Python is an industry-standard programming language for working with data on all levels of the data analytics pipeline, thanks to the rich ecosystem of libraries ranging from generic numerical libraries to special-purpose and/or domain-specific packages which are often supported by large developer communities and stable funding sources.

This online workshop is meant to give an overview of working with research data in Python using general libraries for storing, processing, analysing and sharing data. The focus is on improving performance. After covering tools for performant processing (netcdf, numpy, pandas, scipy) on single workstations the focus shifts to parallel, distributed and GPU computing (snakemake, numba, dask, multiprocessing, mpi4py).

Prerequisites

• Basic experience with Python
• Basic experience in working in a Linux-like terminal
• Some prior experience in working with large or small datasets