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Profiling data science code with FIL

Episode #274, published Fri, Jul 24, 2020, recorded Wed, Jul 8, 2020.

This episode is carbon neutral.
Do you write data science code? Do you struggle loading large amounts of data or wonder what parts of your code use the maximum amount of memory? Maybe you just want to require smaller compute resources (servers, RAM, and so on).

If so, this episode is for you. We have Itamar Turner-Trauring, creator of the Python data science memory profiler FIL here to talk memory usage and data science.

Links from the show

Itamar on twitter: @itamarst
FIL: pythonspeed.com
Python Bytes coverage of FIL: pythonbytes.fm
Video: Small Big Data: using NumPy and Pandas when your data doesn't fit in memory: youtube.com
Software Engineering for Data Scientists Article: pythonspeed.com

Python Tutor: pythontutor.com
Weak references: docs.python.org

memory_profiler package: github.com
Austin profiler: github.com
WSL2 on Windows: pbpython.com/wsl-python.html

Want to go deeper? Check out our courses

Itamar Turner-Trauring
Itamar Turner-Trauring
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