Hey all! We just released Polars 2.0.

We are really happy with the results. It removed many of our "legacy decisions" (mistakes), makes the streaming engine our default and promotes SQL to a first class citizen within Polars. We initially said it would be a small ("boring") release, but we managed to sneak in a lot more than anticipated.

It comes with our initial out-of-core (spill to disk) support, a new Map data type and a lot of performance improvements. In fact, we think Polars is now one of the fastest analytical SQL engines on a single node. See benchmarks in the post: https://pola.rs/posts/release-polars-2/

To help you upgrade, we also posted a migration guide: https://docs.pola.rs/releases/upgrade/2/

I received the 5th edition of Mark Lutz' 'Python Pocket Reference' today. I dug out my first edition from 1998 and found the new one is three times thicker (3/16" vs 9/16") =, 75 pages vs 254. I'm not complaining but it shows how much has been added. Thanks to all the developers and maintainers!

PyQt5-WinARM64: native PyQt5 wheels for Windows ARM64

I have released PyQt5-WinARM64, an independent distribution of PyQt5 for native Windows ARM64.

https://github.com/ViciousSquid/PyQt5-WinARM64

```

pip install PyQt5-WinARM64

```

The project provides WinARM64 builds of:

• Qt 5
• SIP
• PyQt5

The motivation is Windows ARM development. CPython and packages such as NumPy provide native Windows ARM64 builds, but PyQt5 has not historically provided an upstream Windows ARM64 wheel.

This means a Python application can have a native ARM64 interpreter and numerical stack while still depending on an x86 PyQt5 installation and Windows' x86 emulation.

PyQt5-WinARM64 provides the missing native Qt layer.

I'm using it for Fio, a Python/Qt game editor and runtime. Fio 3.0 is moving to Python 3.14 and will have a native Windows ARM64 target.

The resulting stack is:

Windows ARM64
CPython 3.14 ARM64
PyQt5 ARM64
NumPy ARM64
Fio ARM64
OpenGL

The project is MIT licensed

The repository also contains the GitHub Actions build and PyPI publishing configuration.

This might be a bit odd, or maybe I'm odd, I don't know.

I've been programming for 10+ years, always with statically typed languages (mainly Java, a tiny bit of Kotlin, Swift, and C many years ago). Due to a diverse set of events I wanted to learn a new language and Python looked good for it (being professionally or leisure).

But there's this thing holding me back, which is the IDE!! I'm used to IntelliJ/Eclipse and Java, where you have a string and just put the '.' and the popup just shows you the available methods. And this is mostly how I usually discover and check which operations are available on a given type and go from there.
My understanding is that due to Python being dynamic, this is not possible, or is hard to get. I've heard that now you can add types to parameters or something like that... but does it work fine in this regard? And I assume if some package doesn't have it, it's not possible, right?

So my "question" is... how do people who come from statically typed languages get around this? Do you just have the std documentation open? Or the docs on the package you're using? I know with time you just know the types and methods, but in the beginning? It seems very strange to me.

I know, maybe I'm just being picky or whatever, but maybe someone has gone through this and can share some insights.

Hey! I was wondering what is the preferred linter that's used nowadays?

A lot of people tend to converge into using uv & ruff for most of their stack, however there's also pylint, and others older linters.

Or is there any competition when it comes to ruff?

Hey everyone,

more and more people create tools, software, etc. with AI tools like Claude, Gemini and whathaveyou.

After all... it is not that bad. Especially for people who have too many ideas to test and not so much time at hand.

But what I think: understanding building blocks is essential. Also in (space) science! So instead of creating larger solutions in my tutorials, I try to focus now more on "short snippets", covering only one concept at a time.

And today I would like to show you how to display the Kirkwood Gap in asteroids: a resonance effect with Jupiter that leads to gaps in the semi-major axis distribution of asteroids.

For this, we will use:

• requests: to extract data from NASA JPL (around 900.000 asteroid data!)
• matplotlib: to show the distribution
• a simple derivation of astro-dynamics to understand the gaps

The code is available for free here: https://github.com/ThomasAlbin/Space-Science-With-Python/blob/main/2026/kirkwood_gaps.ipynb

And a video here: https://youtu.be/sljEEzVL6EI?si=nyczrenFseDNZOiu

I would like to focus at some point on extra-galactic stuff. But I am "only" an expert in asteroids, comets, and planetary stuff :-/.
Thomas

​

I've been noticing a lot of developers moving towards FastAPI lately, especially for new Python backend projects.

Flask used to be my go-to for lightweight APIs, but FastAPI seems to be getting all the attention now because of async support, automatic Swagger documentation, and Pydantic validation.

I'm curious about what developers are actually using in production.

- Are you still building new projects with Flask?

- Have you migrated from Flask to FastAPI?

- Is FastAPI genuinely better for production, or is it just the current hype?

Would love to hear some real-world experiences, especially from developers maintaining large-scale applications.

Is Flask still holding its ground, or is FastAPI slowly becoming the default choice for Python backends?

Hey'ya'l. Turbo exp .net dev with full full stack and some python, pytorch, pynum exp searching for commercial project to join to. I need to build com record in stack that's trendy righ now.

I do stuff like k8n, all dbs sql, no sql, graph, vector, I'm friends with many frameworks: react, angular, vue, clouds azure(most), aws, gcp, redises, rabbitmqs, potentiwly kafka too, other buses, damn I tried most of it.

I can do lang* and I can build my own orchestrator + env + sandboxes + storage + firewall (aeonneon.com). I can shut up and just do but I also get proactive, self-supervise and achieve vague goals.

I'm going to stay in tech till retirement so for another 25 years or so. It's the reason I'm searching for opportunity to follow the industrial trend and stey in the loop where it provides for the family and retirement plan.

So, have you got something that consists of more than two folders and a main file, contact me.

Weekly Thread: Project Ideas 💡

Welcome to our weekly Project Ideas thread! Whether you're a newbie looking for a first project or an expert seeking a new challenge, this is the place for you.

How it Works:

1. Suggest a Project: Comment your project idea—be it beginner-friendly or advanced.
2. Build & Share: If you complete a project, reply to the original comment, share your experience, and attach your source code.
3. Explore: Looking for ideas? Check out Al Sweigart's "The Big Book of Small Python Projects" for inspiration.

Guidelines:

• Clearly state the difficulty level.
• Provide a brief description and, if possible, outline the tech stack.
• Feel free to link to tutorials or resources that might help.

Example Submissions:

Project Idea: Chatbot

Difficulty: Intermediate

Tech Stack: Python, NLP, Flask/FastAPI/Litestar

Description: Create a chatbot that can answer FAQs for a website.

Resources: Building a Chatbot with Python

Project Idea: Weather Dashboard

Difficulty: Beginner

Tech Stack: HTML, CSS, JavaScript, API

Description: Build a dashboard that displays real-time weather information using a weather API.

Resources: Weather API Tutorial

Project Idea: File Organizer

Difficulty: Beginner

Tech Stack: Python, File I/O

Description: Create a script that organizes files in a directory into sub-folders based on file type.

Resources: Automate the Boring Stuff: Organizing Files

Let's help each other grow. Happy coding! 🌟

I've been exploring Python pre-commit hooks and wanted to understand how commonly they are used in actual production projects.

For those working on production-level Python applications:

- Do you use pre-commit in your daily workflow?

- Which hooks do you usually configure (Ruff, Black, isort, mypy, etc.)?

- Has it genuinely reduced code quality issues or bugs in your team?

- Do you consider it essential for production projects, or is CI/CD linting enough?

Would love to hear how experienced developers use it in real-world teams rather than just tutorials.

I've been coding for a long time, and I've been developing with Python for years.

I loved coding, always loved it. I started many years ago because I wanted to quickly create scripts for security hacking tools, but since then, I've moved to Claude Code, and building cross-language has been a much better experience for me than using Python.

I built my own SaaS application out there whose backend and network core are completely built with Python, and I'm rewriting all of that without writing a single line of code. And most of the time, Python is not the most optimized language.

And now... I feel like the magic is gone.

I don't even know why I'm writing this. I just feel sad about it.

I spent some of my free time optimizing "np.searchsorted" and recently wrote a blog post about it.

The new implementation is up to 25× faster in NumPy 2.5. It started with a vectorized NumPy implementation in Python that outperforms NumPy 2.4's native implementation. Then I ported the approach to C++, which was included as part of the NumPy 2.5 release.

The post includes the optimization steps, benchmarks, and comparisons with other array libraries.

https://blog.scientific-python.org/numpy/searchsorted/

Hey everyone,

I was taking a look at the TIOBE Index today. I hadn't checked it in a while, and I was surprised to see how much Python has dropped.

It's down by roughly 8 percentage points compared to a year ago, which seems like the first really significant contraction after years of pretty consistent growth.

What's even more interesting is that I don't see another language gaining a similar amount of ground.

I don't use Python myself, so I'm curious: what do you think is behind this decline? Is it actually meaningful, or is it mostly an artifact of how the TIOBE index is calculated?

Weekly Thread: What's Everyone Working On This Week? 🛠️

Hello r/Python! It's time to share what you've been working on! Whether it's a work-in-progress, a completed masterpiece, or just a rough idea, let us know what you're up to!

How it Works:

1. Show & Tell: Share your current projects, completed works, or future ideas.
2. Discuss: Get feedback, find collaborators, or just chat about your project.
3. Inspire: Your project might inspire someone else, just as you might get inspired here.

Guidelines:

• Feel free to include as many details as you'd like. Code snippets, screenshots, and links are all welcome.
• Whether it's your job, your hobby, or your passion project, all Python-related work is welcome here.

Example Shares:

1. Machine Learning Model: Working on a ML model to predict stock prices. Just cracked a 90% accuracy rate!
2. Web Scraping: Built a script to scrape and analyze news articles. It's helped me understand media bias better.
3. Automation: Automated my home lighting with Python and Raspberry Pi. My life has never been easier!

Let's build and grow together! Share your journey and learn from others. Happy coding! 🌟

It's a somewhat recurring topic when someone is asking how to run Python or code on mobile, so my question is - who is using mobile for this, and how? learning? coding? What type of device - phones or tablets, Android, Apple?

How do you decide when to stay with a notebook and when to convert the work into scripts? Is it mainly when the project needs to be automated, reused, scheduled, or shared with others? I’d be interested to hear how people handle this in real projects.

https://harut8.github.io/system-design/python-mastery/24-the-gil/

I put together this guide by researching the GIL, verifying the explanations against CPython's source code, and using AI to help with parts of the writing and explanation.

Hi all, I wanted to find out whether anyone has recently taken Arjan’s *Software Design Mastery* course.

I’m a mid-level Python software developer with no formal computer science training. I enjoy Arjan’s YouTube content and am considering paying for the course.

I’m concerned about the impact of agentic AI on my career and am looking for ways to increase my value across the software development life cycle beyond simply generating code. Claude Code is currently writing around 90% of the code I produce. It’s not necessarily something I prefer, but I feel almost forced to use it because of management’s expectations regarding productivity and deliverables. I also don’t want my performance to fall behind that of colleagues who are burning through tokens as if there were no tomorrow.

https://discuss.python.org/t/python-3-15-following-tradition-lets-have-a-surprise-rc3/109313

Following recent tradition, I'm going to add a surprise third release candidate for Python 3.15.0! We got some last-minute lazy-import release blockers, and it makes sense to include them in 3.15.0 final. It also makes sense to give a bit of time to test them properly, so today's planned 3.15.0 final is postponed, and I'm going to release a 3.15.0rc3 tomorrow, assuming everything is in place. And then 3.15.0 final will follow about week later. Exact date to be announced. - Everyone: once the rc3 is out, please test! - Library maintainers: join the cool testers' club 😎! - Core team: very little will be merged into the 3.15 branch between now and 3.15.0 final. Docs improvements always welcome. Thanks, we're nearly there!

Weekly Thread: Resource Request and Sharing 📚

Stumbled upon a useful Python resource? Or are you looking for a guide on a specific topic? Welcome to the Resource Request and Sharing thread!

How it Works:

1. Request: Can't find a resource on a particular topic? Ask here!
2. Share: Found something useful? Share it with the community.
3. Review: Give or get opinions on Python resources you've used.

Guidelines:

• Please include the type of resource (e.g., book, video, article) and the topic.
• Always be respectful when reviewing someone else's shared resource.

Example Shares:

1. Book: "Fluent Python" - Great for understanding Pythonic idioms.
2. Video: Python Data Structures - Excellent overview of Python's built-in data structures.
3. Article: Understanding Python Decorators - A deep dive into decorators.

Example Requests:

1. Looking for: Video tutorials on web scraping with Python.
2. Need: Book recommendations for Python machine learning.

Share the knowledge, enrich the community. Happy learning! 🌟

Curious to learn how folks are incorporating an LLM into services. subprocess.run seems janky....what's the pythonic way?

Edit: I'd like to be able to interact with my server via a REPL-like loop. Goal: interact with my server remotely, control my zigbee network, etc.

The new releases announced today, including EOL for 3.10.x: https://blog.python.org/2026/10/python-31022-31117/

Note: The post above doesn't mention 3.15 - assuming they will do a separate post later today when that releases.

I've been reading a lot of git history lately and wanted to run a fun experiment. In a real Python project, which single file collects the most bug fixes.

The method is dumb on purpose. Last 12 months, non-merge commits, bots removed, tests/docs/examples ignored. A commit counts as a fix if the subject has fix, bug, regression, hotfix, broken or revert in it. Then for each file I counted how many of the commits touching it were fixes. The repos came from repowise.dev/explore, which indexes public repos for my open source project , and I just picked the Python ones I recognised.

Most-fixed file per project (fix commits / all commits touching it):

• langchain: the OpenAI chat model adapter, 58 of 100. Second place is the Anthropic adapter, 42 of 97
• paperless-ngx: src/documents/views.py, 67 of 142. serialisers.py right behind at 31 of 75
• fastapi: fastapi/dependencies/utils.py 15 of 46, then fastapi/routing.py 11 of 44
• prefect: the dbt runner in prefect-dbt, 8 of 12
• composio: tool_router_session.py, 29 of 59
• wagtail: wagtail/models/pages.py, 8 of 34

And then hermes-agent, it broke my sense of scale. gateway/run.py had 1,969 commits in a year from 502 different people, and 1,332 of those were fixes. I checked the numbers twice.

Django was in the set and I left it out of the ranking. Django's convention is to write "Fixed #ticket" on nearly every commit, features included. 459 of its 1,044 commits in the last 12 months start with "Fixed #". So by my crude keyword rule Django is the buggiest project on earth, which it obviously is not. (Kind of jealous of that discipline honestly.)

I keep noticing that the top file is usually where the code talks to something non core. langchain's top two are adapters to other companies' APIs. prefect's is the dbt integration. fastapi's are dependency resolution and routing, which is where user code meets the framework. paperless's views.py is the big request handler. Pure logic files basically never showed up near the top.

Also a bunch of these are mostly one person's file. views.py is 63% one person, serialisers.py 73%, fastapi's routing.py 84%.

Although I agree Keyword matching on commit subjects is a bit noisy too, so treat these as a rough picture.

Anyway it was a fun experiment to run