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المحتوى المقدم من Michael Kennedy. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Michael Kennedy أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.
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<div class="span index">1</div> <span><a class="" data-remote="true" data-type="html" href="/series/all-about-change">All About Change</a></span>


How do we build an inclusive world? Hear intimate and in-depth conversations with changemakers on disability rights, youth mental health advocacy, prison reform, grassroots activism, and more. First-hand stories about activism, change, and courage from people who are changing the world: from how a teen mom became the Planned Parenthood CEO, to NBA player Kevin Love on mental health in professional sports, to Beetlejuice actress Geena Davis on Hollywood’s role in women’s rights. All About Change is hosted by Jay Ruderman, whose life’s work is seeking social justice and inclusion for people with disabilities worldwide. Join Jay as he interviews iconic guests who have gone through adversity and harnessed their experiences to better the world. This show ultimately offers the message of hope that we need to keep going. All About Change is a production of the Ruderman Family Foundation. Listen and subscribe to All About Change wherever you get podcasts. https://allaboutchangepodcast.com/
#505: t-strings in Python (PEP 750)
Manage episode 482613994 series 2453836
المحتوى المقدم من Michael Kennedy. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Michael Kennedy أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.
Python has many string formatting styles which have been added to the language over the years. Early Python used the % operator to injected formatted values into strings. And we have string.format() which offers several powerful styles. Both were verbose and indirect, so f-strings were added in Python 3.6. But these f-strings lacked security features (think little bobby tables) and they manifested as fully-formed strings to runtime code. Today we talk about the next evolution of Python string formatting for advanced use-cases (SQL, HTML, DSLs, etc): t-strings. We have Paul Everitt, David Peck, and Jim Baker on the show to introduce this upcoming new language feature.
Episode sponsors
Posit
Auth0
Talk Python Courses
…
continue reading
Episode sponsors
Posit
Auth0
Talk Python Courses
Links from the show
Guests:
Paul on X: @paulweveritt
Paul on Mastodon: @pauleveritt@fosstodon.org
Dave Peck on Github: github.com
Jim Baker: github.com
PEP 750 – Template Strings: peps.python.org
tdom - Placeholder for future library on PyPI using PEP 750 t-strings: github.com
PEP 750: Tag Strings For Writing Domain-Specific Languages: discuss.python.org
How To Teach This: peps.python.org
PEP 501 – General purpose template literal strings: peps.python.org
Python's new t-strings: davepeck.org
PyFormat: Using % and .format() for great good!: pyformat.info
flynt: A tool to automatically convert old string literal formatting to f-strings: github.com
Examples of using t-strings as defined in PEP 750: github.com
htm.py issue: github.com
Exploits of a Mom: xkcd.com
pyparsing: github.com
Watch this episode on YouTube: youtube.com
Episode #505 deep-dive: talkpython.fm/505
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy
Paul on X: @paulweveritt
Paul on Mastodon: @pauleveritt@fosstodon.org
Dave Peck on Github: github.com
Jim Baker: github.com
PEP 750 – Template Strings: peps.python.org
tdom - Placeholder for future library on PyPI using PEP 750 t-strings: github.com
PEP 750: Tag Strings For Writing Domain-Specific Languages: discuss.python.org
How To Teach This: peps.python.org
PEP 501 – General purpose template literal strings: peps.python.org
Python's new t-strings: davepeck.org
PyFormat: Using % and .format() for great good!: pyformat.info
flynt: A tool to automatically convert old string literal formatting to f-strings: github.com
Examples of using t-strings as defined in PEP 750: github.com
htm.py issue: github.com
Exploits of a Mom: xkcd.com
pyparsing: github.com
Watch this episode on YouTube: youtube.com
Episode #505 deep-dive: talkpython.fm/505
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy
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Manage episode 482613994 series 2453836
المحتوى المقدم من Michael Kennedy. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Michael Kennedy أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.
Python has many string formatting styles which have been added to the language over the years. Early Python used the % operator to injected formatted values into strings. And we have string.format() which offers several powerful styles. Both were verbose and indirect, so f-strings were added in Python 3.6. But these f-strings lacked security features (think little bobby tables) and they manifested as fully-formed strings to runtime code. Today we talk about the next evolution of Python string formatting for advanced use-cases (SQL, HTML, DSLs, etc): t-strings. We have Paul Everitt, David Peck, and Jim Baker on the show to introduce this upcoming new language feature.
Episode sponsors
Posit
Auth0
Talk Python Courses
…
continue reading
Episode sponsors
Posit
Auth0
Talk Python Courses
Links from the show
Guests:
Paul on X: @paulweveritt
Paul on Mastodon: @pauleveritt@fosstodon.org
Dave Peck on Github: github.com
Jim Baker: github.com
PEP 750 – Template Strings: peps.python.org
tdom - Placeholder for future library on PyPI using PEP 750 t-strings: github.com
PEP 750: Tag Strings For Writing Domain-Specific Languages: discuss.python.org
How To Teach This: peps.python.org
PEP 501 – General purpose template literal strings: peps.python.org
Python's new t-strings: davepeck.org
PyFormat: Using % and .format() for great good!: pyformat.info
flynt: A tool to automatically convert old string literal formatting to f-strings: github.com
Examples of using t-strings as defined in PEP 750: github.com
htm.py issue: github.com
Exploits of a Mom: xkcd.com
pyparsing: github.com
Watch this episode on YouTube: youtube.com
Episode #505 deep-dive: talkpython.fm/505
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy
Paul on X: @paulweveritt
Paul on Mastodon: @pauleveritt@fosstodon.org
Dave Peck on Github: github.com
Jim Baker: github.com
PEP 750 – Template Strings: peps.python.org
tdom - Placeholder for future library on PyPI using PEP 750 t-strings: github.com
PEP 750: Tag Strings For Writing Domain-Specific Languages: discuss.python.org
How To Teach This: peps.python.org
PEP 501 – General purpose template literal strings: peps.python.org
Python's new t-strings: davepeck.org
PyFormat: Using % and .format() for great good!: pyformat.info
flynt: A tool to automatically convert old string literal formatting to f-strings: github.com
Examples of using t-strings as defined in PEP 750: github.com
htm.py issue: github.com
Exploits of a Mom: xkcd.com
pyparsing: github.com
Watch this episode on YouTube: youtube.com
Episode #505 deep-dive: talkpython.fm/505
Episode transcripts: talkpython.fm
--- Stay in touch with us ---
Subscribe to Talk Python on YouTube: youtube.com
Talk Python on Bluesky: @talkpython.fm at bsky.app
Talk Python on Mastodon: talkpython
Michael on Bluesky: @mkennedy.codes at bsky.app
Michael on Mastodon: mkennedy
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1 #512: Building a JIT Compiler for CPython 1:08:18
1:08:18
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Do you like to dive into the details and intricacies of how Python executes and how we can optimize it? Well, do I have an episode for you. We welcome back Brandt Bucher to give us an update on the upcoming JIT compiler for Python and why it differs from JITs for languages such as C# and Java. Episode sponsors Posit Talk Python Courses Links from the show Brandt Bucher : github.com/brandtbucher PyCon Talk: What they don't tell you about building a JIT compiler for CPython : youtube.com Specializing, Adaptive Interpreter Episode : talkpython.fm Watch this episode on YouTube : youtube.com Episode #512 deep-dive : talkpython.fm/512 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #511: From Notebooks to Production Data Science Systems 54:15
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If you're doing data science and have mostly spent your time doing exploratory or just local development, this could be the episode for you. We are joined by Catherine Nelson to discuss techniques and tools to move your data science game from local notebooks to full-on production workflows. Episode sponsors Agntcy Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show New Course: LLM Building Blocks for Python : training.talkpython.fm Catherine Nelson LinkedIn Profile : linkedin.com Catherine Nelson Bluesky Profile : bsky.app Enter to win the book : forms.google.com Going From Notebooks to Scalable Systems - PyCon US 2025 : us.pycon.org Going From Notebooks to Scalable Systems - Catherine Nelson – YouTube : youtube.com From Notebooks to Scalable Systems Code Repository : github.com Building Machine Learning Pipelines Book : oreilly.com Software Engineering for Data Scientists Book : oreilly.com Jupytext - Jupyter Notebooks as Markdown Documents : github.com Jupyter nbconvert - Notebook Conversion Tool : github.com Awesome MLOps - Curated List : github.com Watch this episode on YouTube : youtube.com Episode #511 deep-dive : talkpython.fm/511 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #510: 10 Polars Tools and Techniques To Level Up Your Data Science 1:02:04
1:02:04
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Are you using Polars for your data science work? Maybe you've been sticking with the tried-and-true Pandas? There are many benefits to Polars directly of course. But you might not be aware of all the excellent tools and libraries that make Polars even better. Examples include Patito which combines Pydantic and Polars for data validation and polars_encryption which adds AES encryption to selected columns. We have Christopher Trudeau back on Talk Python To Me to tell us about his list of excellent libraries to power up your Polars game and we also talk a bit about his new Polars course. Episode sponsors Agntcy Sentry Error Monitoring, Code TALKPYTHON Talk Python Courses Links from the show New Theme Song (Full-Length Download and backstory) : talkpython.fm/blog Polars for Power Users Course : training.talkpython.fm Awesome Polars : github.com Polars Visualization with Plotly : docs.pola.rs Dataframely : github.com Patito : github.com polars_iptools : github.com polars-fuzzy-match : github.com Nucleo Fuzzy Matcher : github.com polars-strsim : github.com polars_encryption : github.com polars-xdt : github.com polars_ols : github.com Least Mean Squares Filter in Signal Processing : www.geeksforgeeks.org polars-pairing : github.com Pairing Function : en.wikipedia.org polars_list_utils : github.com Harley Schema Helpers : tomburdge.github.io Marimo Reactive Notebooks Episode : talkpython.fm Marimo : marimo.io Ahoy Narwhals Podcast Episode Links : talkpython.fm Watch this episode on YouTube : youtube.com Episode #510 deep-dive : talkpython.fm/510 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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If you're looking to leverage the insane power of modern GPUs for data science and ML, you might think you'll need to use some low-level programming language such as C++. But the folks over at NVIDIA have been hard at work building Python SDKs which provide nearly native level of performance when doing Pythonic GPU programming. Bryce Adelstein Lelbach is here to tell us about programming your GPU in pure Python. Episode sponsors Posit Agntcy Talk Python Courses Links from the show Bryce Adelstein Lelbach on Twitter : @blelbach Episode Deep Dive write up : talkpython.fm/blog NVIDIA CUDA Python API : github.com Numba (JIT Compiler for Python) : numba.pydata.org Applied Data Science Podcast : adspthepodcast.com NVIDIA Accelerated Computing Hub : github.com NVIDIA CUDA Python Math API Documentation : docs.nvidia.com CUDA Cooperative Groups (CCCL) : nvidia.github.io Numba CUDA User Guide : nvidia.github.io CUDA Python Core API : nvidia.github.io Numba (JIT Compiler for Python) : numba.pydata.org NVIDIA’s First Desktop AI PC ($3,000) : arstechnica.com Google Colab : colab.research.google.com Compiler Explorer (“Godbolt”) : godbolt.org CuPy : github.com RAPIDS User Guide : docs.rapids.ai Watch this episode on YouTube : youtube.com Episode #509 deep-dive : talkpython.fm/509 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #508: Program Your Own Computer with Python 1:11:56
1:11:56
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If you've heard the phrase "Automate the boring things" for Python, this episode starts with that idea and takes it to another level. We have Glyph back on the podcast to talk about "Programming YOUR computer with Python." We dive into a bunch of tools and frameworks and especially spend some time on integrating with existing platform APIs (e.g. macOS's BrowserKit and Window's COM APIs) to build desktop apps in Python that make you happier and more productive. Let's dive in! Episode sponsors Posit Agntcy Talk Python Courses Links from the show Glyph on Mastodon : @glyph@mastodon.social Glyph on GitHub : github.com/glyph Glyph's Conference Talk: LceLUPdIzRs : youtube.com Notify Py : ms7m.github.io Rumps : github.com QuickMacHotkey : pypi.org QuickMacApp : pypi.org LM Studio : lmstudio.ai Coolify : coolify.io PyWin32 : pypi.org WinRT : pypi.org PyObjC : pypi.org PyObjC Documentation : pyobjc.readthedocs.io Watch this episode on YouTube : youtube.com Episode #508 deep-dive : talkpython.fm/508 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #507: Agentic AI Workflows with LangGraph 1:03:59
1:03:59
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If you want to leverage the power of LLMs in your Python apps, you would be wise to consider an agentic framework. Agentic empowers the LLMs to use tools and take further action based on what it has learned at that point. And frameworks provide all the necessary building blocks to weave these into your apps with features like long-term memory and durable resumability. I'm excited to have Sydney Runkle back on the podcast to dive into building Python apps with LangChain and LangGraph. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Sydney Runkle : linkedin.com LangGraph : github.com LangChain : langchain.com LangGraph Studio : github.com LangGraph (Web) : langchain.com LangGraph Tutorials Introduction : langchain-ai.github.io How to Think About Agent Frameworks : blog.langchain.dev Human in the Loop Concept : langchain-ai.github.io GPT-4 Prompting Guide : cookbook.openai.com Watch this episode on YouTube : youtube.com Episode #507 deep-dive : talkpython.fm/507 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #506: ty: Astral's New Type Checker (Formerly Red-Knot) 1:04:19
1:04:19
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The folks over at Astral have made some big-time impacts in the Python space with uv and ruff. They are back with another amazing project named ty. You may have known it as Red-Knot. But it's coming up on release time for the first version and with the release it comes with a new official name: ty. We have Charlie Marsh and Carl Meyer on the show to tell us all about this new project. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Talk Python's Rock Solid Python: Type Hints & Modern Tools (Pydantic, FastAPI, and More) Course : training.talkpython.fm Charlie Marsh on Twitter : @charliermarsh Charlie Marsh on Mastodon : @charliermarsh Carl Meyer : @carljm ty on Github : github.com/astral-sh/ty A Very Early Play with Astral’s Red Knot Static Type Checker : app.daily.dev Will Red Knot be a drop-in replacement for mypy or pyright? : github.com Hacker News Announcement : news.ycombinator.com Early Explorations of Astral’s Red Knot Type Checker : pydevtools.com Astral's Blog : astral.sh Rust Analyzer Salsa Docs : docs.rs Ruff Open Issues (label: red-knot) : github.com Ruff Types : types.ruff.rs Ruff Docs (Astral) : docs.astral.sh uv Repository : github.com Watch this episode on YouTube : youtube.com Episode #506 deep-dive : talkpython.fm/506 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #505: t-strings in Python (PEP 750) 1:11:59
1:11:59
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Python has many string formatting styles which have been added to the language over the years. Early Python used the % operator to injected formatted values into strings. And we have string.format() which offers several powerful styles. Both were verbose and indirect, so f-strings were added in Python 3.6. But these f-strings lacked security features (think little bobby tables) and they manifested as fully-formed strings to runtime code. Today we talk about the next evolution of Python string formatting for advanced use-cases (SQL, HTML, DSLs, etc): t-strings. We have Paul Everitt, David Peck, and Jim Baker on the show to introduce this upcoming new language feature. Episode sponsors Posit Auth0 Talk Python Courses Links from the show Guests: Paul on X : @paulweveritt Paul on Mastodon : @pauleveritt@fosstodon.org Dave Peck on Github : github.com Jim Baker : github.com PEP 750 – Template Strings : peps.python.org tdom - Placeholder for future library on PyPI using PEP 750 t-strings : github.com PEP 750: Tag Strings For Writing Domain-Specific Languages : discuss.python.org How To Teach This : peps.python.org PEP 501 – General purpose template literal strings : peps.python.org Python's new t-strings : davepeck.org PyFormat: Using % and .format() for great good! : pyformat.info flynt: A tool to automatically convert old string literal formatting to f-strings : github.com Examples of using t-strings as defined in PEP 750 : github.com htm.py issue : github.com Exploits of a Mom : xkcd.com pyparsing : github.com Watch this episode on YouTube : youtube.com Episode #505 deep-dive : talkpython.fm/505 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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What trends and technologies should you be paying attention to today? Are there hot new database servers you should check out? Or will that just be a flash in the pan? I love these forward looking episodes and this one is super fun. I've put together an amazing panel: Gina Häußge, Ines Montani, Richard Campbell, and Calvin Hendryx-Parker. We dive into the recent Stack Overflow Developer survey results as a sounding board for our thoughts on rising and falling trends in the Python and broader developer space. Episode sponsors NordLayer Auth0 Talk Python Courses Links from the show The Stack Overflow Survey Results : survey.stackoverflow.co/2024 Panelists Gina Häußge : chaos.social/@foosel Ines Montani : ines.io Richard Campbell : about.me/richard.campbell Calvin Hendryx-Parker : github.com/calvinhp Explosion : explosion.ai spaCy : spacy.io OctoPrint : octoprint.org .NET Rocks : dotnetrocks.com Six Feet Up : sixfeetup.com Stack Overflow : stackoverflow.com Python.org : python.org GitHub Copilot : github.com OpenAI ChatGPT : chat.openai.com Claude : anthropic.com LM Studio : lmstudio.ai Hetzner : hetzner.com Docker : docker.com Aider Chat : github.com Codename Goose AI : block.github.io/goose/ IndyPy : indypy.org OctoPrint Community Forum : community.octoprint.org spaCy GitHub : github.com Hugging Face : huggingface.co Watch this episode on YouTube : youtube.com Episode #504 deep-dive : talkpython.fm/504 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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Pandas is at a the core of virtually all data science done in Python, that is virtually all data science. Since it's beginning, Pandas has been based upon numpy. But changes are afoot to update those internals and you can now optionally use PyArrow. PyArrow comes with a ton of benefits including it's columnar format which makes answering analytical questions faster, support for a range of high performance file formats, inter-machine data streaming, faster file IO and more. Reuven Lerner is here to give us the low-down on the PyArrow revolution. Episode sponsors NordLayer Auth0 Talk Python Courses Links from the show Reuven : github.com/reuven Apache Arrow : github.com Parquet : parquet.apache.org Feather format : arrow.apache.org Python Workout Book (45% off with code talkpython45) : manning.com Pandas Workout Book (45% off with code talkpython45) : manning.com Pandas : pandas.pydata.org PyArrow CSV docs : arrow.apache.org Future string inference in Pandas : pandas.pydata.org Pandas NA/nullable dtypes : pandas.pydata.org Pandas `.iloc` indexing : pandas.pydata.org DuckDB : duckdb.org Pandas user guide : pandas.pydata.org Pandas GitHub issues : github.com Watch this episode on YouTube : youtube.com Episode #503 deep-dive : talkpython.fm/503 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #502: Django Ledger: Accounting with Python 1:03:38
1:03:38
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Do you or your company need accounting software? Well, there are plenty of SaaS products out there that you can give your data to. but maybe you also really like Django and would rather have a foundation to build your own accounting system exactly as you need for your company or your product. On this episode, we're diving into Django Ledger, created by Miguel Sanda, which can do just that. Episode sponsors Auth0 Talk Python Courses Links from the show Miguel Sanda on Twitter : @elarroba Miguel on Mastodon : @elarroba@fosstodon.org Miguel on GitHub : github.com Django Ledger on Github : github.com Django Ledger Discord : discord.gg Get Started with Django MongoDB Backend : mongodb.com Wagtail CMS : wagtail.org Watch this episode on YouTube : youtube.com Episode #502 deep-dive : talkpython.fm/502 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #501: Marimo - Reactive Notebooks for Python 1:00:35
1:00:35
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Have you ever spent an afternoon wrestling with a Jupyter notebook, hoping that you ran the cells in just the right order, only to realize your outputs were completely out of sync? Today's guest has a fresh take on solving that exact problem. Akshay Agrawal is here to introduce Marimo, a reactive Python notebook that ensures your code and outputs always stay in lockstep. And that's just the start! We'll also dig into Akshay's background at Google Brain and Stanford, what it's like to work on the cutting edge of AI, and how Marimo is uniting the best of data science exploration and real software engineering. Episode sponsors Worth Search Talk Python Courses Links from the show Akshay Agrawal : akshayagrawal.com YouTube : youtube.com Source : github.com Docs : marimo.io Marimo : marimo.io Discord : marimo.io WASM playground : marimo.new Experimental generate notebooks with AI : marimo.app Pluto.jl : plutojl.org Observable JS : observablehq.com Watch this episode on YouTube : youtube.com Episode #501 deep-dive : talkpython.fm/501 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #500: Django Simple Deploy and other DevOps Things 57:34
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We're sitting down with Eric Matthes, the educator, author, and developer behind Django Simple Deploy. If you've ever struggled with taking that final step of getting your Django app onto a live server (without spending days wrestling with DevOps complexities), then give Django Simple Deploy a look. Eric shares how Django Simple Deploy automates away the boilerplate parts of deployment, so you can focus on building features instead of deciphering endless configs. We'll talk about this new project's journey to 1.0, the range of hosting platforms it supports, and why it's not just for beginners. Episode sponsors Worth Search Talk Python Courses Links from the show django-simple-deploy documentation : readthedocs.io django-simple-deploy repository : github.com Python Crash Course book : ehmatthes.github.io Code Red : codered.cloud Docker : docker.com Caddy : caddyserver.com Bunny.net CDN : bunny.net Platform.sh : platform.sh fly.io : fly.io Heroku : heroku.com Watch this episode on YouTube : youtube.com Episode #500 deep-dive : talkpython.fm/500 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #499: BeeWare and the State of Python on Mobile 1:07:47
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This episode is all about Beeware, the project that working towards true native apps built on Python, especially for iOS and Android. Russell's been at this for more than a decade, and the progress is now hitting critical mass. We'll talk about the Toga GUI toolkit, building and shipping your apps with Briefcase, the newly official support for iOS and Android in CPython, and so much more. I can't wait to explore how BeeWare opens up the entire mobile ecosystem for Python developers, let's jump right in. Episode sponsors Posit Python in Production Talk Python Courses Links from the show Anaconda open source team : anaconda.com PEP 730 – Adding iOS : peps.python.org PEP 738 – Adding Android : peps.python.org Toga : beeware.org Briefcase : beeware.org emscripten : emscripten.org Russell Keith-Magee - Keynote - PyCon 2019 : youtube.com Watch this episode on YouTube : youtube.com Episode #499 deep-dive : talkpython.fm/499 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #498: Algorithms for high performance terminal apps 1:08:16
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In this episode, we welcome back Will McGugan, the creator of the wildly popular Rich library and founder of Textualize. We'll dive into Will's latest article on "Algorithms for High Performance Terminal Apps" and explore how he's quietly revolutionizing what's possible in the terminal, from smooth animations and dynamic widgets to full-on TUI (or should we say GUI?) frameworks. Whether you're looking to supercharge your command-line tools or just curious how Python can push the limits of text-based UIs, you'll love hearing how Will's taking a modern, web-inspired approach to old-school terminals. Episode sponsors Posit Python in Production Talk Python Courses Links from the show Algorithms for high performance terminal apps post : textual.textualize.io Textual Demo : github.com Textual : textualize.io Zero ver : 0ver.org memray : github.com Posting app : posting.sh Bulma CSS framewokr : bulma.io JP Term : davidbrochart.github.io Rich : github.com btop : github.com starship : starship.rs Watch this episode on YouTube : youtube.com Episode #498 deep-dive : talkpython.fm/498 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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Have you ever wondered why certain data points stand out so dramatically? They might hold the key to everything from fraud detection to groundbreaking discoveries. This week on Talk Python to Me, we dive into the world of outlier detection with Python with Brett Kennedy. You'll learn how outliers can signal errors, highlight novel insights, or even reveal hidden patterns lurking in the data you thought you understood. We'll explore fresh research developments, practical use cases, and how outlier detection compares to other core data science tasks like prediction and clustering. If you're ready to spot those game-changing anomalies in your own projects, stay tuned. Discount code for Outlier Detection in Python book : talkpython45 (45% off, no expiration date). Episode sponsors Posit Python in Production Talk Python Courses Links from the show Data-morph : github.com PyOD : github.com Prophet : github.com Outlier Detection in Python Book : manning.com Episode #497 deep-dive : talkpython.fm/497 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #496: Scaf: Complete blueprint for new Python Kubernetes projects 1:19:34
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Today we explore the wild world of Python deployment with my friend, Calvin Hendryx-Parker from Six Feet Up. We’ll tackle some of the biggest challenges in taking a Python app from “it works on my machine” to production, covering inconsistent environments, conflicting dependencies, and sneaky security pitfalls. Along the way, Calvin shares how containerization with Docker and Kubernetes can both simplify and complicate deployments, especially for smaller teams. Finally, we’ll introduce Scaf, a powerful project blueprint designed to give developers a rock-solid start on Python web projects of all sizes. Get notified when the Talk Python in Production book goes live and read the first third online right now. Episode sponsors Posit Python in Production Talk Python Courses Links from the show Calvin Hendryx-Parker : github.com Scaf on GitHub : github.com Scaf on GitHub (duplicate) : github.com "Deploy the Dream" song : deploy-the-dream-talk-python.mp3 CloudDevEngineering YouTube Channel : youtube.com TechWorld with Nana YouTube Channel : youtube.com Tilt (Kubernetes Dev Tool) : tilt.dev Talos (Minimal OS for Kubernetes) : talos.dev Traefik Reverse Proxy : traefik.io Sealed Secrets on GitHub : github.com Argo CD Documentation : readthedocs.io MailHog on GitHub : github.com Next.js : nextjs.org Cloud Custodian : cloudcustodian.io Valkey (Redis Replacement) : valkey.io “The ‘Works on My Machine’ Certification Program” (Coding Horror) : blog.codinghorror.com NVIDIA’s First Desktop AI PC (Ars Technica) : arstechnica.com Kind (Kubernetes in Docker) : kind.sigs.k8s.io Updated Effective PyCharm Course : training.talkpython.fm Talk Python in Production book : talkpython.fm/books/python-in-production Watch this episode on YouTube : youtube.com Episode #496 deep-dive : talkpython.fm/496 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #495: OSMnx: Python and OpenStreetMap 1:01:55
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On this episode, I'm joined by Dr. Geoff Boeing, an assistant professor at the University of Southern California whose research spans urban planning, spatial analysis, and data science. We explore why OpenStreetMap is such a powerful source of global map data—and how Geoff's Python library, OSMnx, makes that data easier to download, model, and visualize. Along the way, we talk about what shapes city streets around the world, how urban design influences everything from daily commutes to disaster resilience, and why turning open data into accessible tools can open up completely new ways of understanding our cities. If you've ever wondered how to build or analyze your own digital maps in Python, or what it takes to manage a project that transforms raw geographic data into meaningful research, you won't want to miss this conversation. Episode sponsors Posit Python in Production Talk Python Courses Links from the show City Street Orientations World : geoffboeing.com OSMnx Documentation : readthedocs.io OSMnx GitHub : github.com OpenStreetMap : openstreetmap.org Open Database License : opendatacommons.org ID Editor (Web Editor) : wiki.openstreetmap.org Planet OSM : planet.openstreetmap.org Overpass API : wiki.openstreetmap.org GeoPandas : geopandas.org NetworkX : networkx.org Shapely : shapely.readthedocs.io Watch this episode on YouTube : youtube.com Episode #495 deep-dive : talkpython.fm/495 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #494: Update on Flet: Python + Flutter UIs 1:00:23
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As Python developers, we're incredibly lucky to have over half a million packages that we can use to build our applications with over at PyPI. However, when it comes to choosing a UI framework, the options get narrowed down very quickly. Intersect those choices with the ones that work on mobile, and you have a very short list. Flutter is a UI framework for building desktop and mobile applications, and is in fact the one that we used to build the Talk Python courses app, you'd find at talkpython.fm/apps . That's why I'm so excited about Flet. Flet is a Python UI framework that is distributed and executed on the Flutter framework, making it possible to build mobile apps and desktop apps with Python. We have Feodor Fitsner back on the show after he launched his project a couple years ago to give us an update on how close they are to a full featured mobile app framework in Python. Episode sponsors Posit Python in Production Talk Python Courses Links from the show Flet : flet.dev Flet on Github : github.com Packaging apps with Flet : flet.dev/docs/publish Flutter : flutter.dev React vs. Flutter : trends.stackoverflow.co Kivy : kivy.org Beeware : beeware.org Mobile forge from Beeware : github.com The list of built-in binary wheels : flet.dev/docs/publish/android#binary-python-packages Difference between dynamic and static Flet web apps : flet.dev/docs/publish/web Integrating Flutter packages : flet.dev/docs/extend/integrating-existing-flutter-packages serious_python : pub.dev/packages/serious_python Watch this episode on YouTube : youtube.com Episode #494 deep-dive : talkpython.fm/494 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #493: Quarto: Open-source technical publishing 1:05:01
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In this episode, I'm joined by JJ Allaire, founder and executive chairman at Posit, and Carlos Scheidegger, a software engineer at Posit, to explore Quarto, an open-source tool revolutionizing technical publishing. We discuss how Quarto empowers users to seamlessly transform Jupyter notebooks into polished reports, dashboards, e-books, websites, and more. JJ shares his journey from creating RStudio to developing Quarto as a versatile, multi-language tool, while Carlos delves into its roots in reproducibility and the challenges of academic publishing. Don't miss this deep dive into a tool that's shaping the future of data-driven storytelling! Episode sponsors Talk Python Courses Podcast Later Links from the show JJ Allaire JJ on LinkedIn : linkedin.com JJ on GitHub : github.com Carlos Scheidegger Personal site : cscheid.net Mastodon : @scheidegger Fast AI : fast.ai nbdev : nbdev.fast.ai nbsanity - Share Notebooks as Polished Web Pages in Seconds : answer.ai Pandoc : pandoc.org Observable : github.com Quarto Pub : quartopub.com Deno : deno.com Real World Data Science site : realworlddatascience.net Typst : typst.app Github Actions for Quarto : github.com Watch this episode on YouTube : youtube.com Episode #493 deep-dive : talkpython.fm/493 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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Join me as I chat with Rich Iannone and Michael Chow from Posit where we explore the transformative power of data tables with the Great Tables library. We'll cover practical applications of Great Tables, showcasing how thoughtful design and advanced formatting can elevate your data presentations. And you'll learn about innovative features like nano plots and interactive elements and the importance of structure, format, and style in crafting tables that both inform and inspire. Whether you're a seasoned data scientist or just starting out, this episode is packed with valuable tips and inspiring examples to enhance your data storytelling. Episode sponsors Posit Python in Production Talk Python Courses Links from the show Michael Chow : github.com/machow Richard Iannone : github.com/rich-iannone Episode Deep Dives Writeup : talkpython.fm/blog Great Tables : github.com Making Beautiful, Publication Quality Tables PyCon talk : youtube.com Andrew Weatherman's Visualization Gallery : aweatherman.com Bureau of the Census Manual of Tabular Presentation : census.gov Table Contest : posit.co Watch this episode on YouTube : youtube.com Episode #492 deep-dive : talkpython.fm/492 Episode transcripts : talkpython.fm --- Stay in touch with us --- Subscribe to Talk Python on YouTube : youtube.com Talk Python on Bluesky : @talkpython.fm at bsky.app Talk Python on Mastodon : talkpython Michael on Bluesky : @mkennedy.codes at bsky.app Michael on Mastodon : mkennedy…
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1 #491: DuckDB and Python: Ducks and Snakes living together 1:02:03
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1 #489: Anaconda Toolbox for Excel and more with Peter Wang 1:09:09
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1 #487: Building Rust Extensions for Python 1:02:24
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1 #486: CSnakes: Embed Python code in .NET 1:02:45
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1 #485: Secure coding for Python with SheHacksPurple 1:09:28
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1 #484: From React to a Django+HTMX based stack 57:44
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1 #483: Reflex Framework: Frontend, Backend, Pure Python 1:03:05
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1 #482: Pre-commit Hooks for Python Devs 1:10:20
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1 #481: Python Opinions and Zeitgeist with Hynek 1:03:00
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1 #480: Ahoy, Narwhals are bridging the data science APIs 59:15
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1 #479: Designing Effective Load Tests for Your Python App 59:06
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1 #478: When and how to start coding with kids 54:25
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1 #477: Awesome Text Tricks with NLP and spaCy 1:03:47
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1 #476: Unified Python packaging with uv 1:12:59
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1 #475: Python Language Summit 2024 1:01:24
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1 #474: Python Performance for Data Science 1:08:23
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1 #473: Being a developer with ADHD 1:00:32
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1 #472: State of Flask and Pallets in 2024 1:01:33
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1 #471: Learning and teaching Pandas 1:04:14
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1 #470: Python in Medicine and Patient Care 1:19:04
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1 #469: PuePy: Reactive frontend framework in Python 58:46
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1 #468: Python Trends Episode 2024 1:05:37
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1 #467: Data Science Panel at PyCon 2024 34:40
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1 #465: The AI Revolution Won't Be Monopolized 1:08:46
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1 #464: Seeing code flows and generating tests with Kolo 54:59
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1 #463: Running on Rust: Granian Web Server 1:04:51
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1 #462: Pandas and Beyond with Wes McKinney 59:51
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1 #461: Python in Neuroscience and Academic Labs 1:03:37
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1 #460: Dropbase: Build Internal Tools with Python 52:29
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1 #457: Software Supply Chain Security with Phylum 1:08:21
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1 #456: Building GPT Actions with FastAPI and Pydantic 1:05:20
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See the full show notes for this episode on the website at talkpython.fm/455
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See the full show notes for this episode on the website at talkpython.fm/454
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1 #453: uv - The Next Evolution in Python Packages? 1:14:21
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1 #452: Top Quart (async Flask) Extensions 55:42
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1 #451: Djangonauts, Ready for Blast-Off 58:33
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1 #450: Versioning Web APIs in Python 1:02:37
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1 #449: Building UIs in Python with FastUI 1:06:16
1:06:16
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1 #448: Full-Time Open Source Devs Panel 58:46
58:46
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1 #447: Parallel Python Apps with Sub Interpreters 1:11:09
1:11:09
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1 #445: Inside Azure Data Centers with Mark Russinovich 42:25
42:25
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1 #444: The Young Coder's Blueprint to Success 54:59
54:59
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1 #442: Ultra High Speed Message Parsing with msgspec 1:00:01
1:00:01
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1 #440: Talking to Notebooks with Jupyter AI 58:52
58:52
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1 #438: Celebrating JupyterLab 4 and Jupyter 7 Releases 1:04:55
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1 #437: HTMX for Django Developers (And All of Us) 1:04:50
1:04:50
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1 #436: An Unbiased Evaluation of Environment and Packaging Tools 58:32
58:32
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1 #434: Building Mobile Apps Backed with Python 58:49
58:49
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1 #433: Litestar: Effortlessly Build Performant APIs 1:04:46
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1 #432: Migrating to Pydantic 2.0: Beanie for MongoDB 53:54
53:54
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1 #431: Visualizing CPython Release Process 1:02:12
1:02:12
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1 #430: Delightful Machine Learning Apps with Gradio 59:43
59:43
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1 #427: 10 Tips and Ideas for the Beginner to Expert Python Journey 1:06:06
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1 #426: What's New in PyScript [August 2023] 1:05:00
1:05:00
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1 #425: Memray: The endgame Python memory profiler 1:10:28
1:10:28
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1 #423: Solving 10 different simulation problems with Python 1:06:32
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1 #422: How data scientists use Python 1:02:16
1:02:16
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1 #420: Database Consistency & Isolation for Python Devs 56:02
56:02
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1 #419: Debugging Python in Production with PyStack 1:12:23
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1 #418: How To Keep A Secret in Python Apps 1:07:11
1:07:11
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1 #417: Test-Driven Prompt Engineering for LLMs with Promptimize 1:13:41
1:13:41
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1 #416: Open Source Sports Analytics with PySport 57:29
57:29
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1 #412: PEP 711 - Distributing Python Binaries 1:18:20
1:18:20
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1 #411: Things I Wish Someone Had Explained To Me Sooner About Python 1:03:45
1:03:45
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1 #410: The Intersection of Tabular Data and Generative AI 1:05:38
1:05:38
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1 #409: Privacy as Code with Fides 1:08:01
1:08:01
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1 #408: Hatch: A Modern Python Workflow 1:02:59
1:02:59
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1 #407: pytest tips and tricks for better testing 56:22
56:22
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1 #406: Reimagining Python's Packaging Workflows 1:06:41
1:06:41
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1 #405: Testing in Radio Astronomy with Python and pytest 59:21
59:21
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1 #403: Fusion Ignition Breakthrough and Python 1:04:42
1:04:42
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1 #402: Polars: A Lightning-fast DataFrame for Python [updated audio] 58:48
58:48
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1 #401: Migrating 3.8 Million Lines of Python 1:00:57
1:00:57
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1 #400: Ruff - The Fast, Rust-based Python Linter 1:03:45
1:03:45
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See the full show notes for this episode on the website at talkpython.fm/398
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1 #397: Evaluating New Open Source Tech Panel 1:03:25
1:03:25
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1 #396: AI Goes on Trial For Writing Code (crossover) 37:32
37:32
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1 #395: Tools for README.md Creation and Maintenance 1:13:31
1:13:31
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1 #394: Awesome Jupyter Libraries and Extensions in 2022 1:02:22
1:02:22
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See the full show notes for this episode on the website at talkpython.fm/393
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1 #392: Data Science from the Command Line 1:11:04
1:11:04
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1 #391: Pyscript powered by MicroPython 1:11:04
1:11:04
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1 #389: 18 awesome asyncio packages in Python 57:28
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1 #388: Python 3.11 is here and it's fast 1:06:29
1:06:29
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1 #387: Build All the Things with Pants Build System 1:07:50
1:07:50
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1 #386: Realtime Web Apps and Dashboards with H2O Wave 1:05:37
1:05:37
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1 #385: Higher level Python asyncio with AnyIO 59:55
59:55
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1 #384: Python Data Visualization - Where To Start? 1:13:16
1:13:16
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1 #383: Textinator and Building macOS Apps with Python 1:06:08
1:06:08
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1 #382: Apache Superset: Modern Data Exploration Platform 1:08:16
1:08:16
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1 #381: Python Perf: Specializing, Adaptive Interpreter 1:08:53
1:08:53
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1 #380: 7 lessons from building a modern TUI framework 1:07:49
1:07:49
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1 #379: 17 Libraries You Should Be Using in Django 1:20:32
1:20:32
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1 #378: Flet: Flutter apps in Python 1:00:21
1:00:21
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1 #377: Python Packaging and PyPI in 2022 1:08:11
1:08:11
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1 #373: Reinventing Azure's Python CLI 1:06:30
1:06:30
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1 #372: Applied mathematics with Python 1:15:44
1:15:44
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1 #371: pipx - Installable, Isolated Python Applications 58:43
58:43
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1 #370: OpenBB: Python's Open-source Investment Platform 54:28
54:28
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1 #369: Getting Lazy with Python Imports and PEP 690 56:26
56:26
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1 #368: End-to-End Web Testing with Playwright 1:13:19
1:13:19
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1 #367: Say Hello to PyScript (WebAssembly Python) 1:13:41
1:13:41
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1 #366: Optimizing PostgreSQL DB Queries with pgMustard 1:14:06
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1 #365: Solving Negative Engineering Problems with Prefect 1:04:10
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1 #364: Symbolic Math with Python using SymPy 1:07:52
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1 #363: Python for .NET and C# developers 1:06:36
1:06:36
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1 #362: Hypermodern Python Projects 1:06:14
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1 #360: Removing Python's Dead Batteries (in just 5 years) 1:20:03
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1 #359: Lifecycle of a machine learning project 1:07:29
1:07:29
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1 #358: Understanding Pandas visually with PandasTutor 46:48
46:48
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1 #357: Python and the James Webb Space Telescope 1:02:30
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1 #355: EdgeDB - Building a database in Python 1:18:06
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1 #354: Sphinx, MyST, and Python Docs in 2022 1:11:55
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1 #353: SQLModel: The New ORM for FastAPI and Beyond 1:18:21
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1 #352: Running Python in Production 1:00:12
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1 #351: Machine Learning Ethics and Laws Panel 1:10:28
1:10:28
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1 #350: Python Steering Council 2021 Retrospective 1:10:18
1:10:18
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1 #349: Meet Beanie: A MongoDB ODM + Pydantic 1:20:50
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1 #348: Dear PyGui: Simple yet Fast Python GUI Apps 1:01:32
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1 #347: Cinder - Specialized Python that Flies 1:11:04
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1 #346: 20 Recommended Packages in Review 1:13:43
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1 #345: 10 Tips and Tools for Developer Productivity 1:16:39
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1 #343: Do Excel things, get notebook Python code with Mito 1:06:14
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1 #342: Python in Architecture (as in actual buildings) 1:01:28
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1 #341: 25 Pandas Functions You Didn’t Know Existed 59:16
59:16
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1 #340: Time to JIT your Python with Pyjion? 1:13:38
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1 #339: Making Python Faster with Guido and Mark 1:01:02
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1 #338: Using cibuildwheel to manage the scikit-HEP packages 1:17:44
1:17:44
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1 #337: Kedro for Maintainable Data Science 1:03:14
1:03:14
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1 #336: Terminal magic with Rich and Textual 59:12
59:12
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1 #333: State of Data Science in 2021 1:03:59
1:03:59
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1 #331: Meet the Python Developer in Residence: Lukasz Langa 1:06:21
1:06:21
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1 #330: Apache Airflow Open-Source Workflow with Python 1:07:50
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1 #328: Piccolo: A fast, async ORM for Python (updated) 58:51
58:51
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1 #327: Little Automation Tools in Python 1:05:58
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1 #326: Building Desktop Apps with wxPython 53:32
53:32
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1 #325: MicroPython + CircuitPython 1:08:37
1:08:37
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1 #324: Gatorade-powered Python APIs 1:12:23
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1 #323: Best practices for Docker in production 1:10:47
1:10:47
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1 #321: HTMX - Clean, Dynamic HTML Pages 1:12:25
1:12:25
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1 #320: Python in the Electrical Energy Sector 1:08:01
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1 #319: Typosquatting and Supply Chains Vulnerabilities 59:43
59:43
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1 #318: Measuring your ML impact with CodeCarbon 1:07:43
1:07:43
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1 #317: Python at the US Federal Election Commission 52:39
52:39
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1 #315: Awesome FastAPI extensions and add ons 1:07:50
1:07:50
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1 #314: Ask us about modern Python projects and tools 1:04:40
1:04:40
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1 #313: Automate your data exchange with Pydantic 1:00:58
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1 #312: Python Apps that Scale to Billions of Users 1:17:09
1:17:09
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1 #311: Get inside the .git folder 1:12:16
1:12:16
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1 #310: AMA (Ask Me Anything) with Michael 1:02:04
1:02:04
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1 #309: What ML Can Teach Us About Life: 7 Lessons 55:44
55:44
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1 #308: Docker for Python Developers (2021 Edition) 1:08:05
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1 #307: Python from 1994 to 2021, my how you've grown! 1:11:56
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1 #306: Scaling Python and Jupyter with ZeroMQ 1:05:58
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1 #305: Python community at Python Discord 1:01:41
1:01:41
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1 #304: asyncio all the things with Omnilib 1:00:50
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1 #303: Python for Astronomy with Dr. Becky 1:03:56
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1 #302: The Data Engineering Landscape in 2021 1:04:33
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1 #301: Deploying and running Django web apps in 2021 1:08:34
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1 #300: Building a data science startup (panel) 1:06:22
1:06:22
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1 #299: Personal search engine with datasette and dogsheep 1:01:42
1:01:42
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1 #298: Building ML teams and finding ML jobs 56:10
56:10
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1 #297: Python year in review (2020 edition) 1:10:23
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1 #293: Learning how to learn as a developer 1:00:07
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1 #292: Pythonic identity (auth in Python ecosystem) 1:05:01
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1 #291: Operational Resilience with Pyomo 59:36
59:36
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1 #290: Side Hustles for Data Scientists 1:08:04
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1 #289: Discovering exoplanets with Python 47:26
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1 #288: 10 tips to move from Excel to Python 1:03:23
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1 #287: Testing without dependencies, mocking in Python 1:03:29
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1 #286: Python and ML at NASA Jet Propulsion Laboratory (JPL) 1:07:32
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1 #285: Dask as a Platform Service with Coiled 1:11:04
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1 #284: Modern and fast APIs with FastAPI 1:06:06
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1 #279: Modern Python Developer's Toolkit 1:01:29
1:01:29
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1 #278: Teach kids Python with real programming and fun games at Code Combat 1:05:28
1:05:28
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1 #277: 10 tips every Django developer should know 1:11:57
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1 #276: Geekout: Life in the solar system and beyond 1:14:15
1:14:15
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1 #274: Profiling data science code with FIL 58:12
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1 #273: CoCalc: A fully colloborative notebook development environment 55:06
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1 #272: No IoT things in hand? Simulate them with Device Simulator Express 51:47
51:47
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1 #271: Unlock the mysteries of time, Python's datetime that is! 1:04:33
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1 #270: Python in supply chains: oil rigs, rockets, and lettuce 59:20
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1 #269: HoloViz - a suite of tools for Python visualization 55:57
55:57
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1 #268: Analyzing dozens of notebook environments 54:24
54:24
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1 #266: Refactoring your code, like magic with Sourcery 57:05
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1 #264: 10 tips every Flask developer should know 1:08:49
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1 #262: Build a career in data science 1:11:17
1:11:17
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1 #261: Monitoring and auditing machine learning 1:00:42
1:00:42
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1 #260: From basic script to interactive data sci app with Streamlit 59:25
59:25
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1 #259: From Academia to Tech Industry and Python 1:00:58
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1 #258: Thriving in a remote developer environment 1:07:30
1:07:30
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1 #257: Exploring the galaxy with the fastest supercomputer, Python, and radio astronomy 52:23
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1 #256: Click to run your notebook with Binder 57:26
57:26
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1 #252: What scientific computing can learn from CS 1:10:58
1:10:58
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1 #251: Building and UX Testing Azure's Python SDK 52:44
52:44
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1 #250: Capture over 400x C02 as trees with AI and Python 1:03:07
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1 #249: Capture the Staff of Pythonic Knowledge in TwilioQuest 59:01
59:01
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1 #248: Climate change and your Python code 1:16:34
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1 #247: Solo maintainer of open-source in academia 1:05:20
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1 #246: Practices of the Python Pro 1:04:08
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1 #245: Python packaging landscape in 2020 1:01:54
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1 #244: Top 10 Real Python Articles of 2019 59:49
59:49
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1 #243: Python on Windows is OK, actually 57:38
57:38
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1 #242: Your education will be live-streamed 54:23
54:23
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1 #241: Opal: Full stack health care apps 53:23
53:23
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1 #240: A guided tour of the CPython source code 1:00:25
1:00:25
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1 #236: Scaling data science across Python and R 1:00:48
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1 #235: Python in your Browser with Skulpt 1:00:20
1:00:20
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1 #234: Awesome Python Applications 1:04:36
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1 #233: The Masonite Python Web Framework 1:08:18
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1 #232: Become a robot developer with Python 1:01:32
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1 #231: Advice for freelancing with Python 1:09:41
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1 #230: Python in digital humanities research 57:42
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1 #229: Building advanced Pythonic interviews with docassemble 1:00:43
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1 #228: Hunting bugs and tech startups with Python 59:41
59:41
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1 #227: Maintainable data science: Tips for non-developers 1:10:48
1:10:48
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1 #226: Building Flask APIs for data scientists 1:09:02
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1 #225: Can subinterpreters free us from Python's GIL? 1:10:38
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1 #224: 12 lessons from 100 days of web 1:08:54
1:08:54
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1 #223: Fun and Easy 2D Games with Python 1:03:59
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1 #222: Interactive graphs with Bokeh and Python 59:19
59:19
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1 #221: Empowering developers by embedding Python 1:07:04
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1 #220: Machine Learning in the cloud with Azure ML 54:44
54:44
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1 #219: Take a Python tour of duty at the United States Digital Service 1:01:36
1:01:36
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1 #218: Serverless Python functions in Azure 47:57
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1 #217: Notebooks vs data science-enabled scripts 54:25
54:25
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1 #215: The software powering Talk Python courses and podcast 1:07:51
1:07:51
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1 #214: Dive into CPython 3.8 and beyond 1:00:22
1:00:22
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1 #212: Python in Web Assembly with Pyodide 57:21
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1 #211: Classic CS problems in Python 1:08:46
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1 #210: Making the most out of in-person training 1:07:20
1:07:20
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1 #209: Inside Python's new governance model 1:07:25
1:07:25
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1 #208: Packaging, Making the most of PyCon, and more 1:10:17
1:10:17
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1 #207: Parallelizing computation with Dask 57:53
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1 #204: StaticFrame, like Pandas but safer 1:01:17
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1 #203: Beginners and Experts in Software Development 57:35
57:35
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1 #201: Choosing JupyterHub and Python over MATLAB 1:03:35
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1 #200: Escaping Excel Hell with Python and Pandas 1:06:09
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1 #199: Automate all the things with Python at Zapier 1:05:28
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1 #198: Catching up with the Anaconda distribution 1:05:14
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1 #197: Modern Python Standard Library Cookbook 1:01:25
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1 #196: Datalore: Hosted smart notebooks 49:50
49:50
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1 #194: Learning (and teaching) Python in a vacuum 1:06:50
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1 #193: Data Science Year in Review 2018 Edition 1:20:36
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1 #192: Python Year in Review 2018 Edition 59:03
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1 #191: Python's journey at Microsoft 1:12:27
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1 #189: War Stories of the Developer Evangelists 59:11
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1 #188: Async for the Pythonic web with Sanic 56:22
56:22
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1 #187: Secure all the things with HubbleStack 59:53
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1 #186: 100 Days of Python in a Magical Universe 1:02:57
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1 #185: Creating a Python 3 Culture at Facebook 1:07:46
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1 #184: Teaching Python with BBC micro:bit 1:07:38
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1 #166: Continuous delivery with Python 1:09:44
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1 #164: Python in Brain Research at the Allen Institute 57:54
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1 #162: Python in Building and Architecture 54:35
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1 #160: Lektor: Beautiful websites out of flat files 55:41
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1 #159: Inside the new PyPI launch 1:00:41
1:00:41
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1 #157: The Journal of Open Source Software 1:04:46
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1 #155: Practical steps for moving to Python 3 1:03:26
1:03:26
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1 #152: Understanding and using Python's AST 51:55
51:55
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1 #180: What's new in Python 3.7 and beyond 57:26
57:26
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1 #176: The Python Community by the Numbers 53:29
53:29
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1 #175: Teaching Python to network engineers 55:27
55:27
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1 #174: Coming into Python from another Industry (part 2) 51:06
51:06
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1 #173: Coming into Python from another Industry (part 1) 57:25
57:25
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1 #172: Nuitka: A full Python compiler 1:06:53
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1 #169: Becoming a Python content creator 1:06:40
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1 #168: 10 Python security holes and how to plug them 1:00:00
1:00:00
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1 #167: Simplifying Python's Async with Trio 55:41
55:41
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1 #151: Gradual Typing of Production Applications 1:09:00
1:09:00
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1 #150: Technical Lessons Learned from Pythonic Refactoring 59:57
59:57
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1 #149: 4 Python Web Frameworks, Compared 57:25
57:25
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1 #148: Python Book Authors' Panel Discussion 1:02:05
1:02:05
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1 #146: Building Alexa Skills with Python and Flask 1:01:07
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1 #144: Machine Learning at the Large Hadron Collider 58:22
58:22
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1 #143: Tuning Python Web App Performance 1:03:23
1:03:23
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1 #140: Level up your Python with #100DaysOfCode challenge 58:18
58:18
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1 #139: Paths into a data science career 1:02:47
1:02:47
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1 #138: Anvil: All web, all Python 1:00:11
1:00:11
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1 #137: Design concepts and tips for developers 58:03
58:03
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1 #136: Secure code lessons from Have I Been Pwned 1:00:18
1:00:18
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1 #135: Capturing human moments with AI and Python 54:09
54:09
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1 #133: Productivity for developers 1:04:28
1:04:28
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1 #132: Contributing to open source 1:04:44
1:04:44
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1 #131: Top 10 machine learning libraries 54:08
54:08
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1 #130: 10 books Python developers should be reading 52:28
52:28
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1 #129: Falcon: The bare-metal Python web framework 59:53
59:53
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1 #127: Shipping software to users 1:15:10
1:15:10
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1 #125: Django REST framework and a new API star is born 1:07:17
1:07:17
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1 #123: Lessons from 100 straight dev job interviews 46:23
46:23
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1 #122: Home Assistant: Pythonic Home Automation 58:08
58:08
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1 #117: Functional Python with Coconut 1:03:31
1:03:31
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1 #116: 10 top talks of PyCon 2017 reviewed 1:00:19
1:00:19
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1 #114: Empowering developers at the Hidden Genius project 38:02
38:02
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1 #113: Dedicated AI chips and running old Python faster at Intel 53:02
53:02
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1 #112: Geeking out in the golden years 1:07:15
1:07:15
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1 #110: Data Democratization with Redash 56:02
56:02
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1 #109: MongoDB Applied Design Patterns 1:00:25
1:00:25
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1 #108: MicroPython and Open Source Hardware at Adafruit 1:04:43
1:04:43
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1 #107: Python concurrency with Curio 1:01:20
1:01:20
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1 #106: Invent your own computer games with Python 58:12
58:12
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1 #103: Compiling Python through PyLLVM and MongoDB for Data Scientists 52:36
52:36
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1 #101: Adding a full featured Python environment to Visual Studio Code 55:11
55:11
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1 #100: Python past, present, and future with Guido van Rossum 1:02:33
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1 #99: Morepath: Super Powered Python Web Framework 1:04:52
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1 #98: Adding concurrency to Django with Django Channels 1:05:14
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1 #97: Flask, Django style with Flask-Diamond 1:05:03
1:05:03
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1 #94: Guarenteed packages via Conda and Conda-Forge 46:31
46:31
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1 #93: Spreading Python through the sciences with Software Carpentry 1:01:34
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1 #92: Bonus: Python Bytes Crossover: Python 3.6 is going to be awesome, Kite: your friendly co-developing AI 21:00
21:00
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1 #91: Top 10 Data Science Stories of 2016 1:07:34
1:07:34
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1 #89: A conversation with the Chief Data Scientist of the United States 52:39
52:39
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1 #87: PonyORM: The most Pythonic ORM yet? 56:29
56:29
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1 #85: Parsing horrible things with Python 59:46
59:46
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1 #84: Are we failing to fund Python's core infrastructure? 1:05:27
1:05:27
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1 #83: Python Videos on Demand at PyVideo 53:26
53:26
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1 #81: Python and Machine Learning in Astronomy 1:02:13
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1 #80: TinyDB: A tiny document db written in Python 47:01
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1 #78: How I built an entire game and toolchain 100% in Python 53:26
53:26
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1 #77: 20 Python Libraries You Aren't Using (But Should) 1:16:54
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1 #74: Past, Present, and Future of IronPython 50:48
50:48
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1 #73: Machine learning at the new Microsoft 1:04:43
1:04:43
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1 #72: Fashion-driven open source software at Zalando 1:01:18
1:01:18
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1 #71: Soft Skills: The software developer's life manual 1:05:32
1:05:32
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1 #70: Pythonic cover songs at Loudr 1:01:39
1:01:39
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1 #69: Write an Excellent Programming Blog 57:41
57:41
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1 #68: Crossing the streams with Podcast.__init__ 1:02:21
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1 #67: Property-based Testing with Hypothesis 58:17
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1 #66: Faster Python Programs: Measure, Don't Guess 1:04:55
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1 #65: Jump on the real-time web with RethinkDB 59:22
59:22
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1 #63: Validating Python tests with mutation testing 59:36
59:36
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1 #62: San Diego Technology Immersion Group Learns Python 1:09:21
1:09:21
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1 #45: The Python Testing Column, Now a Thing 58:49
58:49
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1 #44: Project Jupyter and IPython 1:00:11
1:00:11
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1 #43: Monitoring high performance Python apps at Opbeat 39:33
39:33
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1 #41: Getting your first dev job as a Python developer (part 2) 52:17
52:17
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1 #40: Top 10 Data Science Stories from 2015 59:19
59:19
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1 #39: Getting your first dev job as a Python developer (part 1) 50:44
50:44
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1 #38: Continuous Integration and Delivery at Codeship 1:02:50
1:02:50
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1 #37: Python Cybersecurity and Penetration Testing 1:04:02
1:04:02
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1 #36: Python IDEs with the PyCharm team 59:43
59:43
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1 #35: Turbogears and the future of Python web frameworks 1:01:15
1:01:15
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1 #34: Continuum: Scientific Python and The Business of Open Source 59:52
59:52
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1 #33: OpenStack: Cloud computing built on Python 56:37
56:37
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1 #32: PyPy.js - PyPy Python in Your Browser 59:12
59:12
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1 #31: Machine Learning with Python and scikit-learn 49:19
49:19
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1 #30: Python Community and Python at Dropbox 46:53
46:53
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1 #29: Python at the Large Hadron Collider and CERN 52:10
52:10
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1 #28: Making Python Fast: Profiling Python Code 50:09
50:09
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1 #27: Four Years of Python for High Schoolers 44:35
44:35
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1 #26: Deploying Python Web Applications (Updated) 1:13:04
1:13:04
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1 #23: 3D Printing with Python at Authentise 47:25
47:25
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1 #22: CPython Internals and Learning Python with pythontutor.com 1:02:35
1:02:35
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1 #21: PyPy - The JIT Compiled Python Implementation 53:57
53:57
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1 #20: Interactive Python and Teaching Python: Beyond Text Books 48:46
48:46
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1 #19: Automate the Boring Stuff with Python 41:05
41:05
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1 #18: Python Anti-patterns and other mistakes 48:16
48:16
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1 #17: Python on bare metal with MicroPython 51:18
51:18
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1 #60: Scaling Python to 1000's of cores with Ufora 1:07:28
1:07:28
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1 #59: SageMath - Open source is ready to compete in the classroom 59:24
59:24
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1 #58: Create better Python programs with concurrency, libraries, and patterns 54:51
54:51
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1 #57: Python performance from the inside-out at Intel 56:34
56:34
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1 #55: How our engineering environments are killing diversity (and how we can fix it) 1:01:40
1:01:40
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1 #54: Enterprise Software with Python 1:07:43
1:07:43
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1 #52: EVE Online: MMO game powered by Python 54:35
54:35
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1 #51: SigOpt: Optimizing Everything with Python 37:50
37:50
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1 #50: Web scraping at scale with Scrapy and ScrapingHub 58:17
58:17
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1 #49: Microsoft's JIT-based Python Project: Pyjion 1:03:45
1:03:45
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1 #47: Python in Typeface and Font Development 44:39
44:39
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1 #46: Python in Movies and Entertainment 57:13
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1 #15: Python at Spotify, PSF, and PyLadies 49:01
49:01
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1 #14: Moving from PHP to Python 3 with Patreon 49:26
49:26
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1 #13: Flask web framework and much, much more 50:01
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1 #12: Deep Dive into Modules and Packages 52:10
52:10
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1 #11: PyImageSearch and Computer Vision 50:06
50:06
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1 #10: Bringing Python to the Masses with Hosting and DevOps at PythonAnywhere 1:05:38
1:05:38
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1 #8: Teaching Python at Grok Learning and Classrooms 37:03
37:03
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1 #7: Robot Operating System (ROS) and ROSPy 49:50
49:50
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1 #6: Requests, PyCon, and Python's future 43:51
43:51
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1 #5: SQLAlchemy and data access in Python 1:02:42
1:02:42
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1 #4: Enterprise Python and Large-Scale Projects 54:05
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مرحبًا بك في مشغل أف ام!
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