Developing in python is fast. Computation, however, can often be another story. Or at least that is how it may seem. When working with arrays and numerical datasets one can subvert many of python’s computational limitations by utilizing numpy. Numpy is python’s standard matrix computation library. Many python users only use numpy to store and generate arrays, failing to utilize one of python’s most powerful computational tools. By leveraging numpy’s ufuncs, aggregation, broadcasting and slicing/masking/indexing functionality one can cut back on slow python loops and increase the speed of their programs by as much as 100x. This talk aims at teaching attendees how to use these tools through toy examples.
|Talk Length||30 minutes|