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Watch the live stream: Watch on YouTubeAbout 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