Python usually gets a bad rep because "it's slow".
While this might be generally true, there is nowadays
a plethora of tools and techniques that help with improving
python's speed.
concurrent.futures is a module of the standard
library that provides a high-level API for running
asynchronous code using threads or processes.
This tutorial makes a case for when it can come in handy for things such as data processing or web applications, while briefly exploring better alternatives for specific use cases.
All important files are in /notebooks/.
The slides are cfintro.ipynb and the visualization
notebook is visualization.ipynb (duh).
quick-dask.ipynb is a quick and not very well documented
demonstration of dask distributed on a local machine.
jupyter-nbconvert ./notebooks/cf-intro.ipynb --to slides --post serve