In this last episode of this course, I talk to Itamar Turner-Trauring who created the website PythonSpeed and spent a considerable time on finding ways to make Python code faster and more efficient. Python and its ecosystem also have great tools how you can measure performance. Links: https://pythonspeed.com a set of articles and recommendations on how to improve your performance https://blog.sentry.io/python-performance-testing-a-comprehensive-guide/ a general blog post on performance testing https://uwpce-pythoncert.github.io/SystemDevelopment/profiling.html https://uwpce-pythoncert.github.io/SystemDevelopment/index.html https://en.wikipedia.org/wiki/Computer_performance https://python-102.readthedocs.io/en/latest/performance.html https://docs.python.org/3/tutorial/datastructures.html https://www.green-algorithms.org https://doi.org/10.1145/356635.356640 Donald Knuth's paper on over optimisation https://wiki.python.org/moin/TimeComplexity https://blog.jetbrains.com/dataspell/2023/08/polars-vs-pandas-what-s-the-difference/ comparing Polars with Panda Profiling tools https://pyinstrument.readthedocs.io/en/latest/ https://docs.python.org/3/library/profile.html https://docs.python.org/3/library/time.html the time function in Python https://docs.python.org/3/library/timeit.html another function to measure time in Python https://jiffyclub.github.io/snakeviz/ a graphic profile viewer https://bloomberg.github.io/memray/ flexible memory profiler https://github.com/benfred/py-spy https://www.wrighters.io/profiling-python-code-with-py-spy/ https://github.com/P403n1x87/austin-python The Python wrapper for the Austin profiler Don't be shy - say Hi This podcast is brought to you by the Advanced Research Computing Centre of the University College London, UK. Producer and Host: Peter Schmidt
