https://github.com/WyattBlue/auto-editor - command line application for automatically editing video and audio by analyzing a variety of methods, most notably audio loudness
Most people using python for prediction are interested in accuracy and typically use sklearn or NeuralNetwork libraries (TensorFlow, PyTorch). However if you are interested in understanding the data and doing linear models,`statsmodels` is an excellent library. It has a learning curve to interpret results, but that's really something people must use to understand and get a deeper insight into data
I can't recommend django-ninja enough. It's an easy to use, extremely fast, typed API for django. I've found it to be better in almost all aspects when compared to djangorestframework.
It's gaining popularity but is still widely unknown.
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[ 4.5 ms ] story [ 32.8 ms ] thread- symbex
- polars
- python-lenses
- nbdev
https://github.com/pycompiled/compiled - compiled variants of the Python standard library
It's gaining popularity but is still widely unknown.
https://github.com/vitalik/django-ninja
Or print debugging where you can't print (eg you're in a pipeline) https://pypi.org/project/q/
Portion - represent intervals. Use this to describe tax bands, calendar appointments, etc. https://pypi.org/project/portion/
trio: https://trio.readthedocs.io/en/stable/index.html
and a couple of trio-compatible libraries that blend with it nicely:
tractor: https://github.com/goodboy/tractor
trio-parallel: https://trio-parallel.readthedocs.io/en/latest/
scalene profiler
pipgrep
pycallflow
rich
ducks,polars,duckdb
transitions for fsm
dash,perspective
smart_open,fsutil,platformdirs
environs
diskcache
datasketch,setsimilaritysearch,more_itertools
act (local github actions)
verbex (for regexp)
keyring
ftfy,chardet,unidecode
ulid,cyksuid
watchdog
dradonfly
tenacity
lox,parsl,dask,ray
delegator
huey
Using this one a lot nowadays. It's great for getting embedded JSON out of things which are not very consistent.
Extremely useful for extracting JSON from LLM responses, also great for general purpose data munging.
https://dataset.readthedocs.io/en/latest/
Used to love this one when I didn't know SQL: It's kind of like RedbeanPHP, but in Python, and lets you do stuff like this (from link) :
import dataset
db = dataset.connect('sqlite:///:memory:')
table = db['my_table']
table.insert(dict(name='John Doe', age=37))
table.insert(dict(name='Jane Doe', age=34, gender='female'))
john = table.find_one(name='John Doe')