logo
episode-header-image
Aug 2024
1h 8m

#474: Python Performance for Data Scienc...

MICHAEL KENNEDY
About this episode
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.

Episode sponsors

Posit
Talk Python Courses

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

--- Stay in touch with us ---
Subscribe to us on YouTube: youtube.com
Follow Talk Python on Mastodon: talkpython
Follow Michael on Mastodon: mkennedy
Up next
Aug 22
#517: Agentic Al Programming with Python
Agentic AI programming is what happens when coding assistants stop acting like autocomplete and start collaborating on real work. In this episode, we cut through the hype and incentives to define “agentic,” then get hands-on with how tools like Cursor, Claude Code, and LangChain ... Show More
1h 17m
Aug 19
#516: Accelerating Python Data Science at NVIDIA
Python’s data stack is getting a serious GPU turbo boost. In this episode, Ben Zaitlen from NVIDIA joins us to unpack RAPIDS, the open source toolkit that lets pandas, scikit-learn, Spark, Polars, and even NetworkX execute on GPUs. We trace the project’s origin and why NVIDIA bui ... Show More
1h 5m
Aug 11
#515: Durable Python Execution with Temporal
What if your code was crash-proof? That's the value prop for a framework called Temporal. Temporal is a durable execution platform that enables developers to build scalable applications without sacrificing productivity or reliability. The Temporal server executes units of applica ... Show More
1h 10m
Recommended Episodes
Aug 5
911: The Future of Python Notebooks is Here, with Marimo’s Dr. Akshay Agrawal
Reproducibility, Python notebooks, and data science communities: Software developer Akshay Agrawal speaks to Jon Krohn about Marimo, the next-generation computational notebook for Python, how he built and fostered a thriving community around the product, and what makes this noteb ... Show More
58m 20s
May 2023
675: Pandas for Data Analysis and Visualization
Wrangling data in Pandas, when to use Pandas, Matplotlib or Seaborn, and why you should learn to create Python packages: Jon Krohn speaks with guest Stefanie Molin, author of Hands-On Data Analysis with Pandas.This episode is brought to you by Posit, the open-source data science ... Show More
1h 8m
Mar 2025
NVIDIA RAPIDS and Open Source ML Acceleration with Chris Deotte and Jean-Francois Puget
NVIDIA RAPIDS is an open-source suite of GPU-accelerated data science and AI libraries. It leverages CUDA and significantly enhances the performance of core Python frameworks including Polars, pandas, scikit-learn and NetworkX. Chris Deotte is a Senior Data Scientist at NVIDIA an ... Show More
42m 6s
Jun 2024
SE Radio 622: Wolf Vollprecht on Python Tooling in Rust
Wolf Vollprecht, the CEO and founder of Prefix.dev, speaks with host Gregory M. Kapfhammer about how to implement Python tools, such as package managers, in the Rust programming language. They discuss the challenges associated with building Python infrastructure tooling in Python ... Show More
55m 10s
Jul 2019
Episode 67: Classic Computer Science Problems in Python
Today I am with David Kopec, author of Classic Computer Science Problems in Python, published by Manning Publications. His book deepens your knowledge of problem solving techniques from the realm of computer science by challenging you with interesting and realistic scenarios, exe ... Show More
28m 35s
Jul 2024
803: How to Thrive in Your (Data Science) Career, with Daliana Liu
Daliana Liu is a big name in data science teaching, and she has always been generous in sharing everything she knows about getting a job in data science. In this episode, she continues to extend her generosity, helping listeners define their approach to achieving a fulfilling car ... Show More
1h 54m
Sep 2024
819: PyTorch: From Zero to Hero, with Luka Anicin
SuperDataScience veteran and Udemy teacher Luka Anicin is on the podcast to talk about his brand-new course, “PyTorch: From Zero to Hero”, available exclusively on superdatascience.com. Host Jon Krohn asks Luka why he feels that every data scientist should consider PyTorch as the ... Show More
1h 6m
Dec 2024
849: 2025 AI and Data Science Predictions, with Sadie St. Lawrence
Sadie St Lawrence returns for her 4th annual prediction episode on the Super Data Science Podcast. Together with host Jon Krohn, they reflect on 2024’s most transformative trends—like agentic AI and enterprise AI monetization—and predict what's coming in 2025, from AI-driven scie ... Show More
1h 18m
Feb 2022
Modern Code Generation with Jordan Adler
Jordan Adler is Head of Developer Engineering at OneSignal and has a deep interest in code generation. He has helped migrate large systems from Python 2 or Python 3 using code generation and code transformation. Using tools like Yellicode, Python Future, and others, Jordan's team ... Show More
34m 49s
Jun 2020
Rust and machine learning #4: practical tools (Ep. 110)
In this episode I make a non exhaustive list of machine learning tools and frameworks, written in Rust. Not all of them are mature enough for production environments. I believe that community effort can change this very quickly. To make a comparison with the Python ecosystem I wi ... Show More
24m 18s