Python is one of the most popular languages at the time, and new applications and uses are being found for it every year. With the emergence of cutting-edge technologies like AI and machine learning and the dissemination of activities related to data, this open-source language has earned a position at the top for programmers and students.
Top Python uses and trends
As we’ve said, there are many needs for which Python has become the favorite solution. Some of them are:
- Machine Learning and AI
- Web development
- Robotics and Hardware
However, before we go deep into each of the practices and the way Python is used in those environments, it’s important to know the reasons why Python is usually used, and what its particularities are.
Why’s Python so popular
Python has a set of quirks that make it unique among other languages, both for its versatility for application and its attractiveness to developers. Let’s check on some of them.
First of all, Python is a general-purpose language. This basically means it wasn’t designed for a specific task from scratch, and it can be used for a wide variety of activities and areas. Domain-specific languages, on the contrary, are built to work in a particular area, which allows them to improve simplicity and performance while decreasing their range.
Besides being general-purpose, there are several other factors that make Python versatile. First of all, it has a native binding to C. As a result, it inherited the power and years of experience of many C-based languages such as C++ and Fortran, allowing many engineers to handle it quickly and well.
Easy to learn
Python is one of the most recommended languages for beginners, and one of the best reasons for it is the simplicity of its syntax. Python was created with the aim of making it as near as possible to the English language, reducing the number of total characters to be written in the code and making its lines easier to remember.
This particularity has created a large community of developers, which takes us to the third reason for the language’s popularity.
Thriving community and libraries
Being developed around 1990, Python has been circulating for some time. And both because of its user-friendly approach and for its native relation with C, it has developed a large, open, enthusiastic community. This gives place to the availability of many libraries and frameworks as a second result.
Uses and applications
It’s commonly said that Python’s overrated because of being beginner-friendly. In this sense, the main argument is usually slowness, as it is interpreted on the run unlike compilable to native code languages. On the other hand, it’s also true that languages like C++ can be used with the same ends as Python.
However, simplicity isn’t an important value only for beginners or low-skilled developers; It’s useful in general for the sake of the communication, manageability, and correction of programs. Many times, achieving a simpler solution is the key factor for choosing a programming language.
Being that said, it’s time to go deeper into Python’s versatility. In which areas is it mostly used? Let’s see.
Machine Learning & Artificial Intelligence
Technologies based on machine learning and AI are improving user experience and optimizing functionalities across many industries. When it comes to software development specificities, these kinds of projects have several requirements: Most of all, great flexibility is needed, but trading off consistency for it isn’t an option.
In this sense, Python’s large collection of libraries and frameworks allows people to dedicate themselves fully to machine learning problems; this way, they skip unraveling technical problems that are already solved by somebody else. In the end, it leads to better-built and more solid coding.
In the case of data professionals, Python is by far the most popular programming language. There are many good reasons for this, like libraries’ availability and community participation. The definitive one, however, is its simplicity.
As we’ve said above, Python has an accessible language that’s easy to learn, and data professionals prefer building their programs and prototypes in this language without losing too much time with complicated requirements. Besides, it allows data engineers to export CSV output that can be easily read in spreadsheets. In the case of needing something more sophisticated, Python can be ported to languages like C or Java.
It’s important to have in mind that data scientists do not necessarily have a developer’s education, and programming is something they learn in order to apply to specific data processes and not for any other functionality.
Python provides several plotting libraries, including Matplotlib, Seaborn, and many other data visualization packages, each with unique features for creating informative, customized, and appealing plots to present data in the simplest and most effective way possible.
Most of all, Python’s popularity for data engineering and data analysis makes it much more comfortable to keep working with the same technology for visualization instead of changing to a new one.
Python’s code is simple and easy to understand, which simplifies web and software development services and makes the process more efficient. Python’s compatibility with other languages is another appealing feature, making it a more versatile option.
Some effective frameworks, such as Django and Pyramid, aid developers in Python software development and allow them to create web apps from scratch. Python’s standard library also supports a variety of internet protocols, including XML, JSON, and HTML. Instagram is one such Django-based application.
It is a versatile, adaptable, and highly efficient programming language with dynamic typing capabilities. Python enables programmers to create scientific applications, system applications with graphics, games, command-line utilities, web applications, and a variety of other options.
Finances and blockchain
Python is often used in fintech because it’s great for dealing with large amounts of data, and it’s relatively easy (for a senior developer) to make an airtight app with military-grade security.
When it comes to blockchain, Python’s low learning curve makes it simple for developers to adopt it in a reasonable amount of time and even allows less experienced developers to contribute to Blockchain projects right away.
Besides, Python can be used to code a blockchain without writing a lot of code. Python, for example, allows you to write a simple blockchain in less than 50 lines of code.
Robotics and Hardware
Robotics and hardware consist mostly of research programming, and there are few languages better suited for it than Python. Some of the main reasons for this are:
- It’s simple: There is no build process, a REPL, simple syntax, and dynamic typing.
- It communicates with C/C++: Because they are C counterparts, many Python libraries are just as fast as their C counterparts.
- It has numerous libraries: You can rely on them in order to focus in the main needs of your program.
- It is (mostly) platform-independent: Works well on Linux and OSX (the primary platforms for robotics), and mostly on Windows, depending on the libraries used.
- Python is a core language of ROS (Robot Operating System), which means that you can access the full power of a distributed robotics system and all of its libraries/tools through Python.
As you may see, Python is a very versatile language that’s particularly popular among new technologies. Many of the coming tech trends can be developed in this language, which makes it an important tool for any developer or data professional.
If you are looking for top-level developers to work on your Python projects, check out our solutions here!