With the pandas library there is a powerful alternative to scientific programming languages such as R, Octave or Matlab. Originally designed for the analysis of financial data is has become a standard in terms of data handling and manipulation and is widely used not just in science but also the financial industry.
In this article we describe how pandas can be applied in everyday analyses where efficient data handling is required and how it can be integrated with other Python libraries such as numpy or matplotlib.
