Speaker
Jim Pivarski
(Princeton University)
Description
In recent years, Python has become a glue language for scientific computing. Although code written in Python is generally slow, it has a good connection with compiled C code and a common data abstraction through Numpy. Many data processing, statistical, and most machine learning software has a Python interface as a matter of course.
This tutorial will introduce you to core Python packages for science, such as Numpy, SciPy, Matplotlib, Pandas, and Numba, as well as HEP-specific tools like iminuit, particle, pyjet, uncertainties, and pyhf. We'll especially focus on accessing ROOT data in PyROOT and uproot.
