Monitor errors and performance issues with

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
Python Bytes coverage of FIL:
Video: Small Big Data: using NumPy and Pandas when your data doesn't fit in memory:
Software Engineering for Data Scientists Article:

Python Tutor:
Weak references:

memory_profiler package:
Austin profiler:
WSL2 on Windows:
Episode transcripts:

--- Stay in touch with us ---
Subscribe to us on YouTube:
Follow Talk Python on Twitter: @talkpython
Follow Michael on Twitter: @mkennedy

Want to go deeper? Check out our courses

Itamar Turner-Trauring
Itamar Turner-Trauring
Episode sponsored by
Ads served ethically
Become a friend of the show
Stay in the know and get a chance to win our contests.
See our privacy statement about email communications.