Shownotes Transcript
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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Links from the show
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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