In data analysis, we often perform processing tasks which are computationally expensive. In these cases we will need multiprocessing tools that enable us to improve the performance. Multiprocessing in IPython is a big enough topic to have its own chapter. In this section, we only show how we can run a
map function into parallel processes with the
Pool object in Wakari.
Pool class is the easiest way to run a parallel process into a Wakari IPython Notebook. In this case, we will create a function that will be applied to each element on a numpy array by using the
map_async method, which is a variant of the
map method that delivers the result asynchronously.
You can find the multiprocessing module documentation at http://docs.python.org/2/library/multiprocessing.html.