PyCSP runs on single CPUs, multiprocessors and networked systems. We suggest using Python and PyCSP to structure scientific software through Communicating Sequential Processes (CSP). Three scientific applications are presented to demonstrate the features of PyCSP and how networks of processes may easily be mapped into a visual representation for better understanding of the process workflow. The use of standard multi-threading mechanisms such as locks, conditions and monitors are completely hidden in the PyCSP library. The three scientific applications; kNN, stochastic minimum search and McStas are shown to scale well on multi-processing, cluster computing and grid computing platforms using PyCSP.