How we work with Python | Topics
When I think of Python, the images in my mind are completely different from those of my fellow developers. For them, the mental image is not of a reptile from the scale-covered reptile family, but rather a scripting language with abstract data structures and a simple yet effective approach to object-oriented programming. The programme code is executed at runtime by an interpreter within a corresponding runtime environment. Python was developed in the 1990s by Guido van Rossum, who conceived it as a successor to the educational programming language ABC.
Python programmes are characterised by a clear and well-organised structure; in particular, they support the rapid implementation of ideas (rapid prototyping). The language is ideally suited to a wide range of applications and is used, amongst other things, for data analysis, automation, front-end and back-end development, as well as countless other applications.
The use of Python is particularly noteworthy in the fields of machine learning and AI development. Suitable Python libraries for this include TensorFlow, NumPy, PyTorch and many others. Precisely because the programme code is relatively easy to read and understand, the language is ideally suited to complex and intricate topics. However, one disadvantage when compared directly with static or compiled languages such as C or C++ is the sometimes significantly lower execution speed, due to the interpretative execution of the programme code at runtime.
You can find a programming example demonstrating the use of Python and how the inputs and outputs of the sys WORXX CTR-700 can be controlled via the CAN bus in the detailed programming example.
Python in practice
Due to its scope and versatility, Python is suitable for a very wide range of applications. It is used for data analysis, automation, front-end and back-end development, as well as countless other applications. Google, for example, has chosen Python as its standard scripting language for web applications.
Particularly noteworthy is its use in the fields of machine learning and AI development, for which it is exceptionally well-suited due to its structure and characteristics (stability, flexibility, available tools and extensions, etc.). Python libraries suitable for this purpose include TensorFlow, NumPy and PyTorch. It is precisely because Python is relatively easy to read and understand that it is so well suited to complex and intricate topics.
