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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#359: Lifecycle of a machine learning project
Are you working on or considering a machine learning project? On this episode, we'll meet three people from the MLOps community: Demetrios Brinkmann, Kate Kuznecova, and Vishnu Rachakonda. They are here to tell us about the lifecycle of a machine learning project. We'll talk about getting started with prototypes and choosing frameworks, the development process, and finally moving into deployment and production. Links from the show Demetrios Brinkmann: @DPBrinkm Kate Kuznecova: linkedin.com Vishnu Rachakonda: linkedin.com MLOps Community: mlops.community Feature stores: mlops.community Great Expectations: github.com source control: DVC: dvc.org StreamLit: streamlit.io MLOps Jobs: mlops.pallet.com Made With ML Apps: madewithml.com Banana.dev: banana.dev FastAPI: fastapi.tiangolo.com MLOps without too much Ops: towardsdatascience.com NBDev: nbdev.fast.ai The "Works on My Machine" Certification Program: codinghorror.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 Sentry Error Monitoring, Code TALKPYTHON Stack Overflow Talk Python Training
#358: Understanding Pandas visually with PandasTutor
Pandas is a great library that allows you to accomplish a ton of filtering and processing in condensed syntax. But how well do you understand what's happening? Sam Lau and Philip Guo built a great site to help use visually explore how Pandas is processing your dataset with your specific syntax. It's called PandasTutor, and Sam is here to tell us about it. Links from the show Sam Lau: samlau.me PandasTutor: pandastutor.com PythonTutor: pythontutor.com Principles and Techniques of Data Science book: textbook.ds100.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 SignalWire Stack Overflow Talk Python Training
#357: Python and the James Webb Space Telescope
Telescopes have been fundamental in our understanding of our place in the universe. And when you think about images that have shaped our modern view of space, you probably think about Hubble. But just this year, the JWST or James Web Space Telescope, was launch. JWST will go far beyond what Hubble has discovered. And did you know Python is used extensively in the whole data pipeline of JWST? We have two great guests here to tell us about it: Megan Sosey and Mike Swam. Links from the show James Web Space Telescope: webbtelescope.org JWST at NASA: jwst.nasa.gov JWST's YouTube channel: youtube.com JWST Repo on GitHub: github.com/spacetelescope/jwst STSci's AstroConda: ssb.stsci.edu/astroconda Telescope pointing: github.com/spacetelescope/gwcs Simulator: github.com/spacetelescope/webbpsf STSci's Archive and Tools: archive.stsci.edu htcondor: datasci.danforthcenter.org/htcondor Silly faker: github.com/cube-drone/silly Nancy Grace Roman Space Telescope: roman.gsfc.nasa.gov Myst Parser: myst-parser.readthedocs.io Watch this episode on YouTube: youtube.com --- Stay in touch with us --- Subscribe on YouTube: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Datadog Stack Overflow Talk Python Training
#356: Tips for ML / AI startups
Have you been considering launching a product or even a business based on Python's AI / ML stack? We have a great guest on the episode this week, Dylan Fox, who is the cofounder of AssemblyAI and has been building his startup successfully over the past few years. He has interesting stories of 100s of GPUs in the cloud, evolving ML models, and much more that I know you'll enjoy hearing. Links from the show Dylan Twitter: @YouveGotFox AssemblyAI: assemblyai.com TensorFlow: tensorflow.org PyTorch: pytorch.org hugging face: huggingface.co SciKit-Learn: scikit-learn.org GeForce Card: nvidia.com pLS: twitter.com This journalist’s Otter.ai scare is a reminder that cloud transcription isn’t completely private: theverge.com Programming language trends: insights.stackoverflow.com Can My Water Cooled Raspberry Pi Cluster Beat My MacBook?: the-diy-life.com PyTorch vs TensorFlow in 2022: assemblyai.com/blog/pytorch-vs-tensorflow 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 Stack Overflow Sentry Error Monitoring, Code TALKPYTHON Talk Python Training
#355: EdgeDB - Building a database in Python
What database are you using in your apps these days? If you like most Python people, it's probably PostgreSQL. If you roll with NoSQL like me, you're probably using MongoDB. Maybe you're even using a graph database focused more on relationships. But there's a new Python database in town, and as you learn in during this episode, many critical Python libraries have come into existence because of it. This database is called EdgeDB. EdgeDB is built upon Postgres, implemented mostly in python, and is something of a marriage of a traditional relational database and an ORM. Python's async and await keywords, uvloop - the high performance asyncio event loop, and asyncpg all have ties back to the creation of EdgeDB. Yury Selivanov, the co-founder & CEO of EdgeDB, PSF fellow, and Python core developer is here to tell use about EdgeDB along with the history of many of these impactful language features and packages. Links from the show Yury Selivanov: @1st1 MagicPython: github.com/MagicStack/MagicPython uvloop: github.com/MagicStack/uvloop asyncpg: github.com/MagicStack/asyncpg TaskGroups and ExceptionGroups: twitter.com EdgeDB: edgedb.com Schema modeling: edgedb.com/showcase/data-modeling Easy EdgeDB book: edgedb.com/easy-edgedb Roadmap: edgedb.com/roadmap pgMustard: pgmustard.com PyBay: Building a Database with Python Talk: youtube.com Michael's course on async and await + Cython + uvloop: talkpython.fm/async Michael's PyBay talk: Flask + HTMX: youtube.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 Sentry Error Monitoring, Code TALKPYTHON SignalWire Talk Python Training
#354: Sphinx, MyST, and Python Docs in 2022
When you think about the power of Python, the clean language or powerful standard library may come to mind. You might certainly point to the external packages too. But what about the relative ease of picking up new libraries or even parts of the standard library? Documentation plays an important role there. And the tools in the Python space for building solid documentation and even publishing articles and books involving live code are huge assets. In this episode, we have Paul Everitt, Pradyun Gedam, Chris Holdgraf, and Chris Sewell to update us on Sphinx, MyST-Parser, ExecutableBooks, JupyerBook, Sphinx Themes, and much more. Links from the show Pradyun’s personal website: pradyunsg.me Chris’s personal website: predictablynoisy.com Paul Everitt: @paulweveritt Paul's free Sphinx and Markdown course: training.talkpython.fm Sphinx: sphinx-doc.org Python documentation: docs.python.org ExecutableBooks: executablebooks.org Jupyter Book: jupyterbook.org MyST parser: myst-parser.readthedocs.io Sphinx Book Theme: sphinx-book-theme.readthedocs.io PyData Sphinx Theme: pydata-sphinx-theme.readthedocs.io Sphinx Themes Gallery: sphinx-themes.org Furo Theme: pradyunsg.me sphinx-theme-builder: github.com Python Documentation WG issue tracker: github.com ReadTheDocs CZI: blog.readthedocs.com Pandoc: pandoc.org Google circa 1996: web.archive.org Python doc example: docs.python.org Tailwind doc example: tailwindcss.com/docs Typora app: typora.io CommonMark: commonmark.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 SignalWire Tonic Talk Python Training
#352: Running Python in Production
Do we talk about running Python in production enough? I can tell you that the Talk Python infrastructure (courses, podcasts, APIs, etc.) get a fair amount of traffic, but they look nothing like what Google, or Instagram, or insert [BIG TECH NAME] here's deployments do. Yet, mostly, we hear about interesting feats of engineering at massive scale that is impressive but often is also outside of the world most Python devs need for their companies and services. I have three great guests who do think we should talk more about small to medium-sized Python deployments: Emily Moorehouse, Hynek, and Glyph. I think you'll enjoy the conversation. They each bring their own interesting perspectives. Links from the show Emily on Twitter: @emilyemorehouse Hynek on Twitter: @hynek Glyph on Twitter: @glyph Main article by Hynek Python in Production Article: hynek.me Supporting articles Solid Snakes or: How to Take 5 Weeks of Vacation: hynek.me How to Write Deployment-friendly Applications: hynek.me Common Infrastructure Errors I've Made: matduggan.com Thoughts on Monoliths Give me back my monolith: craigkerstiens.com Goodbye Microservices: From 100s of problem children to 1 superstar: segment.com Configuring uWSGI for Production Deployment: techatbloomberg.com https://martinfowler.com/bliki/MicroservicePremium.html https://martinfowler.com/bliki/MonolithFirst.html More tools CuttleSoft: cuttlesoft.com pgMustard: Helps you review Postgres query plans quickly: pgmustard.com JSON:API: jsonapi.org Tenacity package: tenacity.readthedocs.io glom package: glom.readthedocs.io boltons package: boltons.readthedocs.io Joke: The Torture Never Stops: devops.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 SignalWire Tonic Talk Python Training
#351: Machine Learning Ethics and Laws Panel
The world of AI is changing fast. And the AI / ML space is a bit out of the ordinary for software developers. Typically in software, we can prove that given a certain situations, the code will always behave the same. We can point to where and why a decision is made. ML isn't like that. We set it up and then it takes on a life of its own. Regulators and governments are starting to step in and make rules over AI. The EU is one of the first to do so. That's why it's great to have Ines Montani and Katharine Jarmul, both awesome data scientists and EU residents, here to give us an overview of the coming regulations and other benefits and pitfalls of the AI / ML space. Links from the show Katharine Jarmul on Twitter: @kjam Katharine's site: kjamistan.com Ines Montani on Twitter: @_inesmontani Explosion AI: explosion.ai EU proposes new Artificial Intelligence Regulation: nortonrosefulbright.com The EU’s leaked AI regulation is ambitious but disappointingly vague: techmonitor.ai EU ARTIFICIAL INTELLIGENCE ACT: eur-lex.europa.eu/legal-content Facial Recognition Technology Ban Passed by King County Council: kingcounty.gov On the Opportunities and Risks of Foundation Models paper: arxiv.org thoughtworks: thoughtworks.com I don't care about cookies extension: chrome.google.com Everybody hates “FLoC,” Google’s tracking plan: arstechnica.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 Sentry Error Monitoring, Code TALKPYTHON SignalWire Talk Python Training
#350: Python Steering Council 2021 Retrospective
For 30 years, Python was overseen by Guide van Rossum since he created and released it around in 1990. When he retired in 2018 he left the creation of the new governing body up to the core developers. After a few stressful months, they concept of the steering council became the way forward. On this episode, I welcome the outgoing steering council to give us a look back and how this past year has gone. We welcome Barry Warsaw, Carol Willing, Brett Cannon, Pablo Galindo Salgado, and Thomas Wouters to the show. They are going to give us a rundown on of the important decisions for 2021. Links from the show Guests / Steering Council Members: Barry Warsaw: @pumpichank Carol Willing: @WillingCarol Brett Cannon: @brettsky Pablo Galindo Salgado: @pyblogsal Thomas Wouters: github.com/Yhg1s Python Steering Council: python.org Meet the Python Developer in Residence: Lukasz Langa episode: talkpython.fm/331 @PSF joke thread: twitter.com Do you even need loops #PythonShort video: 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 Research Affiliates SignalWire Talk Python Training
#349: Meet Beanie: A MongoDB ODM + Pydantic
This podcast episode you're listening to right now was delivered to you, in part, by MongoDB and Python powering our web apps and production processes. But if you're using pymongo, the native driver from MongoDB to talk to the server, you're doing it wrong. Basing your app on a foundation of exchanging raw dictionaries is a castle of sand. BTW, see the joke at the end of the show about this. You should be using an ODM. This time we're talking about Beanie which is one of the exciting, new MongoDB Object Document Mappers which is based on Pydantic and is async-native. Join me as I discuss this project with its creator: Roman Right. Links from the show Roman on Twitter: @roman_the_right Beanie ODM: github.com Tutorial: roman-right.github.io Beanie Relations, Cache, Actions and more!🎉🚀: dev.to/romanright Unsync: asherman.io ResponseModel: fastapi.tiangolo.com Weather Talk Python API docs: weather.talkpython.fm JetBrains fleet: jetbrains.com yarl: github.com Joke: twitter.com/stoltzmaniac Loading Document Objects to Beanie Dynamically: dev.pythonbynight.com Michael's YouTube videos Parsing data with Pydantic: youtube.com Counting the number of times items appear: youtube.com Do you even need loops in Python?: youtube.com Stream Deck + PyCharm - Enhancing Python Developer Productivity: youtube.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 Sentry Error Monitoring, Code TALKPYTHON Talk Python Training AssemblyAI
#353: SQLModel: The New ORM for FastAPI and Beyond
Two frameworks that have taken the Python world by storm are FastAPI and Pydantic. Once you already have your data exchange modeled in Pydantic, you might want to use that code for storing it in the database. And, if you have DB models you might want to somehow use them to power and document the APIs built with FastAPI. But the popular ORMs, such as SQLAlchemy and others, far predate Pydantic. But could they be put together? Sebastián Ramírez is here to tell us the answer is yes. We're covering his project SQLModel which is the marriage between Pydantic and SQLAlchemy. Links from the show Sebastián Ramírez: @tiangolo SQLModel: sqlmodel.tiangolo.com Create a SQLModel Model: sqlmodel.tiangolo.com Multiple Models: sqlmodel.tiangolo.com FastAPI Talk Python episode: talkpython.fm/284 FastAPI Dependency Injection: fastapi.tiangolo.com ODMantic for MongoDB: github.com Beanie for MongoDB: github.com Michael's Short video on Pydantic: youtube.com FastAPI courses by Michael: training.talkpython.fm/fastapi-courses 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 Datadog Tonic Talk Python Training
#348: Dear PyGui: Simple yet Fast Python GUI Apps
I'm always on the look out for a good Python UI framework. This episode focuses on Dear PyGui. Dear PyGui: A fast and powerful Graphical User Interface Toolkit for Python with minimal dependencies, created by Jonathan Hoffstadt and Preston Cothren. They are here to tell us all about it. Links from the show Jonathan Hoffstadt: @jhoffs1 Preston Cothren: @toulaboy3 Dear PyGUI source: github.com Video tutorials: dearpygui.readthedocs.io Getting started tutorial: dearpygui.readthedocs.io OpenFOAM: openfoam.org Vulkan: vulkan.org Michael's Python Shorts video series The playlist: talkpython.fm/python-shorts Michael's YouTube Channel: youtube.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 Sentry Error Monitoring, Code TALKPYTHON TopTal AssemblyAI Talk Python Training
#347: Cinder - Specialized Python that Flies
The team at Instagram dropped a performance bomb on the Python world when they open-sourced Cider, their performance oriented fork of CPython. It contains a number of performance optimizations, including bytecode inline caching, eager evaluation of coroutines, a method-at-a-time JIT, and an experimental bytecode compiler that uses type annotations to emit type-specialized bytecode that performs better in the JIT. While it's not a general purpose runtime we can all pick up and use, it contains many powerful features and optimizations that may make their way back to mainline Python. We welcome Dino Viehland to dive into Cinder. Links from the show Dino on Twitter: @DinoViehland Cinder Python Runtime: github.com/facebookincubator Dino's PyCon talk: youtube.com IronPython: ironpython.net Sam Gross's NoGil work: github.com/colesbury/nogil Pyjion: trypyjion.com uWSGI: uwsgi-docs.readthedocs.io Configuring uWSGI at Bloomberg: techatbloomberg.com Locust perf testing: locust.io 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 Sentry Error Monitoring, Code TALKPYTHON TopTal AssemblyAI Talk Python Training
#346: 20 Recommended Packages in Review
Do you enjoy the "final 2 questions" I always ask at the end of the show? I think it's a great way to track the currents of the Python community. This episode focuses in on one of those questions: "What notable PyPI package have you come across recently? Not necessarily the most popular one but something that delighted you and people should know about?" Our guest, Antonio Andrade put together a GitHub repository cataloging guests' response to this question over the past couple of years. So I invited him to come share the packages covered there. We touch on over 40 packages during this episode so I'm sure you'll learn a few new gems to incorporate into your workflow. Links from the show Antonio on Twitter: @AntonioAndrade Notable PyPI Package Repo: github.com/xandrade/talkpython.fm-notable-packages Antonio's recommended packages from this episode: Sumy: Extract summary from HTML pages or plain texts: github.com gTTS (Google Text-to-Speech): github.com Packages discussed during the episode 1. FastAPI - A-W-E-S-O-M-E web framework for building APIs: fastapi.tiangolo.com 2. Pythonic - Graphical automation tool: github.com 3. umap-learn - Uniform Manifold Approximation and Projection: readthedocs.io 4. Tortoise ORM - Easy async ORM for python, built with relations in mind: tortoise.github.io 5. Beanie - Asynchronous Python ODM for MongoDB: github.com 6. Hathi - SQL host scanner and dictionary attack tool: github.com 7. Plotext - Plots data directly on terminal: github.com 8. Dynaconf - Configuration Management for Python: dynaconf.com 9. Objexplore - Interactive Python Object Explorer: github.com 10. AWS Cloud Development Kit (AWS CDK): docs.aws.amazon.com 11. Luigi - Workflow mgmt + task scheduling + dependency resolution: github.com 12. Seaborn - Statistical Data Visualization: pydata.org 13. CuPy - NumPy & SciPy for GPU: cupy.dev 14. Stevedore - Manage dynamic plugins for Python applications: docs.openstack.org 15. Pydantic - Data validation and settings management: github.com 16. pipx - Install and Run Python Applications in Isolated Environments: pypa.github.io 17. openpyxl - A Python library to read/write Excel 2010 xlsx/xlsm files: readthedocs.io 18. HttpPy - More comfortable requests with python: github.com 19. rich - Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal: readthedocs.io 20. PyO3 - Using Python from Rust: pyo3.rs 21. fastai - Making neural nets uncool again: fast.ai 22. Numba - Accelerate Python Functions by compiling Python code using LLVM: numba.pydata.org 23. NetworkML - Device Functional Role ID via Machine Learning and Network Traffic Analysis: github.com 24. Flask-SQLAlchemy - Adds SQLAlchemy support to your Flask application: palletsprojects.com 25. AutoInvent - Libraries for generating GraphQL API and UI from data: autoinvent.dev 26. trio - A friendly Python library for async concurrency and I/O: readthedocs.io 27. Flake8-docstrings - Extension for flake8 which uses pydocstyle to check docstrings: github.com 28. Hotwire-django - Integrate Hotwire in your Django app: github.com 29. Starlette - The little ASGI library that shines: github.com 30. tenacity - Retry code until it succeeds: readthedocs.io 31. pySerial - Python Serial Port Extension: github.com 32. Click - Composable command line interface toolkit: palletsprojects.com 33. Pytest - Simple powerful testing with Python: docs.pytest.org 34. testcontainers-python - Test almost anything that can run in a Docker container: github.com 35. cibuildwheel - Build Python wheels on CI with minimal configuration: readthedocs.io 36. async-rediscache - An easy to use asynchronous Redis cache: github.com 37. seinfeld - Query a Seinfeld quote database: github.com 38. notebook - A web-based notebook environment for interactive computing: readthedocs.io 39. dagster - A data orchestrator for machine learning, analytics, and ETL: dagster.io 40. bleach - An easy safelist-based HTML-sanitizing tool: github.com 41. flynt - string formatting converter: github.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 Coiled TopTal AssemblyAI Talk Python Training
#345: 10 Tips and Tools for Developer Productivity
You know that feeling when one of your developer friends or colleague tells you about some amazing tool, library, or shell environment that you never heard of that you just have to run out and try right away? This episode is jam-packed full of those moments. We welcome back Jay Miller to discuss tools and tips for developer productivity. The title says 10 tips, but we actually veer into many more along the way. I think you'll really enjoy this useful and light-hearted episode. Links from the show Jay on Twitter: @kjaymiller More Oh my ZSH plugins: github.com exa: the.exa.website bat: github.com ripgrep/amber: github.com Neovim: neovim.io RUMPS macOS Framework: github.com Black: github.com pypi-changes package: readthedocs.io asdf-python: github.com WAVE Web Accessibility Evaluation Tool: wave.webaim.org Google PageSpeed: pagespeed.web.dev XKCD Commit messages: xkcd.com secure package: github.com OWASP Top 10: owasp.org ngrok: ngrok.com starship: starship.rs Homebrew: brew.sh Chocolatey: chocolatey.org pip-tools: github.com Let's Encrypt: letsencrypt.org Sourcetree Git App: sourcetreeapp.com Oh my ZSH: ohmyz.sh nerd fonts: nerdfonts.com Oh my Posh: ohmyposh.dev Windows Terminal: microsoft.com McFly shell history: github.com Fig IO enhanced shell: fig.io Conduit podcast: relay.fm htmx course at Talk Python: talkpython.fm/htmx 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 Coiled CockroachDB AssemblyAI Talk Python Training