#235: Flask 2.0 Articles and Reactions

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About the show

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Special guest: Vincent D. Warmerdam koaning.io, Research Advocate @ Rasa and maintainer of a whole bunch of projects.

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