What is Python?

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.

Why Python?

This language has a fairly natural syntax, which means that we actually write commands in a language similar to English, and Python does it, without overloading the syntax with parentheses and punctuation marks. In addition, Python has a large amount of functionality by default and many additional libraries available on the web, which gives a wide range of tools already available in the language itself without having to implement them on your own. Regardless of the wealth of ready-made Python functions, in this course you will probably write many programs that already exist in Python.

Python is available to everyone for free — you can get it from https://python.org. I suggest that you read the contents of this page, but I do not recommend installing Python directly from it. Detailed instructions can be found later in this lecture.

Who is using it?

Google, Yahoo, Nokia, IBM and NASA use Python in their multi-million or billion dollar applications and projects. Both Microsoft and Apple offer full support for Python in their operating systems and development platforms. Many websites such as YouTube and Grono are written in Python.

NASA has been using Python for many years. One of the well-described implementations is using Python applications to manage shuttle launch controls.

Python in engineering applications

The simplicity of Python and the possibilities it offers have made it an increasingly used tool for engineering and scientific calculations. However, despite the fact that Python offers a wide variety of functionalities by default, its full use in these specific areas requires the use of some additional libraries. These are:

  • numpy — a library that adds an efficient type of multidimensional number arrays and is the basis on which all subsequent libraries are based,
  • scipy — tools for advanced scientific calculations: statistical calculations, numerical integration, searching for extremes and null places of functions, interpolation, etc.,
  • matplotlib — advanced library that allows you to create graphs (examples),
  • pandas — a fast, powerful, flexible and easy to use open source data analysis and manipulation tool.

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