Monitor performance issues & errors in your code

Profiling data science code with FIL

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

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:

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