The title of this book is “Python for Scientists”, but what does that mean? The dictionary defines “Python” as either (a) a non-venomous snake from Asia or Saharan Africa or (b) a computer scripting language, and it is the second option which is intended here. (What exactly this second definition means will be explained later.) By “scientist”, I mean anyone who uses quantitative models either to obtain conclusions by processing pre-collected experimental data or to model potentially observable results from a more abstract theory, and who asks “what if?”. What if I analyse the data in a different way? What if I change the model? Thus the term also includes economists, engineers and mathematicians among others, as well as the usual concept of scientists. Given the volume of potential data or the complexity (non-linearity) of many theoretical models, the use of computers to answer these questions is fast becoming mandatory.

Advances in computer hardware mean that immense amounts of data or ever more complex models can be processed at increasingly rapid speeds. These advances also mean reduced costs so that today virtually every scientist has access to a “personal computer”, either a desktop work station or a laptop, and the distinction between the two is narrowing quickly. It might seem to be a given that suitable software will also be available so that the “what if” questions can be answered readily. However, this turns out not always to be the case. A quick pragmatic reason is that, while there is a huge market for hardware improvements, scientists form a very small fraction of it and so there is little financial incentive to improve scientific software. But for scientists, this issue is important and we need to examine it in more detail.

Scientific Software

Before we discuss what is available, it is important to note that all computer software comes in one of two types: proprietary and open-source. The first is supplied by a commercial firm. Such organizations have both to pay wages and taxes and to provide a return for their shareholders. Therefore, they have to charge real money for their products, and, in order to protect their assets from their competitors, they do not tell the customer how their software works. Thus, the end-users have little chance of being able to adapt or optimize the product for their own use.