WHY PYTHON? PART 3: APPLICATION EXAMPLES

Web development

Python is our preferred language for web development. In particular, by using powerful frameworks such as Django and Flask, web applications can be developed quickly and efficiently. These frameworks offer extensive features and tools that facilitate the development of secure and scalable web applications. Django offers a complete package with a built-in admin interface, authentication system, and ORM, which accelerates and simplifies the development of complex web applications. Flask, on the other hand, is a lightweight micro-framework that is ideal for smaller projects and APIs where flexibility and extensibility are paramount. 

Example:
Companies can use Django to develop their intranet applications from scratch. Django’s extensive features, such as built-in authentication and the admin interface, can help companies create a robust and user-friendly platform that reduces development effort and makes it easy to implement new features.

Data Science & Big Data

Data science is one of the areas where Python truly shines. With libraries like Pandas, NumPy, and Matplotlib, data analysis and visualization can be performed easily and effectively. Pandas offers powerful, user-friendly data structures and analysis tools, while NumPy provides extensive support for numerical computations. Matplotlib enables the creation of engaging and informative visualizations that make complex data patterns understandable. The integration of these libraries allows data scientists to efficiently process, analyze, and visualize data. 

Example:
A financial services provider can use Python to analyze large amounts of financial data and develop predictive models. By using Pandas and NumPy, complex data patterns can be identified and valuable insights gained, leading to better investment decisions.

Artificial intelligence (AI) & machine learning (ML)

Python is the dominant language in the fields of AI and machine learning, thanks to libraries such as TensorFlow, Keras, and Scikit-learn. These tools enable the development of advanced ML models and AI applications. TensorFlow and Keras offer comprehensive frameworks for building, training, and deploying neural networks, while Scikit-learn provides a wide range of algorithms for machine learning and data preprocessing. With these libraries, our developers can create complex models that process large amounts of data and make accurate predictions. 

Example:
A healthcare company is developing an AI system to diagnose diseases using medical images. By using TensorFlow, neural networks were trained that are capable of recognizing disease patterns with high accuracy. 

Find more examples of AI-based applications here: 

LLMs, data mining, intelligent image processing, and more…

Cross-platform app development

Python is also ideal for developing applications that run on various platforms. Frameworks like Capacitor enable the creation of cross-platform apps using a single codebase. There are also smaller frameworks and tools, such as Kivy and BeeWare. Kivy offers an open-source Python library for the rapid development of applications that run on iOS, Android, Windows, macOS, and Linux. BeeWare enables developers to create native user interfaces for various platforms and provides tools for building and distributing apps. 

Example:
A startup is developing a mobile app for budget management that works on both iOS and Android. By using fastAPI, Vue.js, and Capacitor, the company can offer a consistent user experience on both platforms while reducing development time and costs. In addition, the application can of course also run as a web app, i.e., in a browser. 

Learn more about app development in our services: 

Mobile Apps & Web Applications from a Single Code Base Using a Cross-Platform Approach

Automation & RPA

Python is ideal for automation tasks, ranging from simple scripts to complex workflow automations. With libraries like Selenium and Beautiful Soup, routine tasks can be efficiently automated. Selenium enables the automation of web browser interactions, which is particularly useful for test automation and web scraping. Beautiful Soup is a powerful tool for extracting data from HTML and XML files. These tools can be combined to create extensive automation processes that save time and resources. 

Example:
A marketing company uses Python to automate the process of collecting data from various online sources. With the help of Selenium, web scraping scripts can be created that collect relevant data daily and store it in a central system. 

Learn more about automation in our service portfolio: 

Business Process Automation

Interfaces & middleware

Python is ideal for developing interfaces and middleware that connect different systems. Libraries such as Flask and FastAPI make it easy to create APIs and middleware solutions. Flask is a lightweight framework that is ideal for developing smaller APIs, while FastAPI offers powerful tools for creating modern and fast APIs. These frameworks enable the integration of various systems and services, allowing data to be seamlessly exchanged and processed. 

Example:
A logistics company develops a middleware solution to integrate various internal systems and external partner platforms. By using FastAPI, the company can develop a powerful and scalable interface that makes data exchange more efficient. 

Learn more about interfaces & middleware in our service portfolio: 

Interface Programming

Robotics

In robotics, Python is widely used due to its simplicity and the availability of libraries such as ROS (Robot Operating System). ROS provides a flexible framework for writing robot software. Python enables easy control of robots and the integration of sensors and actuators. With Python, complex robot control systems and interactions can be developed, allowing for the precise and efficient execution of tasks. 

Example:
A research team is developing an autonomous robot that can navigate and perform tasks such as object recognition and manipulation using Python and ROS.

Financial technology (FinTech)

Python plays a key role in the fintech industry, where it is used for algorithmic trading, risk management, and financial analysis. Libraries such as Pandas, NumPy, and Scikit-learn enable the analysis and processing of large amounts of data, as well as the development of machine learning models. Python also offers tools for connecting to financial data sources and for implementing trading algorithms that automatically trade on the stock market. 

Example:
A FinTech startup uses Python to develop trading algorithms that automatically trade on the stock market while analyzing large amounts of data.

Internet of Things (IoT)

Python is frequently used for programming IoT devices because it is easy to learn and can run on resource-constrained devices. Libraries such as MicroPython and Zerynth support the development of IoT applications. MicroPython offers a lightweight implementation of Python designed specifically for microcontrollers, while Zerynth provides tools for creating and managing IoT applications. These libraries enable the development and control of IoT devices that communicate and interact within networked systems. 

Example:
A smart home company uses Python to develop a control platform for IoT devices that integrates lighting, heating, and security systems in a smart home. 

A customer example of microPython’s use in IoT devices: 

App-based access control system for Prima Resorts