Talk Python To Me

Talk Python To Me

By Michael Kennedy (@mkennedy)

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.

Episodes

#455: Land Your First Data Job

Interested in data science but you're not quite working in it yet? In software, getting that very first job can truly be the hardest one to land. On this episode, we have Avery Smith from Data Career Jumpstart here to share his advice for getting your first data job. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Posit Talk Python Courses Links from the show Avery Smith: www.linkedin.com Data Career Jumpstart: www.datacareerjumpstart.com Data Nerd Site: datanerd.tech Write C# LINQ queries to query data: learn.microsoft.com A faster way to build and share data apps: streamlit.io Plotly Dash: dash.plotly.com Michael's Keynote: State of Python in 2024: youtube.com 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
04/04/2459m 33s

#454: Data Pipelines with Dagster

Do you have data that you pull from external sources or is generated and appears at your digital doorstep? I bet that data needs processed, filtered, transformed, distributed, and much more. One of the biggest tools to create these data pipelines with Python is Dagster. And we are fortunate to have Pedram Navid on the show this episode. Pedram is the Head of Data Engineering and DevRel at Dagster Labs. And we're talking data pipelines this week at Talk Python. Episode sponsors Talk Python Courses Posit Links from the show Rock Solid Python with Types Course: training.talkpython.fm Pedram on Twitter: twitter.com Pedram on LinkedIn: linkedin.com Ship data pipelines with extraordinary velocity: dagster.io dagster-open-platform: github.com The Dagster Master Plan: dagster.io data load tool (dlt): dlthub.com DataFrames for the new era: pola.rs Apache Arrow: arrow.apache.org DuckDB is a fast in-process analytical database: duckdb.org Ship trusted data products faster: www.getdbt.com 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
21/03/2458m 25s

#453: uv - The Next Evolution in Python Packages?

Have you ever been wait around for pip to do its thing while installing packages or syncing a virtual environment or through some higher level tool such as pip-tools? Then you'll be very excited to hear about the tool just announced from Astral called uv. It's like pip, but 100x faster. Charlie Marsh from Ruff fame and founder of Astral is here to dive in. Let's go. Episode sponsors Neo4j Talk Python Courses Links from the show Charlie Marsh on Twitter: @charliermarsh Charlie Marsh on Mastodon: @charliermarsh Astral: astral.sh uv: github.com Ruff: github.com Ruff Rules: docs.astral.sh When "Everything" Becomes Too Much: The npm Package Chaos of 2024: socket.dev Talk Python's free Audio AI Course: training.talkpython.fm 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
13/03/241h 14m

#452: Top Quart (async Flask) Extensions

Have you heard of Quart? It's the fully-async version of Flask created by Philip Jones who is working closely with the Flask team on these parallel projects. The TL;DR; version is that if you want to take advantage of async and await and you're using Flask, you want to give Quart a solid look. We've spoken to Philip previously about Quart. This time around here's here to share his top Quart extensions and libraries you can adopt today. Episode sponsors Posit Talk Python Courses Links from the show Pallets Team on ExTwitter: @PalletsTeam Quart Framework: quart.palletsprojects.com Using Quart Extensions: quart.palletsprojects.com Quart Tasks: quart-tasks.readthedocs.io Quart Minify: github.com Quart Db: github.com Hypercorn: github.com Quart-CORS: github.com Quart-Auth: github.com Quart-Rate: github.com Quart-Schma: github.com Flask-Socket: github.com Quart-SqlAlchemy: github.com Flask-Login: github.com greenback: github.com secure: github.com msgspec: jcristharif.com Server-Sent Events: pgjones.gitlab.io 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
10/03/2455m 42s

#451: Djangonauts, Ready for Blast-Off

Are you interested in contributing to Django? Then there is an amazing mentorship program that helps Python and Django enthusiasts, because contributes and potentially core developers of Django. It's called Djangonauts and their slogan is "where contributors launch." On this episode, we have Sarah Boyce from the Django team and former Djangonaut and now Djangonaut mentor, Tushar Gupta. Not only is this excellent for the Django community, many of other open source communities would do well to keep an eye on how this creative project is working. Episode sponsors Neo4j Posit Talk Python Courses Links from the show Sarah on Mastodon: @sarahboyce@mastodon.social Sarah on LinkedIn: linkedin.com Tushar on Twitter: @tushar5526 Djangonaut Space on Mastodon: @djangonaut@indieweb.social Djangonaut Space on Twitter: @djangonautspace Djangonaut Space on LinkedIn: linkedin.com Website: djangonaut.space Djangonaut Space Launch Video: youtube.com Sessions: djangonaut.space Djangonaut Space Interest Form: google.com/forms Program: github.com 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
01/03/2458m 33s

#450: Versioning Web APIs in Python

You've built an awesome set of APIs and you have a wide array of devices and clients using them. Then you need to upgrade an end point or change them in a meaningful way. Now what? That's the conversation I dive into over the next hour with Stanislav Zmiev. We're talking about Versioning APIs. Episode sponsors Neo4j Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Stanislav Zmiev: github.com Monite: monite.com Cadwyn: github.com Stripe API Versioning: stripe.com API Versioning NOtes: github.com FastAPI-Versioning: github.com Flask-Rebar: readthedocs.io Django Rest Framework Versioning: django-rest-framework.org pytest-fixture-classes: github.com 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
22/02/241h 2m

#449: Building UIs in Python with FastUI

Building web UIs in Python has always been in interesting proposition. On one end, we have a the full web design story with artisanal HTML and CSS. On another end there are several Python platforms that aim to the bring RAD, rapid app development, style of building with Python. Those can be great, and I've covered a couple of them, but they usually reach a limit on what they can do or how they integrate with the larger web ecosystem. On this episode, we have Samuel Colvin to share his latest exciting project FastUI. With FastUI, you build responsive web applications using React without writing a single line of JavaScript, or touching npm. Yet designers and other tools can focus on React front-ends for a professional SPA like app experience. Episode sponsors bright data Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Samuel on Mastodon: fosstodon.org Samuel on X: x.com FastUI: github.com FastUI Demos: fastui-demo.onrender.com FastAPI: fastapi.tiangolo.com Pydantic: pydantic.dev How Did REST Come To Mean The Opposite of REST Article: htmx.org Tailwind UI: tailwindui.com Dropbase: dropbase.io Anvil: anvil.works Flutter code example: github.com ReactJS code example: github.com 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
13/02/241h 6m

#448: Full-Time Open Source Devs Panel

So you've created a Python-based open source project and it's started to take off. You're getting contributors, lots of buzz in the podcast space, and more. But you have that day job working on Java. How do you make the transition from popular hobby project to full time job? After all, you are giving away your open source project for free, right? Well, on this episode, I have put together an amazing panel of guests who all have done exactly this: Turned their project into full time work and even companies in some cases. We have Samuel Colvin, Gina Häußge, Sebastián Ramírez, Charlie Marsh, Will McGugan and Eric Holscher on to share their stories. Episode sponsors Basedash Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Will McGugan: @willmcgugan Charlie Marsh: @charliermarsh@hachyderm Sebastián Ramírez: @tiangolo Samuel Colvin: @samuel_colvin Gina on Mastodon: chaos.social/@foosel Eric Holscher: @ericholscher Pydantic: pydantic.dev Astral (makes of Ruff): astral.sh Octoprint: octoprint.org Read the Docs: readthedocs.com FastAPI: fastapi.tiangolo.com Textual (makes of Rich): textualize.io 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
08/02/2458m 46s

#447: Parallel Python Apps with Sub Interpreters

It's an exciting time for the capabilities of Python. We have the Faster CPython initiative going strong, the recent async work, the adoption of typing and on this episode we discuss a new isolation and parallelization capability coming to Python through sub-interpreters. We have Eric Snow who spearheaded the work to get them added to Python 3.12 and is working on the Python API for 3.13 along with Anthony Shaw who has been pushing the boundaries of what you can already do with subinterpreters. Episode sponsors Pybites PDM Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Guests Anthony Shaw: @tonybaloney@fosstodon.org Eric Snow: @ericsnowcurrently@mastodon.social PEP 684 – A Per-Interpreter GIL: peps.python.org PEP 734 – Multiple Interpreters in the Stdlib: peps.python.org Running Python Parallel Applications with Sub Interpreters: fosstodon.org pytest subinterpreters: fosstodon.org Long-Term Vision for a Parallel Python Programming Model?: fosstodon.org Hypercorn Server: github.com msgspec: jcristharif.com Dill package: pypi.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
03/02/241h 11m

#446: Python in Excel

Why is Python so popular? There is plenty of room for debate on this but one solid reason is it's easy to adopt, easy to use, and caters to people who are not quite developers/data scientists but need to do some computing. Do you know where there largest untapped set of that group hang out? Excel. That's why it's super exciting that Python is now going to be built directly into Excel. Just go into a cell and type =PY and you're off writing full Python 3 code that is backed by a lite Anaconda distribution of Python. And we have Dr. Sarah Kaiser here to give us the rundown on Python in Excel. Episode sponsors Posit Pybites PDM Talk Python Courses Links from the show Sarah's website: sckaiser.com Sarah on Mastodon: @crazy4pi314@mathstodon.xyz Get started with Python in Excel: microsoft.com Python in SQL Server: microsoft.com 8 of the Biggest Excel Mistakes of All Time: blog.hurree.co Security and Python in Excel: microsoft.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
26/01/2448m 9s

#445: Inside Azure Data Centers with Mark Russinovich

When you run your code in the cloud, how much do you know about where it runs? I mean, the hardware it runs on and the data center it runs in? There are just a couple of hyper-scale cloud providers in the world. This episode is a very unique chance to get a deep look inside one of them: Microsoft Azure. Azure is comprised of over 200 physical data centers, each with 100,000s of servers. A look into how code runs on them is fascinating. Our guide for this journey will be Mark Russinovich. Mark is the CTO of Microsoft Azure and a Technical Fellow, Microsoft's senior-most technical position. He's also a bit of a programming hero of mine. Even if you don't host your code in the cloud, I think you'll enjoy this conversation. Let's dive in. Episode sponsors Posit Pybites PDM Talk Python Courses Links from the show Mark Russinovich: @markrussinovich Mark Russinovich on LinkedIn: linkedin.com SysInternals: learn.microsoft.com Zero Day: A Jeff Aiken Novel: amazon.com Inside Azure Datacenters: youtube.com What runs chatgpt?: youtube.com Azure Cobalt ARM chip: servethehome.com Closing talk by Mark at Ignite 2023: 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
19/01/2442m 25s

#444: The Young Coder's Blueprint to Success

Are you early in your software dev or data science career? Maybe it hasn't even really started yet and you're still in school. On this episode we have Sydney Runkle who has had a ton of success in the Python space and she hasn't even graduated yet. We sit down to talk about what she's done and might do differently again to achieve that success. It's "The Young Coder's Blueprint to Success" on episode 444 of Talk Python To Me. Episode sponsors Talk Python Courses Links from the show Sydney Runkle: linkedin.com Pydantic: pydantic.dev Code Combat: codecombat.com Humanitarian Toolbox: www.htbox.org PyCon 2024: pycon.org Good first issue example: github.com 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
02/01/2454m 59s

#443: Python Bytes Crossover 2023

Special crossover episode of Python Bytes to wrap up 2023. Topics include: Michael #1: Hatch v1.8 Brian #2: svcs : A Flexible Service Locator for Python Michael #3: Steering Council 2024 Term Election Results Brian #4: Python protocols. When to use them in your projects to abstract and decoupling ExtrasJoke: Joke: The dream is dead? Episode sponsors Posit Talk Python Courses
29/12/2335m 15s

#442: Ultra High Speed Message Parsing with msgspec

If you're a fan of Pydantic or dataclasses, you'll definitely be interested in this episode. We are talking about a super fast data modeling and validation framework called msgspec. Some of the types in here might even be better for general purpose use than Python's native classes. Join me and Jim Crist-Harif to talk about his data exchange framework, mspspec. Episode sponsors Posit Talk Python Courses Links from the show Jim Crist-Harif: jcristharif.com Jim @ GitHub: github.com Jim @ Mastdon: @jcristharif@hachyderm.io msgspec: github.com Projects using msgspec: github.com msgspec on Conda Forge: anaconda.org msgspec on PyPI: pypi.org Litestar web framework: litestar.dev Litestar episode: talkpython.fm Pydantic V2 episode: talkpython.fm JSON parsing with msgspec article: pythonspeed.com msgspec bencharmks: jcristharif.com msgspec vs. pydantic v1 and pydantic v2: github.com 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
14/12/231h

#441: Python = Syntactic Sugar?

You've probably heard the term "syntactic sugar", that is, syntax within a programming language that is designed to make things easier to read or to express. It makes the language "sweeter" for human use. It turns out Brett Cannon has spent 2 years diving into and writing about Python's sweet language features and how they really work down inside CPython. He joins me on the show today to dive into a few of the more relevant posts he's written about it. Episode sponsors Talk Python Courses Links from the show Brett Cannon: @brettcannon@fosstodon.org Syntactic sugar series: snarky.ca Syntactic sugar: wikipedia.org Unravelling attribute access in Python: snarky.ca Unravelling binary arithmetic operations: snarky.ca Unravelling the import statement: snarky.ca record-type: pypi.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
06/12/231h 7m

#440: Talking to Notebooks with Jupyter AI

We all know that LLMs and generative AI has been working its way into many products. It's Jupyter's turn to get a really awesome integration. We have David Qiu here to tell us about Jupyter AI. Jupyter AI provides a user-friendly and powerful way to apply generative AI to your notebooks. It lets you choose from many different LLM providers and models to get just the help you're looking for. And it does way more than just a chat pane in the UI. Listen to find out. Episode sponsors Posit Talk Python Courses Links from the show David Qiu: linkedin.com Jupyter AI: jupyter-ai.readthedocs.io Asking about something in your notebook: jupyter-ai.readthedocs.io Generating a new notebook: jupyter-ai.readthedocs.io Learning about local data: jupyter-ai.readthedocs.io Formatting the output: jupyter-ai.readthedocs.io Interpolating in prompts: jupyter-ai.readthedocs.io JupyterCon 2023 Talk: youtube.com PyData Seattle 2023 Talk: youtube.com 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
30/11/2358m 52s

#439: Pixi, A Fast Package Manager

On this episode we have Wolf Vollprecht and Ruben Arts from the pixi project here to talk about pixi, a high performance package manager for Python and other languages that actually manages Python itself too. They have a lot of interesting ideas on where Python packaging should go and are putting their time and effort behind them. Will pixi become your next package manager? Listen in to find out. Episode sponsors Posit Python Tutor Talk Python Courses Links from the show Guests Wolf Vollprecht: github.com/wolfv Ruben Arts: github.com/ruben-arts pixi: prefix.dev Prefix: prefix.dev Launching pixi: prefix.dev Conda: docs.conda.io Conda Forge: conda-forge.org NixOS: nixos.org Packaging Con 2023: packaging-con.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
22/11/2359m 4s

#438: Celebrating JupyterLab 4 and Jupyter 7 Releases

Jupyter Notebooks and Jupyter Lab have to be one of the most important parts of Python when it comes to bring new users to the Python ecosystem and certainly for the day to day work of data scientists and general scientists who have made some of the biggest discoveries of recent times. And that platform has recently gotten a major upgrade with JupyterLab 4 released and Jupyter Notebook being significantly reworked to be based on the changes from JupyterLab as well. We have an excellent panel of guests, Sylvain Corlay, Frederic Collonval, Jeremy Tuloup, and Afshin Darian here to tell us what's new in these and other parts of the Jupyter ecosystem. Episode sponsors Phylum Python Tutor Talk Python Courses Links from the show Guests Sylvain Corlay Frederic Collonval Jeremy Tuloup Afshin Darian JupyterLab 4.0 is Here: blog.jupyter.org Announcing Jupyter Notebook 7: blog.jupyter.org JupyterCon 2023 Videos: youtube.com Jupyterlite: github.com Download JupyterLab Desktop: github.com Mythical Man Month Book: wikipedia.org Blender in Jupyter: twitter.com 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
16/11/231h 4m

#437: HTMX for Django Developers (And All of Us)

Are you considering or struggling with replacing much of the interactivity of your Django app with frontend JavaScript frameworks? After all, your users do expect an interactive and modern app, right? Before you make a rash decision, you owe it to yourself to check out HTMX. It goes well with Django. We have Christopher Trudeau to run through a whole awesome list of HTMX and Python and tell us about his new HTMX + Django course. Episode sponsors IRL Podcast Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Chris on ExTwitter: @cltrudeau Django in Action book: manning.com Django: djangoproject.com HTMX + Django course: talkpython.fm HTMX: htmx.org awesome-htmx: github.com awesome-python-htmx: github.com django-js-lib-htmx: github.com htmxflask: github.com fastapi-sse-htmx: github.com django-htmx-patterns: github.com jinja2-fragments: github.com jinja_partials: github.com chameleon_partials: github.com django-render-block: github.com flask-htmx: github.com htmx-flask: github.com asgi-htmx: github.com hx-requests: github.com django-dashboards: github.com A Real World React -> htmx Port: htmx.org 3 IRL use cases for Python and HTMX: bitecode.dev owela-club: github.com 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
07/11/231h 4m

#436: An Unbiased Evaluation of Environment and Packaging Tools

How well do you know your Python packaging tools? These are things like pip which install your project's dependencies and their dependencies and so on. In this mix, we have more modern tools such as Poetry, Flit, Hatch and others. And even tools outside of Python itself which may attempt to manage Python itself in addition to the libraries. To make sense of all of this, we welcome back Anna-Lena Popkes for an unbiased evaluation of environment and packaging tools. Episode sponsors IRL Podcast Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Anna-Lena's website: alpopkes.com Anna-Lena on GitHub: github.com Accompanying Blog Post: alpopkes.com Talk from PyCon DE: youtube.com Talk from EuroPython: youtube.com Talk Python's Data Science Jumpstart with 10 Projects course: talkpython.fm Rye: github.com Poetry: python-poetry.org Material for MkDocs: squidfunk.github.io 100 Days of Python in a Magical Universe Episode: talkpython.fm pip-tools: pip-tools.readthedocs.io Hatch: hatch.pypa.io PDM: pdm.fming.dev Flit: flit.pypa.io Conda: docs.conda.io Pipenv: pipenv.pypa.io 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
01/11/2358m 32s

#435: PyPI Security

Do you worry about your developer / data science supply chain safety? All the packages for the Python ecosystem are much of what makes Python awesome. But the are also a bit of an open door to your code and machine. Luckily the PSF is taking this seriously and hired Mike Fiedler as the full time PyPI Safety & Security Engineer (not to be confused with the Security Developer in Residence staffed by Seth Michael Larson). Mike is here to give us the state of the PyPI security and plans for the future. Episode sponsors Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show Mike on Twitter: @mikefiedler Mike on Mastodon: @miketheman@hachyderm.io Supply Chain examples SolarWinds: csoonline.com XcodeGhost: wikipedia.org Google Ad Malware: medium.com PyPI: pypi.org OWASP Top 10: owasp.org Trusted Publishers: docs.pypi.org libraries.io: libraries.io GitHub Full 2FA: github.blog Mike's Latest Blog Post: blog.pypi.org pprintpp package: github.com ICDiff: github.com 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
25/10/231h 3m

#434: Building Mobile Apps Backed with Python

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19/10/2358m 49s

#433: Litestar: Effortlessly Build Performant APIs

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15/10/231h 4m

#432: Migrating to Pydantic 2.0: Beanie for MongoDB

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06/10/2353m 54s

#431: Visualizing CPython Release Process

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29/09/231h 2m

#430: Delightful Machine Learning Apps with Gradio

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19/09/2359m 43s

#429: Taming Flaky Tests

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11/09/231h 9m

#428: Django Trends in 2023

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29/08/231h 9m

#427: 10 Tips and Ideas for the Beginner to Expert Python Journey

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19/08/231h 6m

#426: What's New in PyScript [August 2023]

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09/08/231h 5m

#425: Memray: The endgame Python memory profiler

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04/08/231h 10m

#424: Shiny for Python

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27/07/231h 4m

#423: Solving 10 different simulation problems with Python

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24/07/231h 6m

#422: How data scientists use Python

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07/07/231h 2m

#421: Python at Netflix

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02/07/231h 4m

#420: Database Consistency & Isolation for Python Devs

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26/06/2356m 2s

#419: Debugging Python in Production with PyStack

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14/06/231h 12m

#418: How To Keep A Secret in Python Apps

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02/06/231h 7m

#417: Test-Driven Prompt Engineering for LLMs with Promptimize

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30/05/231h 13m

#416: Open Source Sports Analytics with PySport

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22/05/2357m 29s

#415: Future of Pydantic and FastAPI

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15/05/2350m 5s

#414: A Stroll Down Startup Lane

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07/05/2352m 47s

#413: Live from PyCon 2023

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26/04/2347m 20s

#412: PEP 711 - Distributing Python Binaries

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19/04/231h 18m

#411: Things I Wish Someone Had Explained To Me Sooner About Python

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14/04/231h 3m

#410: The Intersection of Tabular Data and Generative AI

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06/04/231h 5m

#409: Privacy as Code with Fides

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01/04/231h 8m

#408: Hatch: A Modern Python Workflow

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24/03/231h 2m

#407: pytest tips and tricks for better testing

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18/03/2356m 22s

#406: Reimagining Python's Packaging Workflows

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12/03/231h 6m

#405: Testing in Radio Astronomy with Python and pytest

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03/03/2359m 21s

#404: Clean Code in Python

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20/02/231h 4m

#403: Fusion Ignition Breakthrough and Python

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13/02/231h 4m

#402: Polars: A Lightning-fast DataFrame for Python [updated audio]

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08/02/2358m 48s

#401: Migrating 3.8 Million Lines of Python

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02/02/231h

#400: Ruff - The Fast, Rust-based Python Linter

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25/01/231h 3m

#399: Monorepos in Python

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18/01/231h 10m

#398: Imaging Black Holes with Python

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14/01/2358m 13s

#397: Evaluating New Open Source Tech Panel

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05/01/231h 3m

#396: AI Goes on Trial For Writing Code (crossover)

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30/12/2237m 32s

#395: Tools for README.md Creation and Maintenance

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22/12/221h 13m

#394: Awesome Jupyter Libraries and Extensions in 2022

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15/12/221h 2m

#393: Space Science with Python

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08/12/221h 3m

#392: Data Science from the Command Line

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02/12/221h 11m

#391: Pyscript powered by MicroPython

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29/11/221h 11m

#390: Mastodon for Python Devs

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15/11/221h 8m

#389: 18 awesome asyncio packages in Python

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09/11/2257m 28s

#388: Python 3.11 is here and it's fast

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02/11/221h 6m

#387: Build All the Things with Pants Build System

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27/10/221h 7m

#386: Realtime Web Apps and Dashboards with H2O Wave

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19/10/221h 5m

#385: Higher level Python asyncio with AnyIO

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15/10/2259m 55s

#384: Python Data Visualization - Where To Start?

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04/10/221h 13m

#383: Textinator and Building macOS Apps with Python

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28/09/221h 6m

#382: Apache Superset: Modern Data Exploration Platform

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22/09/221h 8m

#381: Python Perf: Specializing, Adaptive Interpreter

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16/09/221h 8m

#380: 7 lessons from building a modern TUI framework

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05/09/221h 7m

#379: 17 Libraries You Should Be Using in Django

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26/08/221h 20m

#378: Flet: Flutter apps in Python

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21/08/221h

#377: Python Packaging and PyPI in 2022

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13/08/221h 8m

#376: Pydantic v2 - The Plan

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04/08/221h 18m

#375: Python Language Summit 2022

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30/07/2258m 31s

#374: PSF Survey in Review

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20/07/221h 2m

#373: Reinventing Azure's Python CLI

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12/07/221h 6m

#372: Applied mathematics with Python

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08/07/221h 15m

#371: pipx - Installable, Isolated Python Applications

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30/06/2258m 43s

#370: OpenBB: Python's Open-source Investment Platform

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22/06/2254m 28s

#369: Getting Lazy with Python Imports and PEP 690

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16/06/2256m 26s

#368: End-to-End Web Testing with Playwright

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03/06/221h 13m

#367: Say Hello to PyScript (WebAssembly Python)

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25/05/221h 13m

#366: Optimizing PostgreSQL DB Queries with pgMustard

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20/05/221h 14m

#365: Solving Negative Engineering Problems with Prefect

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12/05/221h 4m

#364: Symbolic Math with Python using SymPy

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07/05/221h 7m

#363: Python for .NET and C# developers

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28/04/221h 6m

#362: Hypermodern Python Projects

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20/04/221h 6m

#361: Pangeo Data Ecosystem

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16/04/2254m 16s

#360: Removing Python's Dead Batteries (in just 5 years)

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08/04/221h 20m

#359: Lifecycle of a machine learning project

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03/04/221h 7m

#358: Understanding Pandas visually with PandasTutor

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25/03/2246m 48s

#357: Python and the James Webb Space Telescope

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21/03/221h 2m

#356: Tips for ML / AI startups

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14/03/221h 6m

#355: EdgeDB - Building a database in Python

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06/03/221h 18m

#354: Sphinx, MyST, and Python Docs in 2022

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24/02/221h 11m

#353: SQLModel: The New ORM for FastAPI and Beyond

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18/02/221h 18m

#352: Running Python in Production

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08/02/221h

#351: Machine Learning Ethics and Laws Panel

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03/02/221h 10m

#350: Python Steering Council 2021 Retrospective

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26/01/221h 10m

#349: Meet Beanie: A MongoDB ODM + Pydantic

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22/01/221h 20m

#348: Dear PyGui: Simple yet Fast Python GUI Apps

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17/01/221h 1m

#347: Cinder - Specialized Python that Flies

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08/01/221h 11m

#346: 20 Recommended Packages in Review

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21/12/211h 13m

#345: 10 Tips and Tools for Developer Productivity

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15/12/211h 16m

#344: SQLAlchemy 2.0

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09/12/211h 6m

#343: Do Excel things, get notebook Python code with Mito

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30/11/211h 6m

#342: Python in Architecture (as in actual buildings)

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23/11/211h 1m

#341: 25 Pandas Functions You Didn’t Know Existed

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17/11/2159m 16s

#340: Time to JIT your Python with Pyjion?

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10/11/211h 13m

#339: Making Python Faster with Guido and Mark

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04/11/211h 1m

#338: Using cibuildwheel to manage the scikit-HEP packages

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17/10/211h 17m

#337: Kedro for Maintainable Data Science

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09/10/211h 3m

#336: Terminal magic with Rich and Textual

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05/10/2159m 12s

#335: Gene Editing with Python

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24/09/2158m 20s

#334: Microsoft Planetary Computer

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18/09/2159m 46s

#333: State of Data Science in 2021

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10/09/211h 3m

#332: Robust Python

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31/08/211h 11m

#331: Meet the Python Developer in Residence: Lukasz Langa

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27/08/211h 6m

#330: Apache Airflow Open-Source Workflow with Python

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20/08/211h 7m

#329: Geekout: Renewable Energy

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13/08/2148m 18s

#328: Piccolo: A fast, async ORM for Python (updated)

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08/08/2158m 51s

#327: Little Automation Tools in Python

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30/07/211h 5m

#326: Building Desktop Apps with wxPython

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23/07/2153m 32s

#325: MicroPython + CircuitPython

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15/07/211h 8m

#324: Gatorade-powered Python APIs

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09/07/211h 12m

#323: Best practices for Docker in production

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03/07/211h 10m

#322: A path into data science

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25/06/2159m 56s

#321: HTMX - Clean, Dynamic HTML Pages

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19/06/211h 12m

#320: Python in the Electrical Energy Sector

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12/06/211h 8m

#319: Typosquatting and Supply Chains Vulnerabilities

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06/06/2159m 43s

#318: Measuring your ML impact with CodeCarbon

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28/05/211h 7m

#317: Python at the US Federal Election Commission

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21/05/2152m 39s

#316: Flask 2.0

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14/05/2157m 38s

#315: Awesome FastAPI extensions and add ons

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07/05/211h 7m

#314: Ask us about modern Python projects and tools

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30/04/211h 4m

#313: Automate your data exchange with PyDantic

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22/04/211h

#312: Python Apps that Scale to Billions of Users

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18/04/211h 17m

#311: Get inside the .git folder

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08/04/211h 12m

#310: AMA (Ask Me Anything) with Michael

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02/04/211h 2m

#309: What ML Can Teach Us About Life: 7 Lessons

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26/03/2155m 44s

#308: Docker for Python Developers (2021 Edition)

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20/03/211h 8m

#307: Python from 1994 to 2021, my how you've grown!

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11/03/211h 11m

#306: Scaling Python and Jupyter with ZeroMQ

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05/03/211h 5m

#305: Python community at Python Discord

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01/03/211h 1m

#304: asyncio all the things with Omnilib

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21/02/211h

#303: Python for Astronomy with Dr. Becky

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12/02/211h 3m

#302: The Data Engineering Landscape in 2021

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04/02/211h 4m

#301: Deploying and running Django web apps in 2021

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28/01/211h 8m

#300: Building a data science startup (panel)

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22/01/211h 6m

#299: Personal search engine with datasette and dogsheep

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17/01/211h 1m

#298: Building ML teams and finding ML jobs

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11/01/2156m 10s

#297: Python year in review (2020 edition)

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28/12/201h 10m

#296: Python in F1 racing

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23/12/201h 5m

#295: GIS + Python

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18/12/2057m 0s

#294: oso authorizes Python

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07/12/2051m 27s

#293: Learning how to learn as a developer

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01/12/201h

#292: Pythonic identity (auth in Python ecosystem)

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26/11/201h 5m

#291: Operational Resilience with Pyomo

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22/11/2059m 36s

#290: Side Hustles for Data Scientists

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13/11/201h 8m

#289: Discovering exoplanets with Python

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09/11/2047m 26s

#288: 10 tips to move from Excel to Python

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31/10/201h 3m

#287: Testing without dependencies, mocking in Python

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21/10/201h 3m

#286: Python and ML at NASA Jet Propulsion Laboratory (JPL)

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16/10/201h 7m

#285: Dask as a Platform Service with Coiled

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09/10/201h 11m

#284: Modern and fast APIs with FastAPI

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04/10/201h 6m

#283: Web scraping, the 2020 edition

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23/09/2048m 34s

#282: pre-commit framework

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17/09/2059m 33s

#281: Python in Car Racing

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09/09/201h

#280: Python and AI in Journalism

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05/09/2055m 51s

#279: Modern Python Developer's Toolkit

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29/08/201h 1m

#278: Teach kids Python with real programming and fun games at Code Combat

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21/08/201h 5m

#277: 10 tips every Django developer should know

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10/08/201h 11m

#276: Geekout: Life in the solar system and beyond

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06/08/201h 14m

#275: Beautiful Pythonic Refactorings

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01/08/2055m 4s

#274: Profiling data science code with FIL

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24/07/2058m 12s

#273: CoCalc: A fully colloborative notebook development environment

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18/07/2055m 6s

#272: No IoT things in hand? Simulate them with Device Simulator Express

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12/07/2051m 47s

#271: Unlock the mysteries of time, Python's datetime that is!

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04/07/201h 4m

#270: Python in supply chains: oil rigs, rockets, and lettuce

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25/06/2059m 20s

#269: HoloViz - a suite of tools for Python visualization

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19/06/2055m 57s

#268: Analyzing dozens of notebook environments

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13/06/2054m 24s

#267: 15 amazing pytest plugins

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06/06/2053m 34s

#266: Refactoring your code, like magic with Sourcery

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29/05/2057m 5s

#265: Why is Python slow?

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19/05/201h 3m

#264: 10 tips every Flask developer should know

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12/05/201h 8m

#263: SEO for developers

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06/05/201h 2m

#262: Build a career in data science

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01/05/201h 11m

#261: Monitoring and auditing machine learning

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25/04/201h

#260: From basic script to interactive data sci app with Streamlit

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18/04/2059m 25s

#259: From Academia to Tech Industry and Python

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09/04/201h

#258: Thriving in a remote developer environment

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04/04/201h 7m

#257: Exploring the galaxy with the fastest supercomputer, Python, and radio astronomy

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28/03/2052m 23s

#256: Click to run your notebook with Binder

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20/03/2057m 26s

#255: Talking to cars with Python

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14/03/2051m 51s

#254: A Python mentorship story

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06/03/201h 7m

#253: Moon base geekout

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25/02/201h 22m

#252: What scientific computing can learn from CS

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21/02/201h 10m

#251: Building and UX Testing Azure's Python SDK

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13/02/2052m 44s

#250: Capture over 400x C02 as trees with AI and Python

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08/02/201h 3m

#249: Capture the Staff of Pythonic Knowledge in TwilioQuest

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30/01/2059m 1s

#248: Climate change and your Python code

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24/01/201h 16m

#247: Solo maintainer of open-source in academia

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16/01/201h 5m

#246: Practices of the Python Pro

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09/01/201h 4m

#245: Python packaging landscape in 2020

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03/01/201h 1m

#244: Top 10 Real Python Articles of 2019

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27/12/1959m 49s

#243: Python on Windows is OK, actually

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17/12/1957m 38s

#242: Your education will be live-streamed

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11/12/1954m 23s

#241: Opal: Full stack health care apps

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07/12/1953m 23s

#240: A guided tour of the CPython source code

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27/11/191h

#239: Bayesian foundations

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23/11/1957m 18s

#237: A gut feeling about Python

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06/11/1949m 40s

#236: Scaling data science across Python and R

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29/10/191h

#235: Python in your Browser with Skulpt

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23/10/191h

#234: Awesome Python Applications

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15/10/191h 4m

#233: The Masonite Python Web Framework

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10/10/191h 8m

#232: Become a robot developer with Python

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04/10/191h 1m

#231: Advice for freelancing with Python

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25/09/191h 9m

#230: Python in digital humanities research

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18/09/1957m 42s

#229: Building advanced Pythonic interviews with docassemble

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12/09/191h

#228: Hunting bugs and tech startups with Python

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04/09/1959m 41s

#227: Maintainable data science: Tips for non-developers

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28/08/191h 10m

#226: Building Flask APIs for data scientists

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23/08/191h 9m

#225: Can subinterpreters free us from Python's GIL?

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12/08/191h 10m

#224: 12 lessons from 100 days of web

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05/08/191h 8m

#223: Fun and Easy 2D Games with Python

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30/07/191h 3m

#222: Interactive graphs with Bokeh and Python

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26/07/1959m 19s

#221: Empowering developers by embedding Python

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18/07/191h 7m

#220: Machine Learning in the cloud with Azure ML

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12/07/1954m 44s

#219: Take a Python tour of duty at the United States Digital Service

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05/07/191h 1m

#218: Serverless Python functions in Azure

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25/06/1947m 57s

#217: Notebooks vs data science-enabled scripts

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21/06/1954m 25s

#216: Digging into Visual Studio Code

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14/06/1953m 5s

#215: The software powering Talk Python courses and podcast

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06/06/191h 7m

#214: Dive into CPython 3.8 and beyond

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31/05/191h

#213: WebAssembly and CPython

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25/05/1949m 8s

#212: Python in Web Assembly with Pyodide

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17/05/1957m 21s

#211: Classic CS problems in Python

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11/05/191h 8m

#210: Making the most out of in-person training

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02/05/191h 7m

#209: Inside Python's new governance model

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28/04/191h 7m

#208: Packaging, Making the most of PyCon, and more

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21/04/191h 10m

#207: Parallelizing computation with Dask

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14/04/1957m 53s

#206: Running Django in Production

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06/04/1951m 25s

#205: Beginners and Experts Panel

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02/04/1957m 53s

#204: StaticFrame, like Pandas but safer

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21/03/191h 1m

#203: Beginners and Experts in Software Development

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13/03/1957m 35s

#202: Building a software business

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09/03/1959m 1s

#201: Choosing JupyterHub and Python over MATLAB

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27/02/191h 3m

#200: Escaping Excel Hell with Python and Pandas

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21/02/191h 6m

#199: Automate all the things with Python at Zapier

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14/02/191h 5m

#198: Catching up with the Anaconda distribution

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09/02/191h 5m

#197: Modern Python Standard Library Cookbook

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02/02/191h 1m

#196: Datalore: Hosted smart notebooks

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23/01/1949m 50s

#195: Teaching Python at Apple

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20/01/191h

#194: Learning (and teaching) Python in a vacuum

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11/01/191h 6m

#193: Data Science Year in Review 2018 Edition

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31/12/181h 20m

#192: Python Year in Review 2018 Edition

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26/12/1859m 3s

#191: Python's journey at Microsoft

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18/12/181h 12m

#190: Teaching Django

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11/12/181h 1m

#189: War Stories of the Developer Evangelists

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07/12/1859m 11s

#188: Async for the Pythonic web with Sanic

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01/12/1856m 22s

#187: Secure all the things with HubbleStack

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20/11/1859m 53s

#186: 100 Days of Python in a Magical Universe

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16/11/181h 2m

#185: Creating a Python 3 Culture at Facebook

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09/11/181h 7m

#184: Teaching Python with BBC micro:bit

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02/11/181h 7m

#183: Qt for Python

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24/10/1858m 3s

#182: Picture Python at Shutterfly

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17/10/1858m 15s

#181: 30 amazing Python projects

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12/10/1852m 10s

#180: What's new in Python 3.7 and beyond

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02/10/1857m 26s

#179: Python Language Summit 2018

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26/09/1850m 36s

#178: Coverage.py

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21/09/181h 3m

#177: Flask goes 1.0

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15/09/181h 2m

#176: The Python Community by the Numbers

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10/09/1853m 29s

#175: Teaching Python to network engineers

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31/08/1855m 27s

#174: Coming into Python from another Industry (part 2)

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16/08/1851m 6s

#173: Coming into Python from another Industry (part 1)

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07/08/1857m 25s

#172: Nuitka: A full Python compiler

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01/08/181h 6m

#171: 1M Jupyter notebooks analyzed

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29/07/1858m 32s

#170: Guido van Rossum steps down

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20/07/1837m 55s

#169: Becoming a Python content creator

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13/07/181h 6m

#168: 10 Python security holes and how to plug them

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06/07/181h

#167: Simplifying Python's Async with Trio

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29/06/1855m 41s

#166: Continuous delivery with Python

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14/06/181h 9m

#165: Python and the blockchain

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08/06/181h 5m

#164: Python in Brain Research at the Allen Institute

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01/06/1857m 54s

#163: Python in Geoscience

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25/05/1852m 17s

#162: Python in Building and Architecture

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21/05/1854m 35s

#161: Django 2.0

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12/05/181h 7m

#160: Lektor: Beautiful websites out of flat files

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04/05/1855m 41s

#159: Inside the new PyPI launch

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27/04/181h

#158: Quantum Computing and Python

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20/04/1847m 54s

#157: The Journal of Open Source Software

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06/04/181h 4m

#156: Python History and Perspectives

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24/03/1859m 7s

#155: Practical steps for moving to Python 3

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16/03/181h 3m

#154: Python in Biology and Genomics

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07/03/1858m 16s

#153: How Python Evolves

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01/03/181h 22m

#152: Understanding and using Python's AST

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23/02/1851m 55s

#151: Gradual Typing of Production Applications

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16/02/181h 9m

#150: Technical Lessons Learned from Pythonic Refactoring

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08/02/1859m 57s

#149: 4 Python Web Frameworks, Compared

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02/02/1857m 25s

#148: Python Book Authors' Panel Discussion

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28/01/181h 2m

#147: Quart: Flask, but 3x faster

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19/01/1851m 2s

#146: Building Alexa Skills with Python and Flask

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11/01/181h 1m

#145: 2017 Python Year in Review

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31/12/1759m 23s

#144: Machine Learning at the Large Hadron Collider

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26/12/1758m 22s

#143: Tuning Python Web App Performance

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20/12/171h 3m

#141: Python tricks

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07/12/171h 11m

#140: Level up your Python with #100DaysOfCode challenge

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29/11/1758m 18s

#139: Paths into a data science career

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22/11/171h 2m

#138: Anvil: All web, all Python

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15/11/171h

#137: Design concepts and tips for developers

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08/11/1758m 3s

#136: Secure code lessons from Have I Been Pwned

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02/11/171h

#135: Capturing human moments with AI and Python

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27/10/1754m 9s

#134: Python in Climate Science

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19/10/1752m 50s

#133: Productivity for developers

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11/10/171h 4m

#132: Contributing to open source

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04/10/171h 4m

#131: Top 10 machine learning libraries

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26/09/1754m 8s

#130: 10 books Python developers should be reading

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19/09/1752m 28s

#129: Falcon: The bare-metal Python web framework

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14/09/1759m 53s

#128: Pythonic Networks with NAPALM

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07/09/1756m 22s

#127: Shipping software to users

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31/08/171h 15m

#126: Kubernetes for Pythonistas

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22/08/1759m 39s

#125: Django REST framework and a new API star is born

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15/08/171h 7m

#124: Python for AI research

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07/08/1755m 20s

#123: Lessons from 100 straight dev job interviews

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31/07/1746m 23s

#122: Home Assistant: Pythonic Home Automation

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26/07/1758m 8s

#121: Microservices in Python

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19/07/171h 5m

#120: Python in Finance

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12/07/171h 7m

#119: Python in Engineering

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06/07/1752m 23s

#118: Serverless software

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27/06/1754m 50s

#117: Functional Python with Coconut

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21/06/171h 3m

#116: 10 top talks of PyCon 2017 reviewed

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12/06/171h

#115: Python for Humans projects

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08/06/1753m 45s

#114: Empowering developers at the Hidden Genius project

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30/05/1738m 2s

#113: Dedicated AI chips and running old Python faster at Intel

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27/05/1753m 2s

#112: Geeking out in the golden years

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18/05/171h 7m

#111: Pythonic Career Advice and More

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13/05/1757m 35s

#110: Data Democratization with Redash

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02/05/1756m 2s

#109: MongoDB Applied Design Patterns

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29/04/171h

#108: MicroPython and Open Source Hardware at Adafruit

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21/04/171h 4m

#107: Python concurrency with Curio

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15/04/171h 1m

#106: Invent your own computer games with Python

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07/04/1758m 12s

#105: A Pythonic Database Tour

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27/03/1757m 56s

#104: Game Theory in Python

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23/03/1757m 50s

#103: Compiling Python through PyLLVM and MongoDB for Data Scientists

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16/03/1752m 36s

#102: Effective Code Reviews

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08/03/1750m 52s

#101: Adding a full featured Python environment to Visual Studio Code

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03/03/1755m 11s

#100: Python past, present, and future with Guido van Rossum

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22/02/171h 2m

#99: Morepath: Super Powered Python Web Framework

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15/02/171h 4m

#98: Adding concurrency to Django with Django Channels

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08/02/171h 5m

#97: Flask, Django style with Flask-Diamond

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02/02/171h 5m

#96: Exploring Awesome Python

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25/01/1752m 20s

#95: Grumpy: Running Python on Go

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18/01/1752m 9s

#94: Guarenteed packages via Conda and Conda-Forge

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11/01/1746m 31s

#93: Spreading Python through the sciences with Software Carpentry

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03/01/171h 1m

#92: Bonus: Python Bytes Crossover: Python 3.6 is going to be awesome, Kite: your friendly co-developing AI

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29/12/1621m 0s

#91: Top 10 Data Science Stories of 2016

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27/12/161h 7m

#90: Data Wrangling with Python

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21/12/161h 1m

#89: A conversation with the Chief Data Scientist of the United States

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15/12/1652m 39s

#88: Lightweight Django

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06/12/1659m 58s

#87: PonyORM: The most Pythonic ORM yet?

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29/11/1656m 29s

#86: Python at StackOverflow

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24/11/161h 10m

#85: Parsing horrible things with Python

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17/11/1659m 46s

#84: Are we failing to fund Python's core infrastructure?

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08/11/161h 5m

#83: Python Videos on Demand at PyVideo

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02/11/1653m 26s

#82: Grokking Algorithms in Python

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27/10/1658m 45s

#81: Python and Machine Learning in Astronomy

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21/10/161h 2m

#80: TinyDB: A tiny document db written in Python

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16/10/1647m 1s

#79: Beeware Python Tools

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07/10/161h 22m

#78: How I built an entire game and toolchain 100% in Python

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28/09/1653m 26s

#77: 20 Python Libraries You Aren't Using (But Should)

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23/09/161h 16m

#76: Renewable Python

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15/09/1654m 10s

#75: Pythonic games at CheckIO

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09/09/1655m 27s

#74: Past, Present, and Future of IronPython

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02/09/1650m 48s

#73: Machine learning at the new Microsoft

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26/08/161h 4m

#72: Fashion-driven open source software at Zalando

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19/08/161h 1m

#71: Soft Skills: The software developer's life manual

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11/08/161h 5m

#70: Pythonic cover songs at Loudr

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05/08/161h 1m

#69: Write an Excellent Programming Blog

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26/07/1657m 41s

#68: Crossing the streams with Podcast.__init__

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20/07/161h 2m

#67: Property-based Testing with Hypothesis

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13/07/1658m 17s

#66: Faster Python Programs: Measure, Don't Guess

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07/07/161h 4m

#65: Jump on the real-time web with RethinkDB

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29/06/1659m 22s

#64: Inside the Python Package Index

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24/06/1659m 51s

#63: Validating Python tests with mutation testing

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16/06/1659m 36s

#62: San Diego Technology Immersion Group Learns Python

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07/06/161h 9m

#61: Free software, free people

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30/05/1654m 18s

#60: Scaling Python to 1000's of cores with Ufora

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24/05/161h 7m

#59: SageMath - Open source is ready to compete in the classroom

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18/05/1659m 24s

#58: Create better Python programs with concurrency, libraries, and patterns

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10/05/1654m 51s

#57: Python performance from the inside-out at Intel

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05/05/1656m 34s

#56: Data Science from Scratch

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27/04/1651m 3s

#55: How our engineering environments are killing diversity (and how we can fix it)

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21/04/161h 1m

#54: Enterprise Software with Python

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14/04/161h 7m
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