Talk Python To Me - Python Conversations For Passionate Developers

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!

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

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:

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

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-t

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: youtu

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 loo

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: ladybu

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: youtube.com Follow Talk Python on Twitter: @talkpython Follow Michael on Twitter: @mkennedy Sponsors Shortcut Linod

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 A

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 Episode transcripts: talkpython.fm ---------- Stay in touch with us ---------- Subscribe on YouTube (for live streams):

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: n

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: you

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 S

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 Shortcu

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

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 YouTube live stream edition: 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

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 YouTube live stream edition: 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

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

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 Love