Talk Python to Me is a weekly podcast hosted by developer and entrepreneur Michael Kennedy. We dive deep into the popular packages and software developers, data scientists, and incredible hobbyists doing amazing things with Python. If you're new to Python, you'll quickly learn the ins and outs of the community by hearing from the leaders. And if you've been Pythoning for years, you'll learn about your favorite packages and the hot new ones coming out of open source.
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#344: SQLAlchemy 2.0
SQLAlchemy is the most widely used ORM (Object Relational Mapper) for Python developers. It's been around since February 2006. But we might be in for the most significant release since the first one: SQLAlchemy 2.0. This version adds async and await support, new context-manager friendly features everywhere, and even a unified query syntax. Mike Bayer is back to give us a glimpse of what's coming and why Python's database story is getting stronger. Links from the show SQLAlchemy: sqlalchemy.org Mike on Twitter: @zzzeek Migrating to SQLAlchemy 2.0: sqlalchemy.org awesome-sqlalchemy: github.com sqlalchemy-continuum versioning: readthedocs.io enum support: github.com alembic: sqlalchemy.org GeoAlchemy: geoalchemy.org sqltap profiling: github.com nplusone: github.com Unit of work: duckduckgo.com ORM + Dataclasses: sqlalchemy.org SQLModel: sqlmodel.tiangolo.com Cython example: cython.org Async SQLAlchemy example: sqlalchemy.org ORM Usages Stats (see ORM section): jetbrains.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors TopTal Talk Python Training AssemblyAI
#343: Do Excel things, get notebook Python code with Mito
Here's a question: What's the most common way to explore data? Would you say pandas and matplotlib? Maybe you went more general and said Jupyter notebooks. How about Excel, or Google Sheets, or Numbers, or some other spreadsheet app? Yeah, my bet is on Excel. And while it has many drawbacks, it makes exploring tabular data very accessible to many people, most of whom aren't even developers or data scientists. On this episode, we're talking about a tool called Mito. This is an add-in for Jupyter notebooks that injects an Excel-like interface into the notebook. You pass it data via a pandas dataframe (or some other source) and then you can explore it as if you're using Excel. The cool thing is though, just below that, it's writing the pandas code you'd need to do to actually accomplish that outcome in code. I think this will make pandas and Python data exploration way more accessible to many more people. So if you've been intimidated by pandas, or know someone who has, this could be what you've been looking for. Links from the show Mito: trymito.io Mito summary stats: trymito.io pandas-profiling package: github.com Lux API: pypi.org Hex notebooks: medium.com Deepnote: deepnote.com Papermill: papermill.readthedocs.io JupterLite: jupyter.org Jupyter Desktop App: github.com Jut: github.com Jupyter project: jupyter.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Linode AssemblyAI Talk Python Training
#342: Python in Architecture (as in actual buildings)
At PyCon 2017, Jake Vanderplas gave a great keynote where he said, "Python is a mosaic." He described how Python is stronger and growing because it's being adopted and used by people with diverse technical backgrounds. In this episode, we're adding to that mosaic by diving into how Python is being used in the architecture, engineering, and construction industry. Our guest, Gui Talarico, has worked as an architect who help automate that world by bringing Python to solve problems others were just doing by point-and-click tooling. I think you'll enjoy this look into that world. We also touch on his project pyairtable near the end as well. Links from the show Pyninsula Python in Architecture Talk: youtube.com Using technology to scale building design processes at WeWork talk: youtube.com Revit software: autodesk.com Creating a command in pyRevit: notion.so IronPython: ironpython.net Python.NET: github.com revitpythonwrapper: readthedocs.io aec.works site: aec.works Speckle: speckle.systems Ladybug Tools: ladybug.tools Airtable: airtable.com PyAirtable: pyairtable.readthedocs.io PyAirtable ORM: pyairtable.readthedocs.io Revitron: github.com WeWork: wework.com Article: Using Airtable as a Content Backend: medium.com Python is a Mosaic Talk: youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Linode AssemblyAI Talk Python Training
#341: 25 Pandas Functions You Didn’t Know Existed
Do you do anything with Jupyter notebooks? If you do, there is a very good chance you're working with the pandas library. This is one of THE primary tools of anyone doing computational work or data exploration with Python. Yet, this library is massive and knowing the idiomatic way to use it can be hard to discover. That's why I've invited Bex Tuychiev to be our guest. He wrote an excellent article highlighting 25 idiomatic Pandas functions and properties we should all keep in our data toolkit. I'm sure there is something here for all of us to take away and use pandas that much better. Links from the show Bex Tuychiev: linkedin.com Bex's Medium profile: ibexorigin.medium.com Numpy 25 functions article: towardsdatascience.com missingno package: coderzcolumn.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Linode AssemblyAI Talk Python Training
#340: Time to JIT your Python with Pyjion?
Is Python slow? We touched on that question with Guido and Mark last episode. This time we welcome back friend of the show, Anthony Shaw. Here's there to share the massive amount of work he's been doing to answer that question and speed things up where they answer is yes. He's just released version 1.0 of the Pyjion project. Pyjion is a drop-in JIT compiler for Python 3.10. Pyjion uses the power of the .NET 6 cross-platform JIT compiler to optimize Python code on the fly, with NO changes to your source code required. It runs on Linux, macOS, and Windows, x64 and ARM64. Links from the show Anthony on Twitter: @anthonypjshaw Pyjion: github.com Restarting Pyjion Presentation: youtube.com Hathi: SQL host scanner and dictionary attack tool: github.com Try Pyjion online: trypyjion.com Pyjion optimizations: readthedocs.io Pyjion docs: readthedocs.io .NET: dotnet.microsoft.com PEP 523: python.org Pydantic validation decorator: helpmanual.io Tortoise ORM: github.com pypy: pypy.org Numba: numba.pydata.org NGen AOT Compiler: microsoft.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Linode AssemblyAI Talk Python Training
#339: Making Python Faster with Guido and Mark
There has a been a bunch of renewed interested in making Python faster. While for some of us, Python is already plenty fast. For others, such as those in data science, scientific computing, and even the large tech companies, making Python even a little faster would be a big deal. This episode is the first of several that dive into some of the active efforts to increase the speed of Python while maintaining compatibility with existing code and packages. Who better to help kick this off than Guido van Rossum and Mark Shannon? They both join us to share their project to make Python faster. I'm sure you'll love hearing what they are up to. Links from the show Guido van Rossum: @gvanrossum Mark Shannon: linkedin.com Faster Python Plan: github.com/faster-cpython The “Shannon Plan”: github.com/markshannon Sam Gross's nogil work: docs.google.com Watch this episode on YouTube: youtube.com ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Linode AssemblyAI Talk Python Training
#338: Using cibuildwheel to manage the scikit-HEP packages
How do you build and maintain a complex suite of Python packages? Of course, you want to put them on PyPI. The best format there is as a wheel. This means that when developers use your code, it comes straight down and requires no local tooling to install and use. But if you have compiled dependencies, such as C or FORTRAN, then you have a big challenge. How do you automatically compile and test against Linux, macOS (Intel and Apple Silicon), Windows, and so on? That's the problem cibuildwheel is solving. On this episode, you'll meet Henry Schreiner. He is developing tools for the next era of the Large Hadron Collider (LHC) and is an admin of Scikit-HEP. Of course, cibuildwheel is central to this process. Links from the show Henry on Twitter: @HenrySchreiner3 Henry's website: iscinumpy.gitlab.io Large Hadron Collider (LHC): home.cern cibuildwheel: github.com plumbum package: plumbum.readthedocs.io boost-histogram: github.com vector: github.com hepunits: github.com awkward arrays: github.com Numba: numba.pydata.org uproot4: github.com scikit-hep developer: scikit-hep.org pypa: pypa.io CLI11: github.com pybind11: github.com cling: root.cern Pint: pint.readthedocs.io Python Wheels site: pythonwheels.com Build package: pypa-build.readthedocs.io Mac Mini Colo: macminicolo.net scikit-build: github.com plotext: pypi.org Code Combat: codecombat.com clang format wheel: github.com cibuildwheel examples: cibuildwheel.readthedocs.io Cling in LLVM: root.cern New htmx course: talkpython.fm/htmx Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Talk Python Training AssemblyAI
#337: Kedro for Maintainable Data Science
Have you heard of Kedro? It's a Python framework for creating reproducible, maintainable and modular data science code. We all know that reproducibility and related topics are important ones in the data science space. The freedom to pop open a notebook and just start exploring is much of the magic. Yet, that free-form style can lead to difficulties in versioning, reproducibility, collaboration, and moving to production. Solving these problems is the goal of Kedro. And we have 3 great guests from the Kedro community here to give us the rundown: Yetunde Dada, Waylon Walker, and Ivan Danov. Links from the show Waylong on Twitter: @_WaylonWalker Yetunda on Twitter: @yetudada Ivan on Twitter: @ivandanov Kedro: kedro.readthedocs.io Kedro on GitHub: github.com Join the Kedro Discord: discord.gg Articles about Kedro by Waylan: waylonwalker.com Kedro spaceflights tutorial: kedro.readthedocs.io “Hello World” on Kedro: kedro.readthedocs.io Kedro Viz: quantumblacklabs.github.io Spaceflights Tutorial video: youtube.com Dynaconf package: dynaconf.com fsspec: Filesystem interfaces for Python: filesystem-spec.readthedocs.io Neovim: neovim.io Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Tabnine Talk Python Training AssemblyAI
#336: Terminal magic with Rich and Textual
Have you heard of the package Rich? This library allows you to create very, well, rich terminal-based UIs in Python. When you think of what you can typically build with basic print statements, that may seem quite limited. But with Rich, imagine justified tables, progress bars, rendering of markdown, and way more. This is one of the fastest growing projects in the Python space these days. And the creator, Will McGugan is here to give is the whole history and even a peak at the future of Rich and a follow on library called Textual. Links from the show Will on Twitter: @willmcgugan Rich: github.com Textual: github.com Pyfilesystem: pyfilesystem.org A Look At – and Inside – Textual Video: youtube.com ObjExplore: reposhub.com ghtop: ghtop.fast.ai Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Talk Python Training AssemblyAI
#335: Gene Editing with Python
Gene therapy holds the promise to permanently cure diseases that have been considered life-long challenges. But the complexity of rewriting DNA is truly huge and lives in its own special kind of big-data world. On this episode, you'll meet David Born, a computational biologist who uses Python to help automate genetics research and helps move that work to production. Links from the show David on Twitter: @Hypostulate Beam Therapeutics: beamtx.com AWS Cloud Development Kit: aws.amazon.com/cdk Jupyter: jupyter.org $1,279-per-hour, 30,000-core cluster built on Amazon EC2 cloud: arstechnica.com Luigi data pipelines: luigi.readthedocs.io AWS Batch: aws.amazon.com/batch What is CRISPR?: wikipedia.org SUMMIT supercomputer: olcf.ornl.gov/summit Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Talk Python Training AssemblyAI
#334: Microsoft Planetary Computer
On this episode, Rob Emanuele and Tom Augspurger join us to talk about building and running Microsoft's Planetary Computer project. This project is dedicated to providing the data around climate records and the compute necessary to process it with the mission of help use all understand climate change better. It combines multiple petabytes of data with a powerful hosted Jupyterlab notebook environment to process it. Links from the show Rob Emanuele on Twitter: @lossyrob Tom Augspurger on Twitter: @TomAugspurger Video at example walkthrough by Tom if you want to follow along: youtube.com?t=2360 Planetary computer: planetarycomputer.microsoft.com Applications in public: planetarycomputer.microsoft.com Microsoft's Environmental Commitments Carbon negative: blogs.microsoft.com Report: microsoft.com AI for Earth grants: microsoft.com Python SDK: github.com Planetary computer containers: github.com IPCC Climate Report: ipcc.ch Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Talk Python Training AssemblyAI
#333: State of Data Science in 2021
We know that Python and data science are growing in lock-step together. But exactly what's happening in the data science space in 2021? Stan Seibert from Anaconda is here to give us a report on what they found with their latest "State of Data Science in 2021" survey. Links from the show Stan on Twitter: @seibert State of data science survey results: know.anaconda.com A Python Data Scientist’s Guide to the Apple Silicon Transition: anaconda.com Numpy M1 Issue: github.com A Python Developer Explores Apple's M1 (Michael's video): youtube.com Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Masterworks.io AssemblyAI Talk Python Training
#332: Robust Python
Does it seem like your Python projects are getting bigger and bigger? Are you feeling the pain as your codebase expands and gets tougher to debug and maintain? Patrick Viafore is here to help us write more maintainable, longer-lived, and more enjoyable Python code. Links from the show Pat on Twitter: @PatViaforever Robust Python Book: oreilly.com Typing in Python: docs.python.org mypy: mypy-lang.org SQLModel: sqlmodel.tiangolo.com CUPID principles @ relevant time: overcast.fm Stevedore package: docs.openstack.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Masterworks.io AssemblyAI Talk Python Training
#331: Meet the Python Developer in Residence: Lukasz Langa
Python is a technology and community built upon the goodwill and volunteer time of 1,000s of contributors from the core devs inside CPython to the authors of 100,000s of external packages on PyPI. Until recently, the only full time folks have been at the PSF doing very important work but that work has been largely outside of CPython the technology. In July, 2021, the PSF created the Python Developer in Residence position. The first person in that role is Łukasz Langa and he's here to tell us how it's going and how it will benefit Python at large. Links from the show Łukasz Langa on twitter: @llanga Black: github.com/psf/black CPython PRs: github.com Weekly reports: lukasz.langa.pl Visionary Sponsors: python.org/psf/sponsorship/sponsors What do you get when you sponsor the PSF?: www.python.org/sponsors/application Brett Canon's PyCascades talk: youtube.com Django fellowship program: djangoproject.com Lukasz's prior episodes: Gradual Typing of Production Applications: talkpython.fm/151 Dive into CPython 3.8 and beyond: talkpython.fm/214 Python Language Summit 2018: talkpython.fm/179 Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Talk Python Training AssemblyAI
#330: Apache Airflow Open-Source Workflow with Python
If you are working with data pipelines, you definitely need to give Apache Airflow a look. This pure-Python workflow framework is one of the most popular and capable out there. You create your workflows by writing Python code using clever language operators and then you can monitor them and even debug them visually once they get started. Stop writing manual code or cron-job based code to create data pipelines check out Airflow. We're joined by three excellent guests from the Airflow community: Jarek Potiuk, Kaxil Naik, and Leah Cole. Links from the show Jarek Potiuk: linkedin.com Kaxil Naik: @kaxil Leah Cole: @leahecole Airflow site: airflow.apache.org Airflow on GitHub: github.com Airflow community: airflow.apache.org UI: github.com Helm Chart for Apache Airflow: airflow.apache.org Airflow Summit: airflowsummit.org Astronomer: astronomer.io Astronomer Registry (Easy to search for official and community Providers): registry.astronomer.io REST API: airflow.apache.org Contributing: github.com Airflow Loves Kubernetes talk: airflowsummit.org Watch this episode on YouTube: youtube.com Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams): youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Talk Python Training AssemblyAI