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Special guest: David Smit
Brain #1: mktestdocs
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.
assertstatements 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')
>> bqueue.pop() 'Pete'
- Also supports deque, or double-ended queue, capped collections/queues, and priority queues.
- 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
- Don’t normally like these articles, but this one had several “AH” moments
- convert dtypes
- nasmallest, nalargest
- 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.
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?
- 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.