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
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
Episode transcripts: talkpython.fm
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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
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to us on YouTube: youtube.com
Follow Talk Python on Mastodon: talkpython
Follow Michael on Mastodon: mkennedy