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Python is a versatile programming language with different use cases in different fields. Discover 4 interesting features of Python.

At the end of a Python training module, the basics of programming have been learned. Among them, variables, tuples, dictionaries, conditional statements, object-oriented programming. Now, what’s next? This article presents the different ways to implement Python.

Table of Contents

Python in the real world

Python is a high-level general purpose programming language. As the definition indicates, this language can be used for many purposes, from web development to data science, machine learning and robotics. In fact, its use cases are multiple. The rest of this article explains how to use Python skills in a wide range of fields.

Software Development

Web applications

Developing web applications with Python is among the most in-demand skills. The Python ecosystem provides a rich set of frameworks, tools and libraries for this activity. Some of the most popular web frameworks include Django, FastAPI Flask and Tornado.

Command Line Interface (CLI)

Command line interface (CLI) applications with Python represent another area where Python stands out. These applications allow you toautomate repetitive and boring tasks of daily work. In addition, they create small and large tools for your command line.

Python offers an impressive set of CLI libraries and frameworks that make it easy to create command line tools quickly. In particular argprse, Click Typer.

Game development

Programming has made a big part of video games. A large number of users take up programming not only because they like them but also to recreate their favorite games or design their own games. To achieve this, Python offers several tools, libraries and frameworks in its ecosystem. In particular Arcade, PyGame , pyglet.

Data Science and Mathematics

Machine learning

Machine learning is sometimes the first step for those interested in artificial intelligence. This concept studies the algorithms Which draw information from experience. In fact, they build models from training data samples to make predictions and decisions.

Machine learning is a difficult area to tackle, as it is constantly changing rapidly. However, Python implements powerful tools, including Keras, NLTK, PyTorch, scikit-learn, TensorFlow.

Scientific computing

Python also has an important place in the field of scientific computing. Researchers use the advanced computing capabilities offered by supercomputers, computer clusters or even desktop and mobile devices To understand and solve complex problems.

Libraries and tools used for scientific computing in Python include: NumPy, SciPy, SimPy.

Web scraping

One of the most important sources of information for data science remains the Web. The process of collecting and analyzing raw data from the Web using an automated tool (crawler) is known as “web scraping”.

Python has a large number of tools and libraries for extracting data from the web. Some of them are: Beautiful Soup, requestsest, Scrapy, urllib.requestest.

Automating workflows with Python


Python is among the main technologies used for DevOps. Its flexibility and accessibility make this technology an excellent choice for this task. It allows development teams to improve their workflow and be more efficient and productive.

In its ecosystem, some popular DevOps tools are written in Python. It is also possible to use Python to control most of these tools with Ansible, Docker Compose for example.

Software packaging and deployment

Packaging, distributing, and deploying products to end users is another essential part of your software development life cycle. In Python, a quick and popular way to deploy applications and libraries is to publish them to PyPI.

Tools that can be used for this purpose include Poetry, Flit, PyInstaller, setuptools and Twine.

Developing embedded systems and robots

Writing your own applications for the web or desktop is fun, but writing code that controls the operation of hardware systems and robots proves even more interesting. Areas such as the Internet of Things, home automation, autonomous cars, and robotics have gained popularity due to advances in science and technology.

Python has gradually entered the world of sensors, electric motors, circuits, microcontrollers and robots. Today, there are several projects with Python that go in this direction especially BBC micro: bit, CircuitPython, MicroPython, PythonRobotics.

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