Software developers have to work with over hundreds of languages, making it difficult to find the best options. However, developers understand the importance of choosing the right programming language, which can affect the outcome of the software. When choosing a technology, developers consider scalability, location, complexity, and application type, followed by a maintenance cycle.
Hence, comes Python.
Python is considered to be a high-level and flexible programming language that is easy to interpret, with an emphasis on code readability. This forces developers to choose the Python programming language, which can help them create first-class applications.
According to the latest TIOBE Programming Community Index, Python is the top 1 (most popular) programming language in 2022. Moreover, it was ranked third by the Stack Overflow survey. Other conclusions include:
- Python is the language developers want to work with most if they aren’t already doing so.
- It is the most wanted language for its fifth year.
All these stats aren’t a surprise at all since Python developers can use it to develop desktop graphics applications, websites, and web applications. In addition, Python, as a high-level programming language, allows you to focus on the core functionality of an application while solving common programming problems. The simple syntax rules of a programming language make it even easier for you to keep your code base readable and your application maintainable. There are also a number of reasons why you should choose Python over other programming languages.
Feel like this technology is exactly what you’ve been looking for? Check out all the Python development services available for today! We bet you’ll find a bunch of solutions for your business!
Python API Development
Application Programming Interfaces (APIs) are used to access some information or functionality over the Internet. They are simplifying data transmission across multiple interactions. For example, you can write a Python program to use the Twitter API to execute a function that can collect tweets for a specific hashtag. The API makes it easy for developers to use certain technologies to build applications using certain predefined operations. It helps in connecting different dots.
Luckily, there are many Python frameworks out there. Let's take a look at some of the popular ones web developers can use to develop APIs:
- Django REST
- Flask Restful
In short, Python API development services can help you improve the performance of your iOS and Android mobile apps or boost information accessibility by simplifying data transfer across multiple interactions.
Python Web Application Development
When it comes to web development, Python should be your go-to tool. Why?
This is because Python offers many web development options. For example, you have Django, Pyramid, Flask, and Bottle for developing web frameworks and even advanced content management systems like Plone and Django CMS. These web frameworks contain standard libraries and modules that simplify tasks such as content management, database interaction, and interaction with Internet protocols such as HTTP, SMTP, XML, JSON, FTP, IMAP, and POP.
Python web frameworks are known for their security, scalability, and flexibility. In addition to this, the Python Package Index contains useful libraries such as Requests, BeautifulSoup, Paramiko, Feedparser, and Twisted Python.
Therefore, leveraging Python web development services you are sure to get security, ease of scaling, and convenience in the development process.
Most prominent examples:
Python IoT Solutions
IoT solutions, simply put, connecting all things with sensors and cloud computing is another great example of Python development services.
For many developers, Python is the language of choice in the market. It's easy to learn, has a clean syntax, and is supported by a large online community. In IoT, Python is a great choice for backend developers as well as device software developers. Also, Python is available to run on Linux devices and you can use MicroPython for microcontrollers.
This article states that
“Using this coding language [helps] reduce the amount of data you have to deal with that is available in the cloud. Whether you're building your IoT project from scratch or interacting with sensors, actuators, and accessories, Python recognizes your requirements."
Some of the many benefits of working with Python for IoT devices are the speed at which you can develop code and a large number of libraries for all types of platforms.
Python is a great ally for device prototyping. Even if you rewrite some of your code during production in C, C++, or Java to improve performance, the overall system will run just fine in Python.
Python for AI & ML
Please, don’t skip this section as AI & ML is the best choice for large-scale analytical systems, business intelligence, NLP, and Computer Vision.
AI and ML models and designs are inherently different from traditional software models. When we talk about AI/ML projects, the tools and technologies used, as well as the skill set required, are completely different from those used in the development of conventional software projects.
The job of machine learning engineers requires a language that is stable, secure, flexible, and equipped with tools to handle the various unique requirements of such projects. Python has all these qualities and hence it has become one of the most popular languages for data scientists.
Simplicity, consistency, platform independence, a large collection of resourceful libraries, and an active Python community make it an ideal tool for developing AI and ML applications. Some of the best Python packages for AI and ML:
- SciPy for advanced computing
- Pandas for general data analysis
- Seaborn for data visualization
- Keras, TensorFlow, and Scikit-learn for machine learning
- NumPy for high-performance scientific computing and data analysis
Apart from these libraries, there are other Python-based libraries such as NLTK, Caffee, PyTorch, and Accord.NET which are useful for AI and ML projects.
Companies using it:
Testing & QA services of Python software
Tech specialists doubt what programming language to learn and use to write tests. Usually, they face the "Python vs Java" dilemma. Here are our arguments in favor of Python development services:
- The Zen of Python, a collection of Python writing guidelines, is the perfect guide to test automation. It reminds you of the basic rules that make automated test scripts effective. Tests should be simple and readable, obvious and relevant, complex but not confusing. Some will say that these things are obvious, but "The Zen of Python" is a manifesto that will not let you forget the basics.
- Pytest is one of the best automation platforms available. It can handle any functional test, whether we're talking about unit, integration, or end-to-end testing. Test cases are written simply as functions and can take parameterized input. Plugins extend the power of pytest and allow you to cover code, run multiple tests at the same time, and integrate with other frameworks like Django and Flask.
- A rich library of useful packages and ready-to-use automation ingredients makes Python testing much easier.
- Python is object-oriented and functional. This allows you to choose what is best for your tasks - functions or classes. Distributed functions have no side effects, and the simple syntax makes them readable.
- The command line can manage the entire test automation workflow. Each test framework can launch a console to search for and run tests. Rich command line support greatly simplifies test management. What's more, automation with Python supports exploratory testing. You can use Python calls to bring your application to the point where manual testing is required. Due to its scalability, Python is equally good for beginners and advanced users alike.
- Scalability is achieved through syntax, superior structure, modularity, and a vast ecosystem of tools. Numerous third-party tools and processes can also be integrated.
Python games solutions
Not so long ago (in pointer #2), we discussed how Python makes web development easier for developers. But if Python can do web development, what can stop it from being involved in game development?
The answer is nothing!
Like web development, Python is equipped with an arsenal of game development tools and libraries, and you would believe it if we said that Python was used to develop one of the most beloved shooters of the early 2000s, Battlefield 2.
For those who are wondering, some of the many 2D and 3D game development libraries that make this possible are PyGame, Pycap, Construct, Panda3D, PySoy, PyOpenGL.
Python has also been used to develop several other modern popular games such as The Sims 4, World of Tanks, Civilization IV, and Eve Online which use Python heavily for most tasks. Mount & Blade, Doki Doki Literature Club, Frets on Fire, and Disney's Toontown Online are some of the few other games that use Python.
In addition to game development, game designers can also use Python to develop tools to simplify certain activities, such as level design or dialogue tree creation, and even use these tools to export these tasks to formats that can be used by the main game engine. Moreover, Python is used as a scripting language by many game engines.
Python CMS development
What sets Python apart from others is its mature and fast CMS. Python is made up of Django CMS which raises the bar in terms of usage.
Another reason why Python is really used these days is because of the framework. Its structure is so adaptable that it can also be converted to a CMS-related structure. This additional feature helps developers quickly respond to customer requirements and easily provide CMS requirements to them.
Python has two big players in the CMS world: Wagtail and Django CMS. Both are well-tested and mature quality solutions with a large community of clients, editors, and above all developers who are constantly working on new features and releasing updates and bug fixes. This is important because it makes the software even more functional and reliable.
Python frameworks are easily adaptable and transformable into a custom CMS, while at the same time acting as building blocks for programming. This is extremely useful when it comes to fast delivery with a limited team as you can have a lot of features like contact forms, WYSIWYG editor, or page hierarchy without coding as they are already implemented.
Both Wagtail and Django CMS are built on top of the Django framework which comes with a pre-built admin panel. This is a huge advantage in terms of CMS development speed with a built-in admin space to manage content, users, and so on.
When you use a mature and relatively secure framework supported by so many vetted Django developers you don't need to spend a lot of time and money on any additional support.
This is why building a Python-based CMS can be the safest alternative.
Python enterprise development
Enterprise software or business applications are very different from standard applications because the former requires features such as readability, extensibility, and scalability. Essentially, business applications are designed to meet the requirements of an organization, not the needs of individual customers.
Thus, these applications must be able to integrate with legacy systems such as existing databases and non-web applications. Since business applications are developed with user requirements in mind to meet the specific needs of an organization's operating model, the entire development process becomes very complex.
This is where Python software development services can make a significant difference. The high performance, scalability, flexibility, and readability of Python are exactly what you need to develop rich and efficient business applications. In addition, there are other tools for developing business applications in Python, for example:
- Odoo is an all-in-one management software that provides a complete suite of enterprise management applications.
- Tryton, a three-tier, high-level, general-purpose application platform, is another great tool for building business applications.
Scientific and numerical applications
With an extensive library base, Python has become an essential tool in scientific and numerical computing. In fact, Python provides the foundation for applications related to computing and scientific data processing. Applications such as FreeCAD (3D modeling software) and Abaqus (finite element method software) are written in Python.
Some of the more useful Python packages for scientific and numerical computing include:
- SciPy (scientific numerical library)
- Pandas (data analytics library)
- IPython (shell)
- Numeric Python (basic number package)
- Natural language tools (mathematical and textual analysis)
Python boasts not only an English-like syntax but also a modular architecture and the ability to run on multiple operating systems. These aspects, combined with rich text processing tools, make Python an excellent choice for developing GUI desktop applications.
Python offers many tools and GUI environments that make it easy to develop desktop applications. PyQt, PyGtk, Kivy, Tkinter, WxPython, PyGUI, and PySide are some of the best Python-based GUIs that allow developers to create highly functional graphical user interfaces (GUIs).
By far one of the most exciting uses of Python is the ability to run on embedded hardware. For beginners, embedded hardware is a tiny computer built to perform limited actions. An embedded application is what controls the hardware i.e. the firmware. Popular examples of such applications include MicroPython, Zerynth, PyMite, and EmbeddedPython.
As of today, we have an exhaustive list of embedded devices because they are almost everywhere. For example, digital cameras, smartphones, Raspberry Pi, and industrial robots are just a few of the many devices that can be controlled with Python. FYI, few people know about this, but Python can also be used as an abstraction layer in device firmware, while C/C++ handles the system layer.
Operating systems are the brains of any computer. And Python is one of the many programming languages that underlie Linux-based operating systems. Well, not exactly the operating system itself, but parts of it.
Two popular real-world Python application instances come from the Ubiquity Ubuntu installer and the Red Hat Enterprise Anaconda installer, both of which were built using Python.
Similarly, Gentoo Linux and Google Portage's Chrome OS package management system are also built using Python. The combination of C and Python is known to be very beneficial when designing and developing operating systems.
Data science and data visualization
Data plays a critical role in today's world. Why?
This is because it is the key to understanding people and their tastes in the things around them by collecting and analyzing important information about them. This is what the entire field of data science revolves around. Data science includes problem identification, data collection, data processing, data exploration, data analysis, and data visualization.
Python is probably the best for data analysis. On top of that, the Python ecosystem offers several libraries that can help you solve your data science problems. We have TensorFlow, PyTorch, Pandas, Scikit-Learn, NumPy, SciPy, and other libraries that specialize in building and fine-tuning deep and machine learning models, data-intensive processing, and manipulation.
Data visualization comes into play when you need to communicate your findings to stakeholders and your team. Now, even for visualization, there is no shortage of libraries in the Python ecosystem. We have Plotly, Matplotlib, Seaborn, Ggplot, Geoplotlib as the most widely used data visualization tools.
Web scraping applications
The Internet is home to a vast amount of information ready to use. Basically, web scrapers crawl the websites they are directed to and store all the collected information from their web pages in one place. From now on, this data can be used by researchers, analysts, individuals, and organizations to solve a wide range of problems.
With simple Python code, creating and using Web Scrapers becomes much easier. Here are some examples of the tools behind Web Scrapers: PythonRequest, BeautifulSoup, MechanicalSoup, Selenium, and a few others. Web scrapers are commonly used in price trackers, research and analysis, social media sentiment analysis, machine learning projects, and probably all real projects that benefit from a huge data warehouse.
Over the years, the design and modular architecture of Python have been the inspiration for the development of many new programming languages such as Boo, Swift, CoffeeScript, Cobra, and OCaml. All of these languages share many similarities with Python in terms of the object model, syntax, and indentation.
Wrapping it up
As our Python guide suggests this technology has a solution for every area. It is currently the most versatile language and has a great future. There is a long list of areas where Python development services are considered the most suitable.
The main fields include machine learning and artificial intelligence, web development, data analysis, game development, IoT, application development, microservices, and game development.
Many industries, including the healthcare sector, the financial sector, the aerospace sector, and the banking sector, rely heavily on custom Python development services.
Many big names have either built their applications in Python or have migrated their entire tech stack to Python. Some of them include YouTube, Google, Instagram, and Pinterest.
World’s best full-stack developers admit that its ability to be deployed in almost any area is amazing. This is provided thanks to its vast ecosystem of diverse libraries. In this article, we have tried to showcase 15 of the best Python applications in the real world.
Failed to pick the right way? Turn to our seasoned software consultants. These guys can tell you what Python is and why you should use it for your next web product i. e. guide you through all the secrets of the software world.