Биномиальное — это не нормальное распределение
Вероятно разные распределения скорее описывают разные системы, чем характеризуют разные состояния одной. На примере биномиального, с одной стороны, убеждаемся в специфике применения определенного распределения, с другой, — выясняем при каких параметрах его можно считать частным случаем нормального, и стоит ли доводить до этого. С графиками и без формул
https://habr.com/ru/articles/976468/
#распределения_вероятностей #python #numpy #seaborn #статистический_анализ #случайность #нормальное_распределение #биноминальное_распределение #графики_и_диаграммы #гистограммы
Как реализовать выборочную долговременную память в LLM-боте на Python
LLM-модели хорошо решают задачи диалога, но имеют одно ключевое ограничение: отсутствие встроенной долговременной памяти. Модель опирается только на текущий контекст.
https://habr.com/ru/articles/976466/
#python #llm #telegrambot #bot #bots
Johnnycanencrypt 0.17.0 released https://kushaldas.in/posts/johnnycanencrypt-0-17-0-released.html #openpgp #Python #encryption #privacy #security #rust
Feedzai is hiring Systems Research Engineer- Software Engineer, Data and ML Infrastructure
🔧 #java #python #kafka
🌎 Remote; Portugal
⏰ Full-time
🏢 Feedzai
Job details https://jobsfordevelopers.com/jobs/systems-research-engineer-software-engineer-data-and-ml-infrastructure-at-feedzai-com-jul-18-2025-03b692?utm_source=mastodon.world&utm_medium=social&utm_campaign=posting
#jobalert #jobsearch #hiring
Questions about Kent's port of ANSI cl's condition handling to #python #commonLisp (T- 40 minutes)
-Who is the target audience? Lisp or python users?
-Does it offer new functionality or just [] expressivity?
-How does it integrate with python's native conditions?
-Why not integrate in a lower level way?
-How was adoption of this condition system achieved in Common Lisp?
-Where would this be useful?
-Are there ways people can get involved?
@kentpitman
【Python超入門講座】02.Pythonとは?|Pythonの特徴やできることなどをわかりやすく解説【プログラミング初心者向け】
#Python #Python基礎・入門 #Python基礎・入門 #Python

Example of using Quart and htmx to render a Chart.js line graph using HTML data attributes https://gavinw.me/notes/python/htmx-chartjs.html
#python #javascript
Transformada de Fourier 2D de una imagen
Festival Internacional de cine y musica de Derechos Humanos
Cine Magaly
FFT #python Costa Rica #CostaRica San Jose
Using #Python's #pathlib to compare two repos and get back some missing files from a "recovered" version of a repo (mostly stuff in .gitignore that is handy not to discard right now).
from pathlib import Path
a = Path('sketch-a-day')
b = Path('sketch-a-day_broken')
files_a = {p.relative_to(a) for p in a.rglob('*')
if '.git' not in str(p)
if 'cache' not in str(p)
if 'checkpoint' not in str(p)
}
files_b = {p.relative_to(b) for p in b.rglob('*')
if '.git' not in str(p)
if 'cache' not in str(p)
if 'checkpoint' not in str(p)
}
missing = files_b - files_a
for p in missing:
(b / p).rename((a / p))
Running the whole test suite (~135 tests) for django-new takes around 20 seconds. Trying out the new to me library, pyfakefs: https://pytest-pyfakefs.readthedocs.io.
Example test results:
- Using `tmp_path`: 0.95s
- Using `fs`: 0.17s
I'll take a 5x speed-up if I don't run into any gotchas!
Here’s what I'm following to replace tmp_path in #pytest: https://pytest-pyfakefs.readthedocs.io/en/latest/troubleshooting.html#a-replacement-for-the-tmp-path-fixture.
I use this RPi Reporter #python thingy by #IronSheep.
It's great and all, but sometimes I want to adjust something, because it somehow regularly mixes up Pis, especially when I repurpose a Pi.
Now, usually that's not a problem. But in this case, EVERY time I see it, I feel like I should do something about the script...
I don't know WHAT to do though, because it's too hard for me to properly untangle...
I keep forgetting how rewarding it is to finish a core feature of something and then making a small bit of UI or command that shows the state of it.
You get to have a Nice Visual for your hard work and see it going somehow.
#exosphere #python #softwareDev
