In this final segment, we highlight concepts important for new scientific Python programmers, especially those coming from a Matlab environment. We discuss effective use of Python dictionaries and the timeit profiling module. We also discuss the defaultdict object for quick and easy database creation...

In this segment, we show how the intensive least-the in computation can be accelerated using the parallel computing facilities of IPython we have considered so far.

In this segment, we discuss the push, pull, and push_function, features of IPython that allow finer control over individual namespaces of remote nodes and over the computations that run on them.

In this segment, we discuss the map feature to distribute computations across the available nodes, how to use targets to specify which nodes to use, and how to use blocking to avoid waiting for long computations to finish.

In this segment, we discuss cover importing modules for the remote nodes and how to distribute data (i.e. scatter, gather) to and from these nodes.

IPython provides excellent interactive parallel computing tools. In this segment, we discuss how to set up remote computational engines using ipcluster and how to use special magic commands in IPython to use these remote engines.

Here we show how to run the IPython interpreter on the Wind...