Podcast appearances and mentions of Vincent D Warmerdam

Vincent D. Warmerdam from Marimo shares how they grew their YouTube channel for their Python notebook, using regular Shorts to reach thousands of new viewers each week. He talks about the importance of being genuinely excited about what you're building and how consistent, authentic content can help both founders and creators connect with their audience. He gives practical advice and real-world insights for anyone interested in DevRel or growing a DevTool channel.This episode is brought to you by WorkOS. If you're thinking about selling to enterprise customers, WorkOS can help you add enterprise features like Single Sign On and audit logs.Links:   •  Vincent's blog   •  Vincent's X   •  Marimo

Do you have text that you want to process automatically? Maybe you want to pull out key products or topics of conversation? Maybe you want to get the sentiment? The possibilities are many with this week's topic: NLP with spaCy and Python. Our guest, Vincent D. Warmerdam, has worked on spaCy and other tools at Explosion AI and he's here to give us his tips and tricks for working with text from Python. Episode sponsors Posit Talk Python Courses Links from the show Course: Getting Started with NLP and spaCy: talkpython.fm Vincent on X: @fishnets88 Vincent on Mastodon: @koaning Programmable Keyboards on CalmCode: youtube.com Sample Space Podcast: youtube.com spaCy: spacy.io Course: Build An Audio AI App: talkpython.fm Lemma example: github.com Code for spaCy course: github.com Python Bytes transcripts: github.com scikit-lego: github.com Projects that import "this": calmcode.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

Watch the live stream: Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training Test & Code Podcast Patreon Supporters Brian #1: Polars: Lightning-fast DataFrame library for Rust and Python Suggested by a several listeners “Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as memory model. Lazy | eager execution Multi-threaded SIMD (Single Instruction/Multiple Data) Query optimization Powerful expression API Rust | Python | ...” Python API syntax set up to allow parallel and execution while sidestepping GIL issues, for both lazy and eager use cases. From the docs: Do not kill parallelization The syntax is very functional and pipeline-esque: import polars as pl q = ( pl.scan_csv("iris.csv") .filter(pl.col("sepal_length") > 5) .groupby("species") .agg(pl.all().sum()) ) df = q.collect() Polars User Guide is excellent and looks like it's entirely written with Python examples. Includes a 30 min intro video from PyData Global 2021 Michael #2: PSF Survey is out Have a look, their page summarizes it better than my bullet points will. Brian #3: Gin Config: a lightweight configuration framework for Python Found through Vincent D. Warmerdam's excellent intro videos on gin on calmcode.io Quickly make parts of your code configurable through a configuration file with the @gin.configurable decorator. It's in interesting take on config files. (Example from Vincent) # simulate.py @gin.configurable def simulate(n_samples): ... # config.py simulate.n_samples = 100 You can specify: required settings: def simulate(n_samples=gin.REQUIRED)` blacklisted settings: @gin.configurable(blacklist=["n_samples"]) external configurations (specify values to functions your code is calling) can also references to other functions: dnn.activation_fn = @tf.nn.tanh Documentation suggests that it is especially useful for machine learning. From motivation section: “Modern ML experiments require configuring a dizzying array of hyperparameters, ranging from small details like learning rates or thresholds all the way to parameters affecting the model architecture. Many choices for representing such configuration (proto buffers, tf.HParams, ParameterContainer, ConfigDict) require that model and experiment parameters are duplicated: at least once in the code where they are defined and used, and again when declaring the set of configurable hyperparameters. Gin provides a lightweight dependency injection driven approach to configuring experiments in a reliable and transparent fashion. It allows functions or classes to be annotated as @gin.configurable, which enables setting their parameters via a simple config file using a clear and powerful syntax. This approach reduces configuration maintenance, while making experiment configuration transparent and easily repeatable.” Michael #4: Performance benchmarks for Python 3.11 are amazing via Eduardo Orochena Performance may be the biggest feature of all Python 3.11 has task groups in asyncio fine-grained error locations in tracebacks the self-type to return an instance of their class The "Faster CPython Project" to speed-up the reference implementation. See my interview with Guido and Mark: talkpython.fm/339 Python 3.11 is 10~60% faster than Python 3.10 according to the official figures And a 1.22x speed-up with their standard benchmark suite. Arriving as stable until October Extras Michael: Python 3.10.5 is available (changelog) Raycast (vs Spotlight) e.g. CMD+Space => pypi search: Joke: Why wouldn't you choose a parrot for your next application

Watch the live stream: Watch on YouTube About the show Sponsored by us: Check out the courses over at Talk Python And Brian's book too! Special guest: David Smit Brain #1: mktestdocs Vincent D. Warmerdam Tutorial with videos Utilities to check for valid Python code within markdown files and markdown formatted docstrings. Example: import pathlib import pytest from mktestdocs import check_md_file @pytest.mark.parametrize('fpath', pathlib.Path("docs").glob("**/*.md"), ids=str) def test_files_good(fpath): check_md_file(fpath=fpath) This will take any codeblock that starts with ```python and run it, checking for any errors that might happen. Putting assert statements in the code block will actually check things. Other examples in README.md for markdown formatted docstrings from functions and classes. Suggested usage is for code in mkdocs documentation. I'm planning on trying it with blog posts. Michael #2: Redis powered queues (QR3) via Scot Hacker QR queues store serialized Python objects (using cPickle by default), but that can be changed by setting the serializer on a per-queue basis. There are a few constraints on what can be pickled, and thus put into queues Create a queue: bqueue = Queue('brand_new_queue_name', host='localhost', port=9000) Add items to the queue >> bqueue.push('Pete') >> bqueue.push('John') >> bqueue.push('Paul') >> bqueue.push('George') Getting items out >> bqueue.pop() 'Pete' Also supports deque, or double-ended queue, capped collections/queues, and priority queues. David #3: 25 Pandas Functions You Didn't Know Existed Bex T So often, I come across a pandas method or function that makes me go “AH!” because it saves me so much time and simplifies my code Example: Transform Don't normally like these articles, but this one had several “AH” moments between styler options convert dtypes mask nasmallest, nalargest clip attime Brian #4: FastAPI and Rich Tracebacks in Development Hayden Kotelman Rich has, among other cool features, beautiful tracebacks and logging. FastAPI makes it easy to create web API's This post shows how to integrate the two for API's that are easy to debug. It's really only a few simple steps Create a dataclass for the logger config. Create a function that will either install rich as the handler (while not in production) or use the production log configuration. Call logging.basicConfig() with the new settings. And possibly override the logger for Uvicorn. Article contains all code necessary, including examples of the resulting logging and tracebacks. Michael #5: Dev in Residence I am the new CPython Developer in Residence Report on first week Łukasz Langa: “When the PSF first announced the Developer in Residence position, I was immediately incredibly hopeful for Python. I think it's a role with transformational potential for the project. In short, I believe the mission of the Developer in Residence (DIR) is to accelerate the developer experience of everybody else.” The DIR can: providing a steady review stream which helps dealing with PR backlog; triaging issues on the tracker dealing with issue backlog; being present in official communication channels to unblock people with questions; keeping CI and the test suite in usable state which further helps contributors focus on their changes at hand; keeping tabs on where the most work is needed and what parts of the project are most important. David #6: Dagster Dagster is a data orchestrator for machine learning, analytics, and ETL Great for local development that can be deployed on Kubernetes, etc Dagit provides a rich UI to monitor the execution, view detailed logs, etc Can deploy to Airflow, Dask, etc Quick demo? References https://www.dataengineeringpodcast.com/dagster-data-applications-episode-104/ https://softwareengineeringdaily.com/2019/11/15/dagster-with-nick-schrock/ Extras Michael: Get a vaccine, please. Python 3.10 Type info ---- er Make the 3.9, thanks John Hagen. Here is a quick example. All of these are functionally equivalent to PyCharm/mypy: # Python 3.5-3.8 from typing import List, Optional def fun(l: Optional[List[str]]) -> None: # Python 3.9+ from typing import Optional def fun(l: Optional[list[str]]) -> None: # Python 3.10+ def fun(l: list[str] | None) -> None: Note how with 3.10 we no longer need any imports to represent this type. David: Great SQL resource Joke: Pray

Watch the live stream: Watch on YouTube About the show Sponsored by Sentry: Sign up at pythonbytes.fm/sentry And please, when signing up, click Got a promo code? Redeem and enter PYTHONBYTES Special guest: Vincent D. Warmerdam koaning.io, Research Advocate @ Rasa and maintainer of a whole bunch of projects. Intro: Hello and Welcome to Python Bytes Where we deliver Python news and headlines directly to your earbuds. This is episode 235, recorded May 26 2021 I’m Brian Okken [HTML_REMOVED] [HTML_REMOVED] Brian #1: Flask 2.0 articles and reactions Change list Async in Flask 2.0 Patrick Kennedy on testdriven.io blog Great description discussion of how the async works in Flask 2.0 examples how to test async routes An opinionated review of the most interesting aspects of Flask 2.0 Miguel Grinberg video covers route decorators for common methods, ex @app.post(``"``/``"``) instead of @app.route("/", methods=["POST"]) web socket support async support Also includes some extensions Miguel has written to make things easier Great discussion, worth the 25 min play time. See also: Talk Python Episode 316 Michael #2: Python 3.11 will be 2x faster? via Mike Driscoll From the Python Language summit Guido asks "Can we make CPython faster?” We covered the Shannon Plan for speedups. Small team funded by Microsoft: Eric Snow, Mark Shannon, myself (might grow) Constrains: Mostly don’t break things. How to reach 2x speedup in 3.11 Adaptive, specializing bytecode interpreter “Zero overhead” exception handling Faster integer internals Put __dict__ at a fixed offset (-1?) There’s machine code generation in our future Who will benefit Users running CPU-intensive pure Python code •Users of websites built in Python Users of tools that happen to use Python Vincent #3: DEON, a project with meaningful checklists for data science projects! It’s a command line app that can generate checklists. You customize checklists There’s a set of examples (one for for each check) that explain why the checks it is matter. Make a little course on calmcode to cover it. Brian #4: 3 Tools to Track and Visualize the Execution of your Python Code Khuyen Tran Loguru — print better exceptions we covered in episode 111, Jan 2019, but still super cool snoop — print the lines of code being executed in a function covered in episode 141, July 2019, also still super cool heartrate — visualize the execution of a Python program in real-time this is new to us, woohoo Nice to have one persons take on a group of useful tools Plus great images of them in action. Michael #5: DuckDB + Pandas via __AlexMonahan__ What’s DuckDB? An in-process SQL OLAP database management system SQL on Pandas: After your data has been converted into a Pandas DataFrame often additional data wrangling and analysis still need to be performed. Using DuckDB, it is possible to run SQL efficiently right on top of Pandas DataFrames. Example import pandas as pd import duckdb mydf = pd.DataFrame({'a' : [1, 2, 3]}) print(duckdb.query("SELECT SUM(a) FROM mydf").to_df()) When you run a query in SQL, DuckDB will look for Python variables whose name matches the table names in your query and automatically start reading your Pandas DataFrames. For many queries, you can use DuckDB to process data faster than Pandas, and with a much lower total memory usage, without ever leaving the Pandas DataFrame binary format (“Pandas-in, Pandas-out”). The automatic query optimizer in DuckDB does lots of the hard, expert work you’d need in Pandas. Vincent #6: I work for a company called Rasa. We make a python library to make virtual assistants and there’s a few community projects. There’s a bunch of cool showcases, but one stood out when I was checking our community showcase last week. There’s a project that warns folks about forest fire updates over text. The project is open-sourced on GitHub and can be found here. There’s also a GIF demo here. Amit Tallapragada and Arvind Sankar observed that in the early days of the fires, news outlets and local governments provided a confusing mix of updates about fire containment and evacuation zones, leading some residents to evacuate unnecessarily. They teamed up to build a chatbot that would return accurate information about conditions in individual cities, including nearby fires, air quality, and weather data. What’s cool here isn’t just that Vincent is biased (again, he works for Rasa), it’s also a nice example of grass-roots impact. You can make a lot of impact if there’s open APIs around. They host a scraper that scrapes fire/weather info every 10 seconds. It also fetches evacuation information. You can text a number and it will send you up-to-date info based on your city. It will also notify you if there’s an evacuation order/plan. They even do some fuzzy matching to make sure that your city is matched even when you make a typo. Extras Michael PyCon US 2024 and 2025 Announced Vincent: Human-Learn: a suite of tools to have humans define models before resorting to machines. It’s scikit-learn compatible. One of the main features is that you’re able to draw a model! There’s a small guide that shows how to outperform a deep learning implementation by doing exploratory data analysis. It turns out, you can outperform Keras sometimes. There’s a suite of tools to turn python functions into scikit-learn compatible models. Keyword arguments become grid-search-able. Tutorial on calmcode.io to anybody interested. Can also be used for Bulk Labelling. Joke

Sponsored by us! Support our work through: Our courses at Talk Python Training pytest book Patreon Supporters Special guests: Peter Kazarinoff Brian #1: calmcode.io by Vincent D. Warmerdam Suggested by Rens Dimmendaal Great short intro tutorials & videos. Not deep dives, but not too shallow either. Suggestions: pytest rich datasette I watched the whole series on datasette this morning and learned how to turn a csv data file into a sqlite database use datasette to open a server to explore the data filter the data visualize the data with datasette-vega plugin and charting options learn how I can run random SQL, but it’s safe because it’s read only use it as an API that serves either CSV or json deploy it to a cloud provider by wrapping it in a docker container and deploying that add user authentication to protect the service explore tons of available data sets that have been turned into live services with datasette Michael #2: Natural sort (aka natsort) via Brian Skinn Simple yet flexible natural sorting in Python. Python sort algorithm sorts lexicographically >>> a = ['2 ft 7 in', '1 ft 5 in', '10 ft 2 in', '2 ft 11 in', '7 ft 6 in'] >>> sorted(a) ['1 ft 5 in', '10 ft 2 in', '2 ft 11 in', '2 ft 7 in', '7 ft 6 in'] natsort provides a function natsorted that helps sort lists "naturally” >>> natsorted(a) ['1 ft 5 in', '2 ft 7 in', '2 ft 11 in', '7 ft 6 in', '10 ft 2 in'] Other things that can be sorted: versions file paths (via os_sorted) signed floats (via realsorted) Can go faster using fastnumbers Peter #3: Python controlling a helicopter on Mars. First Flight of the Mars Drone/Helicopter was April 19: https://mars.nasa.gov/technology/helicopter/#Overview. The Drone/Helicopter is called Ingenuity. The helicopter rode to Mars attached to the belly of the Perseverance rover. Community powers NASA’s Ingenuity Helicopter: DEVELOPERS AROUND THE WORLD CONTRIBUTE TO HISTORIC FLIGHT: https://github.com/readme/nasa-ingenuity-helicopter The Drone/Helicopter flight control software is called F’ (pronounced f-prime, sounds like Python f’strings :). You can clone and install the flight control software from GitHub. Make sure you have Python and pip installed. https://github.com/nasa/fprime Brian #4: Pydantic, FastAPI, Typer will all run on 3.10, 3.11, and into the future suggested by an Angry Dwarf It’s a bit of an emotional roller coaster this last week even for those of us on the sidelines watching. I’m sure it was even more so for those involved. Short version: Pydantic, FastAPI, Typer, etc will continue to run as is in 3.10 Minor changes might be necessary in 3.11, but most likely all of us bystanders and users of these packages won’t even see the change, or we will be given specific instructions on what we need to change well ahead of time. If things change in 3.11, your code might still work fine, and you can test that today if you are worried about it. All project leads are involved and talking with the Steering Council. The Steering Council has all of our interests and Pythons in mind and wants to make improvements to Python in a sane way. So don’t freak out. Smart and kind people are involved and know what they are doing. Slightly more detail that I don’t really want to read, and summarized to my perspective: Something about an existing PEP 563, titled Postponed Evaluation of Annotations It was part of 3.7 and it included: “In Python 3.10, function and variable annotations will no longer be evaluated at definition time. Instead, …” This would have implications on Pydantic and projects using it and similar methods, like FastAPI, Typer, … Panic ensues, people wringing their hands, bystanders confused. BTW, the Python steering council knows what they are doing and is aware of all of this already. But lots of people jumped on the bandwagon anyway and freaked out. Even I was thinking “Ugh. I use Typer and FastAPI, can I still use them in 3.10?” Luckily, Sebastian Ramirez posted: I've seen some incorrect conclusions that FastAPI and pydantic "can't be used with Python 3.10". Let's clear that up. In the worst-case scenario (which hasn't been decided), some corner cases would not work and require small refactors. And also if you are worried about the future and your own use as is, you can use from __future__ import annotations to try the new system out. Also thanks Sebastian Then there is this message by Thomas Wouters about PEP 563 and 3.10 “The Steering Council has considered the issue carefully, along with many of the proposed alternatives and solutions, and we’ve decided that at this point, we simply can’t risk the compatibility breakage of PEP 563. We need to roll back the change that made stringified annotations the default, at least for 3.10. (Pablo is already working on this.) “To be clear, we are not reverting PEP 563 itself. The future import will keep working like it did since Python 3.7. We’re delaying making PEP 563 string-based annotations the default until Python 3.11. This will give us time to find a solution that works for everyone (or to find a feasible upgrade path for users who currently rely on evaluated annotations). Some considerations that led us to this decision: …” Michael #5: Extra, Extra, Extra, Extra hear all about it No social trackers on Python Bytes or Talk Python. Python packages on Mars More Mars NordVPN and “going dark” Nobody wants anything to do with Google's new tracking mechanism FLoC (Android Police, Ars Technica). From EFF: Google’s pitch to privacy advocates is that a world with FLoC will be better than the world we have today, where data brokers and ad-tech giants track and profile with impunity. But that framing is based on a false premise that we have to choose between “old tracking” and “new tracking.” It’s not either-or. Instead of re-inventing the tracking wheel, we should imagine a better world without the myriad problems of targeted ads. Peter #6: Build Python books with Jupyter-Book There are many static site generators for Python: Sphinx, Pelican, MkDocs… Jupyter-Book is a static site generator that makes online books from Jupyter notebooks and markdown files. See the Jupyter-book docs. Books can be published on GitHub pages and there is a GitHub action to automatically re-publish your book with each git push. A gallery of Jupyter-books includes: Geographic Data Science with Python, Quantitative Economics with Python, the UW Data Visualization Curriculum, and a book on Algorithms for Automated Driving. All the books are free an online. Extras Brian 2021 South African Pycon, PyConZA - https://za.pycon.org/. The conference will be on 7 and 8 October entirely online deadpendency update . Within a day of us talking about deadpendency last week, the project maintainer added support for pyproject.toml. So projects using poetry, flit should work now. I imagine setuptools with pyproject.toml should also work. Peter Peter’s Book: Problem Solving with Python (dead trees) or free online Joke More code comments // Dear future me. Please forgive me. // I can't even begin to express how sorry I am. try { ... } catch (SQLException ex) { // Basically, without saying too much, you're screwed. Royally and totally. } catch(Exception ex){ //If you thought you were screwed before, boy have I news for you!!! } // This is crap code but it's 3 a.m. and I need to get this working. One more: From TwoHardThings by Martin Fowler: Original saying: There are only two hard things in Computer Science: cache invalidation and naming things. -- Phil Karlton Then there’s This tweet.

In today's episode, senior developer advocate Rachael talks with Rasa developer advocate Vincent D. Warmerdam about how spelling errors, how he got into NLP and a little bit of behind the scenes of developer relations at Rasa. Stay tuned to the end to help us shoutout our newest Rasa contributors! --- Send in a voice message: https://anchor.fm/rasachats/message

PyChat: PyCon Africa is happening now data science for beginners workshop this Sunday PyData Global DfP extended to 16th Aug - Joke about PyData CfP PyjamasConf CfP is opened PyBamn just joined NumFocus Numfocus is looking for a Dev Rel Raspberry Pi Keyboard in Japanese Create google assistant with Raspberry PI??? Mid Meet - Hall of Fame: Vincent D. Warmerdam - co-founder of PyData Amsterdam, Developer Advocate for Rasa Follow Vincent on Twitter PyPi Highlights - Rasa