After successful completion of the course, students are able to...
...write simple python scripts/programs in the scientific context.
...use NumPy, SciPy, and Matplotlib.
...describe the properties of built-in data types (numeric types, boolean type, strings, collections, NumPy arrays).
...describe the options for controlling the program flow.
...compare functions and object oriented programming with other programming languages.

- Built-in types
- Branches, loops, exceptions, modules
- Functions and namespaces
- NumPy, SciPy, and Matplotlib
- Classes

Lecture (Powerpoint with audio); quizzes (TUWEL);  homework developing a program step by step; discussion of homeworks (Zoom meetings).

This course is a slightly expanded version of former course 362.171. The seminar 362.153 will be held in the summer term.

Python is a versatile programming language with a clear, compact syntax. It combines the features of traditional programming languages (Fortran, C/C++, Java) with those of higher-level tools like matlab with, e.g., easy access to operating system operations. Python therefore is on the rise; in the scientific context this is indicated by the SciPy project that coordinates the development of Python tools for scientific computing, and by the fact that an increasing number of software written in traditional programming languages offer Python interfaces that allow their more flexible control and postprocessing of the data.

Homework, oral exam.

Knowledge of a higher programming language like C is required. Familiarity with the basic principles of object oriented programming is an advantage but not required. Knowledge of Python is not assumed.