Scientific Python Tutorial

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Python is a high-level dynamic object-oriented programming language. It is easy to learn, intuitive, well documented, very readable and extremely powerful. Python is packaged with an impressive standard library following the so called "batteries included" philosophy. Together with the large number of additional available scientific packages like NumPy, SciPy, pandas, matplotlib, scikit-learn, etc., Python becomes a very well suited programming language for data analysis. This hands-on session aims towards advanced Python beginners, who have already gained some knowledge about Python (Scripting experience and knowing the term list comprehension should be sufficient). This course gives an introduction and demonstrates the power of Python in data analysis using NumPy and pandas.

Structure of the Course

  • Lectures about NumPy, Matplotlib and Pandas
  • Exercises about NumPy, Matplotlib and Pandas.

You should give it a try. You learn things only by applying it!

Software Environment for the Course

  • We will use ipython notebook for the exercises
  • We have prepared ipython notebook servers running on virtual machines at GridKa

All you need is Mozilla Firefox or Chrome installed on your machine. MS Internet Explorer and Safari do not work out of the box!

Instructions how to connect can be found in the Introduction lecture below.

In case you prefer to use your private notebook, you can download the anaconda package from and follow the installation instructions. In addition, you should also have a git client installed.


  1. Lecture
    1. Introduction
    2. NumPy
    3. Matplotlib
    4. Pandas
  2. Exercises
    1. git clone

Solutions of the Exercises

The git repository has been updated and it contains now the solutions of the exercises, too. In addition, you can also take a look at the solutions without installing ipython notebook.

Just go to

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