After the DjangoCon Europe coffee break, back into the room for @jake ☕️
Django supports multiple database connections, but leaves it up to you to manage how to use them and which queries to send where… let’s dig into this 🙂
python
Last talk of the first day at DjangoCon Europe 2026 🇬🇷
Now listening to Tim Bell 🎤
As tables get extremely large, the abstraction starts to break down and performance issues show up… curious to see how partitioning helps in real-world cases 🙂
First keynote of DjangoCon Europe 2026 just started 🎤
Really happy to see @carlton on stage opening with a talk about keeping Python dynamic while adding small “static islands” where they actually help, curious to see how this lands 🙂
Ever wanted to work on open source full-time? Come join me at the @openhomefoundation; we’ve got 5 new roles on @homeassistant.
👩💼 Program Manager
🐍 Python Engineers
✍️ Technical Writers
🌍 Remote. Open source, every day.
Links in replies 👇
#HomeAssistant #OpenSource #Jobs #OpenSource #Hiring #Python #TechWriters
🤝 We’re happy to announce the Django Software Foundation (DSF) as a sponsor of #PyConAfrica2025! 🐍✨
The DSF works to support and advance the Django framework—funding sprints, meetups, community events, and ensuring its long-term growth for developers worldwide. 🌍
🎟️ Don’t miss out—get your tickets: https://za.pycon.org/tickets
#PyConAfrica #Django #DSF #Python @django
Pessoal,
Estamos iniciando um projeto open source chamado vitae, a ideia é ser o seu curriculo vitae, online, no fediverso.
Será um linkdisney-like, federado, pensando na pessoa profissional, e no ser humano por trás dela.
Não vai ter timeline com coach quântico, até pq já temos o mastodon pra isso né, mas as mudanças no currículo poderão aparecer na TL ou não, de acordo com oq a pessoa usuária quiser mostrar.
O Backend vai ser PYTHON com FASTAPI e quem tá puxando os requisitos e arquitetura é o Dr. @riverfount
O Frontend vai ter o melhor e mais moderno do JS/Deno, quem tá puxando os requisitos e arquitetura é o senhor @jedi.
Eu to cuidando da INFRA.
Quer entrar e ajudar a criar essa maravilha? só vem! ;)
Tem ticket pra todo mundo! :P
Estamos nos organizando o role via https://mattermost.bolha.chat
Aqui o convite para entrar!
https://mattermost.bolha.chat/signup_user_complete/?id=w543hb3aypgyfjy8eh9x9da8oc&md=link&sbr=fa
cc @dunossauro , se puder repassar pros seus alunos, pode ser uma oportunidade de participarem de um projeto open source do zero.
cc @melissawm
cc @ericof
Pythonistas, espalhem!
JSistas, espalhem!
BOOST PLS
Uma iniciativa Bolha.io/Bolha.us
Edited 1y ago
In a couple talks now, I've claimed that using django-simple-deploy should be a good way for experienced developers to try out a new hosting platform.
Before this morning, I had never actually used Render. I started reading the docs in order to help a new plugin author, but I quickly stopped and just tried their plugin.
It worked, and it was *so much easier* to run `git diff` and see what changes the plugin made to my sample project.
Edited 1y ago
With less than two weeks until @pycon, we’re excited to share our Annual Impact Report for 2023 🤩 Check it out, share, and come tell us what you think at the PSF Booth at #PyConUS! #python
https://www.python.org/psf/annual-report/2023/
Under the hood update!
I’ve finally retired the old cron + sh setup for the weather bots. It served us well, but it had a major flaw: if I rebooted the server while it was posting, the job just died halfway. If the server was down during a scheduled slot, the forecast was lost forever.
So, I wrote a custom Python daemon to run inside the FreeBSD Jails.
I’ve finally retired the old cron + sh setup for the weather bots. It served us well, but it had a major flaw: if I rebooted the server while it was posting, the job just died halfway. If the server was down during a scheduled slot, the forecast was lost forever.
So, I wrote a custom Python daemon to run inside the FreeBSD Jails.
- It’s stateful now. If a crash happens at city 15 of 50, it resumes exactly there on reboot.
- If the server naps/is rebooting during a scheduled run, the bot realizes it missed a slot and runs immediately upon waking up.
和我一起做3A游戏《归乡之路》（三） 大家好，我是赛博红兔。欢迎来到《和我一起做3A游戏》第三集！这个系列是对pygame这个Python的游戏库的所有主要功能的介绍。通过这个教程，你应该能掌握制作任何2D游戏的工具，今天我们继续来做《归乡之路》这个打字游戏。先来回顾一下上一集的内容，我们设计了小猫的动画音效和打字的核心玩法，还有分数统计和显示。主要是了解了用矩形绘画，精灵这个pygame核心的类，还有游戏事件的触发和监听。那么今天，我们就在这个代码基础上继续做游戏。我们最后要完成的任务是创建背景的花草树木Trees和家House的类，毕竟小猫还需要回家。最后还要加上游戏BGM一首轻松愉快的背景音乐。今天，我们最主要要学习的核心是处理精灵之间的碰撞。
https://cyberhongtu.com/2024/05/10/%e5%92%8c%e6%88%91%e4%b8%80%e8%b5%b7%e5%81%9a3a%e6%b8%b8%e6%88%8f%e3%80%8a%e5%bd%92%e4%b9%a1%e4%b9%8b%e8%b7%af%e3%80%8b%ef%bc%88%e4%b8%89%ef%bc%89/ #Pygame, #Python, #Python教学, #游戏制作
Edited 2y ago
Want to go deeper with SQLAlchemy, FastAPI Path Operations, Django REST Framework & Channels?
Check out this series of Django articles by @jerivas and various OddBirds emeritus. https://www.oddbird.net/tags/django/
What if Django had an automatic ORM feature matrix — showing which features each database really supports, straight from the code? 🧩
I’ve shared some ideas (with demo data!) on how it could work and why it’d be great for Django’s docs 💡
https://www.paulox.net/2025/10/06/django-orm-comparison/
CC @django
#Django #Python #ORM #OpenSource #Database #PostgreSQL #SQLite #MariaDB #MySQL
🎉 Django PostgreSQL Anonymizer v0.1.0b1 (Beta) is live!
Database anonymization for Django + PostgreSQL with role-based access, context managers, pre-built presets & comprehensive docs.
📦 pip install django-postgres-anonymizer==0.1.0b1
📚 https://django-postgres-anonymizer.readthedocs.io/
🐙 https://github.com/CuriousLearner/django-postgres-anonymizer
Looking for beta testers!
🆓 Wagtail Space is free and virtual next week.
🎟️ Sign up to support the event and drop by to learn something new.
Wagtail Space is TOMORROW! Don't be the late bird that misses the spaceship!
Get your FREE ticket here: https://events.zoom.us/ev/AtWesoZI_eMUAZtG3IN1jHe29h3D8ymQo_CtHXHif8OXHxce920A~AmKLzn7GfJ8vV51FiJz9pNpTy55HaWLo4t8I4dqDbTM5nbyP41_CgAYz_fXVvA638XHQaKXNrH9jlJGSyHzvQwF6lQ
🎭✨ The world premiere of Ada — a rehearsed reading written by Emily Holyoake & performed by Chronic Insanity — is available to watch on YouTube!
Catch it here 👉 https://www.youtube.com/watch?v=CtrsssksCNU
Emily also shared her inspiration & writing process in a brilliant blog post:
You can find that here 👉 https://2025.pyconuk.org/2025/09/staging-ada-at-pycon2025/
💜 Huge thanks to Emily and the Chronic Insanity team for this extra-special event 🐍
#Python #Community #Theatre #AdaLovelace #PyConUK 2025
Here's an ~ official ~ release announcement for #numpydantic
repo: https://github.com/p2p-ld/numpydantic
docs: https://numpydantic.readthedocs.io
Problems: @pydantic is great for modeling data!! but at the moment it doesn't support array data out of the box. Often array shape and dtype are as important as whether something is an array at all, but there isn't a good way to specify and validate that with the Python type system. Many data formats and standards couple their implementation very tightly with their schema, making them less flexible, less interoperable, and more difficult to maintain than they could be. The existing tools for parameterized array types like nptyping and jaxtyping tie their annotations to a specific array library, rather than allowing array specifications that can be abstract across implementations.
numpydantic is a super small, few-dep, and well-tested package that provides generic array annotations for pydantic models. Specify an array along with its shape and dtype and then use that model with any array library you'd like! Extending support for new array libraries is just subclassing - no PRs or monkeypatching needed. The type has some magic under the hood that uses pydantic validators to give a uniform array interface to things that don't usually behave like arrays - pass a path to a video file, that's an array. pass a path to an HDF5 file and a nested array within it, that's an array. We take advantage of the rest of pydantic's features too, including generating rich JSON schema and smart array dumping.
This is a standalone part of my work with @linkml arrays and rearchitecting neurobio data formats like NWB to be dead simple to use and extend, integrating with the tools you already use and across the experimental process - specify your data in a simple yaml format, and get back high quality data modeling code that is standards-compliant out of the box and can be used with arbitrary backends. One step towards the wild exuberance of FAIR data that is just as comfortable in the scattered scripts of real experimental work as it is in carefully curated archives and high performance computing clusters. Longer term I'm trying to abstract away data store implementations to bring content-addressed p2p data stores right into the python interpreter as simply as if something was born in local memory.
plenty of todos, but hope ya like it.
![[This and the following images aren't very screen reader friendly with a lot of code in them. I'll describe what's going on in brackets and then put the text below.
In this image: a demonstration of the basic usage of numpydantic, declaring an "array" field on a pydantic model with an NDArray class with a shape and dtype specification. The model can then be used with a number of different array libraries and data formats, including validation.]
Numpydantic allows you to do this:
from pydantic import BaseModel
from numpydantic import NDArray, Shape
class MyModel(BaseModel):
array: NDArray[Shape["3 x, 4 y, * z"], int]
And use it with your favorite array library:
import numpy as np
import dask.array as da
import zarr
# numpy
model = MyModel(array=np.zeros((3, 4, 5), dtype=int))
# dask
model = MyModel(array=da.zeros((3, 4, 5), dtype=int))
# hdf5 datasets
model = MyModel(array=('data.h5', '/nested/dataset'))
# zarr arrays
model = MyModel(array=zarr.zeros((3,4,5), dtype=int))
model = MyModel(array='data.zarr')
model = MyModel(array=('data.zarr', '/nested/dataset'))
# video files
model = MyModel(array="data.mp4")](/proxy/post_attachment/231932/9159e4f6df.png "[This and the following images aren't very screen reader friendly with a lot of code in them. I'll describe what's going on in brackets and then put the text below.
In this image: a demonstration of the basic usage of numpydantic, declaring an \"array\" field on a pydantic model with an NDArray class with a shape and dtype specification. The model can then be used with a number of different array libraries and data formats, including validation.]
Numpydantic allows you to do this:
from pydantic import BaseModel
from numpydantic import NDArray, Shape
class MyModel(BaseModel):
array: NDArray[Shape[\"3 x, 4 y, * z\"], int]
And use it with your favorite array library:
import numpy as np
import dask.array as da
import zarr
# numpy
model = MyModel(array=np.zeros((3, 4, 5), dtype=int))
# dask
model = MyModel(array=da.zeros((3, 4, 5), dtype=int))
# hdf5 datasets
model = MyModel(array=('data.h5', '/nested/dataset'))
# zarr arrays
model = MyModel(array=zarr.zeros((3,4,5), dtype=int))
model = MyModel(array='data.zarr')
model = MyModel(array=('data.zarr', '/nested/dataset'))
# video files
model = MyModel(array=\"data.mp4\")")
![[Further demonstration of validation and array expression, where a Union of NDArray specifications can specify a more complex data type - eg. an image that can be any shape in x and y, an RGB image, or a specific resolution of a video, each with independently checked dtypes]
For example, to specify a very special type of image that can either be
a 2D float array where the axes can be any size, or
a 3D uint8 array where the third axis must be size 3
a 1080p video
from typing import Union
from pydantic import BaseModel
import numpy as np
from numpydantic import NDArray, Shape
class Image(BaseModel):
array: Union[
NDArray[Shape["* x, * y"], float],
NDArray[Shape["* x, * y, 3 rgb"], np.uint8],
NDArray[Shape["* t, 1080 y, 1920 x, 3 rgb"], np.uint8]
]
And then use that as a transparent interface to your favorite array library!
Interfaces
Numpy
The Coca-Cola of array libraries
import numpy as np
# works
frame_gray = Image(array=np.ones((1280, 720), dtype=float))
frame_rgb = Image(array=np.ones((1280, 720, 3), dtype=np.uint8))
# fails
wrong_n_dimensions = Image(array=np.ones((1280,), dtype=float))
wrong_shape = Image(array=np.ones((1280,720,10), dtype=np.uint8))
# shapes and types are checked together, so this also fails
wrong_shape_dtype_combo = Image(array=np.ones((1280, 720, 3), dtype=float))](/proxy/post_attachment/231933/0ff69a7f55.png "[Further demonstration of validation and array expression, where a Union of NDArray specifications can specify a more complex data type - eg. an image that can be any shape in x and y, an RGB image, or a specific resolution of a video, each with independently checked dtypes]
For example, to specify a very special type of image that can either be
a 2D float array where the axes can be any size, or
a 3D uint8 array where the third axis must be size 3
a 1080p video
from typing import Union
from pydantic import BaseModel
import numpy as np
from numpydantic import NDArray, Shape
class Image(BaseModel):
array: Union[
NDArray[Shape[\"* x, * y\"], float],
NDArray[Shape[\"* x, * y, 3 rgb\"], np.uint8],
NDArray[Shape[\"* t, 1080 y, 1920 x, 3 rgb\"], np.uint8]
]
And then use that as a transparent interface to your favorite array library!
Interfaces
Numpy
The Coca-Cola of array libraries
import numpy as np
# works
frame_gray = Image(array=np.ones((1280, 720), dtype=float))
frame_rgb = Image(array=np.ones((1280, 720, 3), dtype=np.uint8))
# fails
wrong_n_dimensions = Image(array=np.ones((1280,), dtype=float))
wrong_shape = Image(array=np.ones((1280,720,10), dtype=np.uint8))
# shapes and types are checked together, so this also fails
wrong_shape_dtype_combo = Image(array=np.ones((1280, 720, 3), dtype=float))")
![[Demonstration of usage outside of pydantic as just a normal python type - you can validate an array against a specification by checking it the array is an instance of the array specification type]
And use the NDArray type annotation like a regular type outside of pydantic – eg. to validate an array anywhere, use isinstance:
array_type = NDArray[Shape["1, 2, 3"], int]
isinstance(np.zeros((1,2,3), dtype=int), array_type)
# True
isinstance(zarr.zeros((1,2,3), dtype=int), array_type)
# True
isinstance(np.zeros((4,5,6), dtype=int), array_type)
# False
isinstance(np.zeros((1,2,3), dtype=float), array_type)
# False](/proxy/post_attachment/231934/173cae34ba.png "[Demonstration of usage outside of pydantic as just a normal python type - you can validate an array against a specification by checking it the array is an instance of the array specification type]
And use the NDArray type annotation like a regular type outside of pydantic – eg. to validate an array anywhere, use isinstance:
array_type = NDArray[Shape[\"1, 2, 3\"], int]
isinstance(np.zeros((1,2,3), dtype=int), array_type)
# True
isinstance(zarr.zeros((1,2,3), dtype=int), array_type)
# True
isinstance(np.zeros((4,5,6), dtype=int), array_type)
# False
isinstance(np.zeros((1,2,3), dtype=float), array_type)
# False")
![[Demonstration of JSON schema generation using the sort of odd case of an array with a specific dtype but an arbitrary shape. It has to use a recursive JSON schema definition, where the items of a given JSON array can either be the innermost dtype or another instance of that same array. Since JSON Schema doesn't support extended dtypes like 8-bit integers, we encode that information as maximum and minimum constraints on the `integer` class and add it in the schema metadata. Since pydantic renders all recursive schemas like this in the same $defs block, we use a blake2b hash against the dtype specification to keep them deduplicated.]
numpydantic can even handle shapes with unbounded numbers of dimensions by using recursive JSON schema!!!
So the any-shaped array (using nptyping’s ellipsis notation):
class AnyShape(BaseModel):
array: NDArray[Shape["*, ..."], np.uint8]
is rendered to JSON-Schema like this:
{
"$defs": {
"any-shape-array-9b5d89838a990d79": {
"anyOf": [
{
"items": {
"$ref": "#/$defs/any-shape-array-9b5d89838a990d79"
},
"type": "array"
},
{"maximum": 255, "minimum": 0, "type": "integer"}
]
}
},
"properties": {
"array": {
"dtype": "numpy.uint8",
"items": {"$ref": "#/$defs/any-shape-array-9b5d89838a990d79"},
"title": "Array",
"type": "array"
}
},
"required": ["array"],
"title": "AnyShape",
"type": "object"
}](/proxy/post_attachment/231935/4a7a60f64b.png "[Demonstration of JSON schema generation using the sort of odd case of an array with a specific dtype but an arbitrary shape. It has to use a recursive JSON schema definition, where the items of a given JSON array can either be the innermost dtype or another instance of that same array. Since JSON Schema doesn't support extended dtypes like 8-bit integers, we encode that information as maximum and minimum constraints on the `integer` class and add it in the schema metadata. Since pydantic renders all recursive schemas like this in the same $defs block, we use a blake2b hash against the dtype specification to keep them deduplicated.]
numpydantic can even handle shapes with unbounded numbers of dimensions by using recursive JSON schema!!!
So the any-shaped array (using nptyping’s ellipsis notation):
class AnyShape(BaseModel):
array: NDArray[Shape[\"*, ...\"], np.uint8]
is rendered to JSON-Schema like this:
{
\"$defs\": {
\"any-shape-array-9b5d89838a990d79\": {
\"anyOf\": [
{
\"items\": {
\"$ref\": \"#/$defs/any-shape-array-9b5d89838a990d79\"
},
\"type\": \"array\"
},
{\"maximum\": 255, \"minimum\": 0, \"type\": \"integer\"}
]
}
},
\"properties\": {
\"array\": {
\"dtype\": \"numpy.uint8\",
\"items\": {\"$ref\": \"#/$defs/any-shape-array-9b5d89838a990d79\"},
\"title\": \"Array\",
\"type\": \"array\"
}
},
\"required\": [\"array\"],
\"title\": \"AnyShape\",
\"type\": \"object\"
}")
Are you in #tech and running your own #Fediverse instance? You might want to join an Activity Pub relay instance!
My relay at https://fedi-relay.gyptazy.com has currently 139 instances connected, mostly tech related sharing the same mindset and interests like #Linux, #BSD, #Ansible, #Proxmox, #Coding, and many more! You can easily join from your instance when using #Pleroma, #snac (#snac2), #Mastodon and its forks 🙂
#fedi #fediworld #fedicommunity #community #FreeBSD #OpenBSD #NetBSD #homelab #Python #Debian #RockyLinux #Feditips
My relay at https://fedi-relay.gyptazy.com has currently 139 instances connected, mostly tech related sharing the same mindset and interests like #Linux, #BSD, #Ansible, #Proxmox, #Coding, and many more! You can easily join from your instance when using #Pleroma, #snac (#snac2), #Mastodon and its forks 🙂
#fedi #fediworld #fedicommunity #community #FreeBSD #OpenBSD #NetBSD #homelab #Python #Debian #RockyLinux #Feditips
🎉 I hadn't kept up with what Django's Steering Council had been up to outside of random board updates, and I'm happy to share how impressed I am with how transparent they are.
They are meeting several times a month and sharing their minutes as they go. https://github.com/django/steering-council
They are even sharing on the Django Forum to bring more visibility to what they are doing https://forum.djangoproject.com/t/django-steering-council-meetings-2025/38306
These are very welcoming changes. 👏👏
Edited 1y ago
Pillow 12.0.0 is out released!
🎨 Support for Python 3.14!
🎨 Dropped EOL 3.9!
🎨 New ImageText.Text API!
🎨 Removed deprecations!
🎨 New deprecations!
📜 And more: https://pillow.readthedocs.io/en/stable/releasenotes/12.0.0.html
📜📜📜 And much more: https://github.com/python-pillow/Pillow/releases/tag/12.0.0
I'm SO happy with how my talk was produced for #PyLsdiesCon 2025! The intro by Ariane Djeaupang was fantastic and the slick animated title was awesome!
Have a watch here and learn how Python devs (yes, even backend devs) can help out with #accessibility on the web.
https://youtu.be/KrtUTEZzD6U?si=lmTtsCiK600UAAPy
#Wagtail #Django #Python
Django's fresh new task framework has a crontab scheduler now! 🎷 🐍 https://github.com/codingjoe/django-crontask
#django #python
Edited 337d ago
PyCon US 2026 Packaging Summit Recap https://lobste.rs/s/e06upg #python
https://discuss.python.org/t/packaging-summit-at-pycon-us-2026/106911/2
0.6.1 of django-new has been released. 🚀
- Lots of UI tweaks.
- `--data` flag for data-specific applications.
- Generate `summary.html` for a user-friendly explanation of the newly created application (h/t @ehmatthes).
- BETA: `--install` to install packages (only `whitenoise` currently supported).
🛠️ https://github.com/adamghill/django-new
📦 https://pypi.org/project/django-new/
Plastic Flowers to Protect the Hive https://lobste.rs/s/lwjndk #nodejs #python #security #vibecoding
https://phildini.dev/slopsquatting-for-good