DiscoverTalk Python To Me#474: Python Performance for Data Science
#474: Python Performance for Data Science

#474: Python Performance for Data Science

Update: 2024-08-192
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Python performance has come a long way in recent times. And it's often the data scientists, with their computational algorithms and large quantities of data, who care the most about this form of performance. It's great to have Stan Seibert back on the show to talk about Python's performance for data scientists. We cover a wide range of tools and techniques that will be valuable for many Python developers and data scientists.



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Stan on Twitter: @seibert

Anaconda: anaconda.com

High Performance Python with Numba training: learning.anaconda.cloud

PEP 0703: peps.python.org

Python 3.13 gets a JIT: tonybaloney.github.io

Numba: numba.pydata.org

LanceDB: lancedb.com

Profiling tips: docs.python.org

Memray: github.com

Fil: a Python memory profiler for data scientists and scientists: pythonspeed.com

Rust: rust-lang.org

Granian Server: github.com

PIXIE at SciPy 2024: github.com

Free threading Progress: py-free-threading.github.io

Free Threading Compatibility: py-free-threading.github.io

caniuse.com: caniuse.com

SPy, presented at PyCon 2024: us.pycon.org

Watch this episode on YouTube: youtube.com

Episode transcripts: talkpython.fm



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#474: Python Performance for Data Science

#474: Python Performance for Data Science

Michael Kennedy (@mkennedy)