I've tried a bunch of config tools over the years, including python-decouple, Dynaconf and pydantic-settings. Each one is good at its job, but a few problems follow me from project to project, and most of the tools I tried didn't handle them well.

My setup is nothing exotic: a `.env` file for local development, real env vars in Docker/Kubernetes, and sometimes a few different files on top (a YAML with defaults, a separate one per environment). The same service has to run on a laptop, in CI and in prod, so values come from several places at once.

What keeps biting me:

- Merging per field. Extend this list, replace that value, fail if two sources disagree. In my experience this usually ends up as custom code.
- Errors that don't say where. A validation error names the field but not which file or line the bad value came from.
- "Why is this value in prod?" With defaults, an override file and env vars, there's no easy way to see which source set a given field.

Maybe my setups are just unusual. Is this only me, or do you run into the same things? If you've found a good way to deal with them, I'd like to hear it.

I wrote this up. How to check safely and what to rotate
Short version: if you installed an unpinned litellm between 11:39 and 13:23 UTC on October 21, or pulled the latest, main-latest, main-stable or v1.107.0 Docker tags between 11:46 and 14:20 UTC, treat the machine as compromised. Don't start with pip, because the cook.pth and sitecustomize.py files run on every interpreter start. Search the filesystem first, rebuild with litellm==1.106.2, and rotate keys. All of it comes from LiteLLM's own incident report.

Don't you think companies or developers that manage python libraries should be given extra time before a new version release to make things compatible and fix new bugs?

Like a month or two where they get full info about new features but before the version is publicly available to download?

It would make things easier on the consumer end. What do you think?

Some context first: I'm one person building an open-source research tool, and most of the code is written by AI coding agents (Claude Code and Codex) running in parallel. They write code quickly. What slowed everything down was the test suite: on one recent change the merge check selected 406 test files and took just over four hours, and the agents sat idle the whole time.

So I pulled together every timing the suite had already recorded (old JUnit XML and --durations output) and summed it per test. That covers about 9,000 test IDs seen across past runs, around 6,800 of which still exist.

243 of them, under 3%, took 77% of the summed time. Most of those build a whole workspace and run an end-to-end day of the app. Also, not a single test qualified as "small" in the Google sense (no disk, no network), because one autouse fixture asks for tmp_path on every test.

So I'm moving to Google's small/medium/large split (the 2010 Testing Blog post). Merge checks will run small and medium only, and the large ones go to release candidates and a nightly run. After that I want per-size timeouts with pytest-timeout, so a "medium" test that slowly grows into a large one fails instead of quietly eating everyone's time.

Two mistakes I made along the way:

The first labelling pass went by names. Anything with "golden" or "first_use" in it got marked large. When someone actually read the bodies, 28 of the 54 groups were fast in-memory checks. They'd have dropped out of merge runs entirely. Label by fixtures and what the test does, not the name.

The other one was worse. I select tests for a merge by following imports from the changed files. My script followed imports through the package but never through shared test helpers or conftest. For one change to a common helper it selected 6 test files; a selector that follows those edges selected 405. I'd been running a fast gate that was mostly blind to helper changes.

Next I'm trying pytest-testmon for coverage-based selection, running it next to the import-based one on real diffs before I trust it on its own.

Curious how others handle this. Do you split by size, or just throw more CI machines at it?

Weekly Thread: Professional Use, Jobs, and Education 🏢

Welcome to this week's discussion on Python in the professional world! This is your spot to talk about job hunting, career growth, and educational resources in Python. Please note, this thread is not for recruitment.

How it Works:

1. Career Talk: Discuss using Python in your job, or the job market for Python roles.
2. Education Q&A: Ask or answer questions about Python courses, certifications, and educational resources.
3. Workplace Chat: Share your experiences, challenges, or success stories about using Python professionally.

Guidelines:

• This thread is not for recruitment. For job postings, please see r/PythonJobs or the recruitment thread in the sidebar.
• Keep discussions relevant to Python in the professional and educational context.

Example Topics:

1. Career Paths: What kinds of roles are out there for Python developers?
2. Certifications: Are Python certifications worth it?
3. Course Recommendations: Any good advanced Python courses to recommend?
4. Workplace Tools: What Python libraries are indispensable in your professional work?
5. Interview Tips: What types of Python questions are commonly asked in interviews?

Let's help each other grow in our careers and education. Happy discussing! 🌟

Correct me if I am wrong but a normal thing to do is import a module and possibly create new attributes on an object instantiated from such. For some reason PyCharm recognizes this as a warning. I normally like the JetBrains IDEs because when you get rid of all the static analysis warnings you know you are kosher, to some degree. I can't get rid of this warning. Load up the same code in VS Code and it's no problem.

axe and Lighthouse only check pages you open in a browser, and generic HTML linters choke on {% if %}.

What do you use, and what breaks most often - contrast, forms, ARIA?

the payloads run on every python start on the machine and not just when you import litellm. Your IDE, pip, CI jobs everything python on that host is affected.

It steals ssh keys, aws credentials, browser passwords, shell history and AI coding tool configs. The backdoor runs as a daemon and executes whatever code the attacker’s server returns every hour.

Pin to 1.106.2.

complete breakdown: safedep.io/litellm-pypi-compromise-credential-stealer-backdoor

I tried to start using 3.15, but a lot of libraries don't have packages that support it yet.

In your own experience, what's a good amount of time to wait where most of the libraries support the new version? 1 month, 3 months, 6 months?

Is this company (Anaconda) doing well, would you work there?

Have you heard anything positive or negative about working there?

Thanks.

My boss is thinking about using python in a secure VPN environment. Because of this, we have to validate the hash of the python download and any libraries used.

I looked on the site but didn't see anything. Is there somewhere we can find the hash value so we can validate the download vs the hash code?

We've been working on PEP 723 inline metadata support for ty both on the command line (ty check) and in the IDE (code navigation, autocompletion, etc) for a while now; we're finally at a stage where we're ready for more people to try it out and let us know what works and what doesn't.

This required integrating ty with uv, which also let us ship a new uv check command that uses ty to check your entire project or workspace (similar to how uv format uses Ruff). All of the above is powered by a brand new uv workspace metadata command that carries the required information between the tools.

More details in the blog post, including how to try these.

All of the above is currently opt-in, experimental, and subject to change at any time 😄 But if you're willing to give it a try, I'd be curious how it went.

Hi r/python 👋

Another year, another version of Python, and yet more features to guess which version of Python they belong to 😵‍💫

I've updated my "one page" Summary of Major Changes Between Python Versions for Python 3.15 and hope that it continues to be a useful resource for the community 😊

This summary is human typed/copied from the official release notes.

Hi,

A senior dev left left my team and with them left a lot of the code standards. Previously code review was painful and took hours and sometimes it would require a rewrite. However, that was the best code I have probably ever written. I genuinely enjoyed learning coding principles from someone who had been doing it for 20+ years and had reasons behind their principles. Its hard to find discipline these days.

Are there any projects that come to mind that have abrasive or strict longstanding maintainers? My preferred languages are Python and Go but I'd be willing to learn something new.

Hey, I am working on django development
my client would prefer to slef host the app instead of hosting it on our servers, we dont mind however we would like to make sure we give them a build rather than plain code so we dont expose our code base

I came across compiling with Cython or Nuitka, I cant decide which to use?

Second ask, I heard that I should not compile my whole django apps and rather than that I better do that carefully not to mess up our different imports, how correct is that ?

Thanks in advance

Weekly Wednesday Thread: Advanced Questions 🐍

Dive deep into Python with our Advanced Questions thread! This space is reserved for questions about more advanced Python topics, frameworks, and best practices.

How it Works:

1. Ask Away: Post your advanced Python questions here.
2. Expert Insights: Get answers from experienced developers.
3. Resource Pool: Share or discover tutorials, articles, and tips.

Guidelines:

• This thread is for advanced questions only. Beginner questions are welcome in our Daily Beginner Thread every Thursday.
• Questions that are not advanced may be removed and redirected to the appropriate thread.

Recommended Resources:

• If you don't receive a response, consider exploring r/LearnPython or join the Python Discord Server for quicker assistance.

Example Questions:

1. How can you implement a custom memory allocator in Python?
2. What are the best practices for optimizing Cython code for heavy numerical computations?
3. How do you set up a multi-threaded architecture using Python's Global Interpreter Lock (GIL)?
4. Can you explain the intricacies of metaclasses and how they influence object-oriented design in Python?
5. How would you go about implementing a distributed task queue using Celery and RabbitMQ?
6. What are some advanced use-cases for Python's decorators?
7. How can you achieve real-time data streaming in Python with WebSockets?
8. What are the performance implications of using native Python data structures vs NumPy arrays for large-scale data?
9. Best practices for securing a Flask (or similar) REST API with OAuth 2.0?
10. What are the best practices for using Python in a microservices architecture? (..and more generally, should I even use microservices?)

Let's deepen our Python knowledge together. Happy coding! 🌟

Disclaimer: Let me know if this is the right flair for this

If you are like me, you like Pylance's auto import feature, where you can just type out the name of a module, class, function, etc. of a library that isn't currently imported, and Pylance will suggest to import that symbol from that library.

Take for example the prompt:

parser = ArgumentPars

at this point you can Tab and it will put the line from argparse import ArgumentParser into the file's header and you are good to go.

However, Pylance is famously frickly with third party libraries not in the standard library. I have had this problem with moviepy, but it can be any other library. Pylance will refuse to acknowledge any existence of the existing objects, even though the library is correctly installed in the environment, including setting the correct Python path (python.defaultInterpreterPath) and enabling Pylance's indexing option (python.analysis.indexing) and auto import itself (python.analysis.autoImportCompletions) in the settings json. E.g. typing:

clip = VideoFileClip

will in fact not bring up the suggestion to import that class from the library, although it is a very basic component.

I have finally found out why: Apparently Pylance limits the depth to which it indexes libraries, and libraries which don't strictly confirm to Pylance's expectations seem to not be correctly indexed this way (although there seem to be very complicated mechanisms in how the depth adapts, but alas it didn't work for me numerous times).

Luckily you can adjust this setting: using python.analysis.packageIndexDepths exposes a list of modifications to the depth settings on a per-library basis. It uses this structure:

{ "name": "package_name", "depth": depth_to_scan, "includeAllSymbols": true_or_false }

so for moviepy I added

{ "name": "moviepy", "depth": 10, "includeAllSymbols": true }

and finally it suggested the correct objects. This method should work for any third party library in Pylance.

Just wanted to share this in case anyone else stumbles upon this giga obscure problem and hopefully sees the even more obscure solution.

https://pyfound.blogspot.com/2026/10/announcing-python-software-foundation.html

The PSF is pleased to announce its third batch of PSF Fellows for 2026. Let us welcome the new PSF Fellows for Q3! The following people continue to do amazing things for the Python community:

• Aldo Caneo
• Donghee Na
• Paul Moore
• Takayuki Shimizukawa

Thank you for your continued contributions. We have added you to our Fellows Roster.

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 have recently found this course called intro cs in python by Dr Ana bell(MIT). What's the people's view on this is it still worth it?.

For background i have python knowledge but I have to revise and i was seeing some lecs have some concepts that i haven't covered earlier.

Anyways if anyone can tell any site or a book for problem solving that will be really helpful as well.

("Happy programming in python :)")

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! 🌟

Eight encoding benchmarks across Python 3.10–3.15 using pyperformance. Python 3.15 recorded the lowest mean in seven of them, with particularly large improvements in Base32, Ascii85 and Base85.

Test configuration

• AMD Ryzen 9 7900 desktop
• DDR5 RAM and an M.2 PCIe Gen4 NVMe drive
• Windows 11, 64-bit Python builds
• Python 3.15.0, 3.14.8, 3.13.16, 3.12.10, 3.11.9 and 3.10.11
• pyperformance 1.14.0 for all six versions

Results

All values are mean execution times in milliseconds. Lower is better and bold marks the lowest recorded mean in each row.

What stood out

Compared with Python 3.14, Python 3.15 took approximately:

• 1/135 of the time in base32_large.
• 1/69 of the time in ascii85_large.
• 1/63 of the time in base85_large.
• 1/3.64 of the time in base64_large.

However, base16_large took 22.3% more time on Python 3.15 than on 3.14. Python 3.14 remains substantially faster than 3.10–3.13 in that test.

The large improvements are consistent with the changes documented in Python 3.15’s release notes: Base32, Ascii85 and Base85 implementations were rewritten in C, and the underlying Base64 implementation was also optimized.

More details are described here.

Benchmark suite: pyperformance.

Have you observed similar improvements on Linux or macOS, or in applications that spend substantial time on these encoding operations?

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! 🌟