Top 5 Python Applications - part 2

Five most popular applications of Python

As already said, Python can be used in all sorts of fields.

We’d like to focus on the top 5 Python use cases:

• web development & application development
• data science
• automation scripts
• computer graphics & image processing
• fintech

Web development and App development

Web development and app development are two highly developing sectors from the past decade for developers.

Web development includes everything necessary to create websites and web-based software applications. Every website consists of two sides - client and server. The client-side runs code on the internet browser on the user’s computer; and on the server-side where the code runs on the web server.

Python runs on the server and processes the business logic behind user inputs, interacting with databases and other servers. As a result, developers can quickly and efficiently build dynamic web applications using Python’s frameworks. They do not need to learn client-side languages like JavaScript. In addition, frameworks reduce development time by automating common web development tasks.

Application development is creating a computer program or a set of programs to perform the different tasks a business requires. Since Python is written to help reduce the development time and effort - it is ideal for prototyping. In addition, thanks to its versatility, speed, scalability, and robustness, it is also an excellent language for enterprise-scale projects. Finally, python is also one of the languages successfully used in many commercial projects as a language in which the "plugin ecosystem" is very easily implemented.

Additionally, Python comes with a database API - it allows easy connection to numerous databases, and Python’s interfacing to languages like C will enable developers to implement functionality from other languages into a Python application.

But the most helpful thing is a huge collection of Python packages. You can use them to implement any functionality you need in your project. But, what if, unfortunately, the package you need does not exist? Yet! Python is open source - create a new package, solve your problems, and share it with other Python users.

Data science

Data science is the practice of extracting information and insight from data. It includes machine learning, data visualization, and data analysis.

Machine learning (ML) enables computers to program themselves; examples of ML algorithms:

• recommender system - e.g. YouTube makes recommendations based on the watching history, and Netflix does the same.

• image recognition systems - e.g. FaceID by Apple recognizes faces and unlock phones.

Python is useful for ML because it is mainly numerical computation. Also, Python creates a simple and easy-to-use ML package that helps to start Machine Learning (scikit-learn). Python has become the language for ML applications with the introduction of TensorFlow, developed by Google. It is the largest end-to-end ML platform.

In the digital world, we are creating more data than ever. There is no doubt about that. DOMO states that “Data never sleeps”, and with over 2.5 quintillion bytes of data (!) created daily, it is hard not to agree. You can see what happens in an internet minute in their infographic here.

Data visualization and analysis is an easy way to look at a large amount of data and understand patterns better. Showing the data visually (rather than just showing a table) makes it more impactful.

Using libraries like matplotlib and seaborn, with just a few lines of code, you can create perfectly readable data visualizations and make decisions based on them.

Automation scripts

Python language shines when it comes to automation scripts. Scripting means writing small programs that have to perform a specific task and do it automatically. As Python was invented in need of quickness and easiness - it is perfect for that. The book “Automate the Boring Stuff with Python” by Al Sweigart shows how to create simple scripts with Python to perform tasks that typically require hours to be done manually.

Computer graphics and Image processing

When it comes to computer graphics and dealing with images with the aid of computers, we can also use Python.

Python's default digital image library, a Pillow (PIL - Python Image Library), adds image processing capabilities to your Python interpreter. It is an open-source library for image processing tasks that requires Python, such as reading, rescaling, and saving in different formats.

This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. It can be used for image archives, processing, and display.

It has all useful functions and is very comprehensive, but at the same time, Opencv has excellent compatibility with Python.

There is also a possibility to use the Python framework Kivy which provides the ability to the canvas directly rendered instructions over the GPU itself.

Lastly, Python can be used to program image processing and animation scripts in software like Autodesk Maya or Adobe Photoshop.

Fintech

Fintech is a technology that automates and improves the delivery and use of financial services. It starts with an online banking portal and goes to blockchain applications. Python, along with Java and C++, is the most popular language for financial services companies.

Fintech demands applications that are easy to use but also robust, secure, and compliant. Python as a language is easy to use and, thanks to its modularity and mathematical base, meets all the criteria. As far as we know, companies like Stripe, Robinhood, and Revolut use Python in their software development.

Stay tuned for more!

In the meantime read more about Python on our blog:

• All you need to know about Python - part 1
• 7 reasons why Python is the best programming language for AI