines to read it, it makes bug fixing much less of a hassle. The programmer doesn’t have to stop the application, go back to the source code, fix it, recompile, and restart the program. They can simply fix the bug and start the application again, which is a huge plus in blockchain development.
Scripting and Utility Software
Python can be considered a scripting and programming language since it operates as both an interpreter and compiler. In the beginning, Python was only used for utility script writing. Up to this day, many programmers use it for this purpose. Much of the scripts and utilities in Linux systems use Python. Python is also the default language in macOS.
Although Python isn’t the most popular gaming development language, it does come with some advantages when used for this purpose. Its coding speed and simplicity make it an excellent choice for prototyping. Programmers can see the effect of their work right away, so they can offer investors a playable project in fast turnarounds.
If you’re a beginner game developer, you can create great projects in Python, thanks to its beginner-friendly syntax. There is a special library called PyGame designed to help game developers who use Python. Still, given that other languages such as C++ are much faster, Python isn’t a preferred option for serious game development.
Understanding the Scope of Python
As you can see, Python can be used in just about any modern-day industry or field. Its ease of use, compact syntax, versatility, and cross-platform capabilities make it an excellent option for both beginner and advanced programmers.
If you’re considering a career in web development, data science, financial analytics, or machine learning, you’re sure to come across different Python usages in your career. So, the sooner you start learning this robust language – the better!
Hopefully, we managed to answer the question “What is Python used for?” and helped paint a picture of how versatile Python is in real-world applications and why it’s worth exploring a career in using it.
Read also: Python vs. C++