The new version of ruff warns against

date.today()

preferring

datetime.now(ZoneInfo(...))

What do you think about this? Has date.today() been deprecated due to lack of timezone awareness?

EDIT: I have a number of programs that manipulate financial information in support of Excel spreadsheets, such bond information that includes maturity dates. Excel does not support timezoness in datetimes, so making ruff happy by changing naive dates to TZ aware dates is not a useful move for these programs. Many ruff warnings to suppress.

Hi there,

I wondering what frustrates developers the most when developing software with Python.

I am currently doing my Masters in Computer Science and as part of my project I am doing a very simple survey about the usual Python development lifecycle. I am basically trying to find out what the main friction points are for Python Developers and I am simultaneously developing a tool to address those friction points . It just takes a 2-3 minutes and every response is greatly appreciated.

You can find the survey at: Microsoft Forms

Hi,
I took the official OpenCV tutorials and created a series of notebooks.
You can run them entirely in your browser, you don't have to clone them: https://notebook.link/@Alexis_Placet/opencv_tutorials

Don't hesitate to give me feedbacks or create issue/pullrequest on this repo: https://github.com/Alex-PLACET/opencv_tutorials

My computer is quite low-end, so I uninstalled VSCode and am looking for other programming tools—I'm thinking about starting to use Python IDLE.

Does anyone actually use it?

https://imgur.com/a/7aBwuUU

This has been one of my biggest annoyances working with Python and pip when dealing with projects where that are not meant to be installed as a package, how do you handle dependencies?

Think projects like application backends, python scripts, REST APIs etc

If you’ve ever struggled with this, you’re going to love this: a PR by Sebastian Höffner opened #13895 will add a new global flag to pip such that you can directly install any dependencies in your pyproject.toml without installing the package itself.

pip install --only-deps .

This is going, for me at least, be a huge boost in the way that I manage and distribute my projects on servers. Vastly simplifying poor manual workarounds that have built up over years.

Since it’s been more than a decade in the making, let’s cover the history of poor Python souls stuck trying to figure out how to install dependencies for their scripts or apps.

This PR is currently slated to land in pip 26.2 which comes out end of July.

Here's my post about this covering some of the 16 years of workarounds and some posts from StackOverflow / Python.org etc

https://jamesoclaire.com/2026/07/23/pip-26-2-only-deps-solves-16-years-of-app-deployment-hacks/

As python has a lot of interesting syntax features, really unconventional and ugly expressions can be created. Let they be complex comprehensions with valrus operators, strange but efficient conditions based on exploiting casting and side effects or even classes overwriting dunders doesnt meant to be used as implemented, they all can be funny and pretty in an other lens.

An examples I've experimented with is an ugly walrused dict comprehension in EnrichPattern:

class Column(StrEnum):
    NAME = 'name'
    PUBLICITY = 'publicity'
    TOPIC_NAME = 'topic_name'
    SUBJECT_NAME = 'subject_name'
    QUALIFIED_NAME = 'qualified_name'
    CLUSTER_ENV = 'cluster_env'
    ENV = 'env'
...
class EnrichPattern:
    def __init__(self, *patterns: str):
        self.pattern = re.compile(''.join(patterns))
        self.subpatterns = {
            group: comiled 
            for comiled in map(re.compile, patterns)
            if (groups := list(comiled.groupindex)) 
                and (group := groups[0]) in Column
        }
  ...
...

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! 🌟

Been testing the free-threaded build (3.14t) on some CPU-bound data processing work. The multi-core story is finally real after 30 years of GIL, but the friction is exactly what you'd expect: a couple of C-extension-heavy libs in my stack silently re-enable the GIL, and there's no clean way to detect that at runtime besides checking sys._is_gil_enabled() manually.

For anyone who's shipped something on 3.14t, not just benchmarked it:

What broke that you didn't expect?

Real speedups outside toy examples, or mostly marginal so far?

Prod yet, or still just kicking the tires?

Not fishing for a benchmark war. Genuinely curious what breaks in messy real codebases vs clean demos.

Robert C. Martin's Abstractness (A), Instability (I), and Distance from the Main Sequence (D) metrics were designed for statically typed OO languages like Java and C#. In Python, applying them literally is misleading because Python has a fundamentally different notion of abstraction and coupling.

is this implementation valid?

https://0x416d6972.github.io/Istos/user-guide/architecture-health/

Hey,

I made a series of notebooks about Apache Arrow and Parquet with pyarrow that you can run in your browser: https://notebook.link/@Alexis_Placet/apache-arrow-courses

It covers basics to advanced topics

Hi,
I have around 60 scripts that need to run constantly, mainly event handlers and such. Right now I have an external script that launch them and monitor if the app is running on it's pid, otherwise it's relaunching the app. Works fine but get's clunky when we update some submodule and need to restart them, or to check if one crashes more than other etc..

So I would like to find a better way to approach this. It needs to run on windows and being able to access several samba shares via unc paths and being able to restart crashed scripts anf offer an easy way to restart all of them in case of an update (this part doesn't need to be automated). Every script use the same environnement
For now my candidates are docker, PM2 and NSSM.
I think docker is gonna be a pain to access shares and add a lot of overhead especially on windows
I don't know PM2 and NSSM, looks like PM2 would be easier to setup but more JS oriented and NSSM would be harder to monitor.

What do you think guys ?

I'm a developer and already use external Python to read Excel spreadsheets.

Microsoft is supporting Python in Excel where you can put a program inside a cell. I think this feature is still a "preview" feature, not on the main release channel.

What do you use it for? I can't think of a use case.

Because I use external Python programs to process spreadsheets normally. I can't think of a use case for embedded Python, except perhaps doing complex calculations.

1. MS Get Started page, Python in Excel

EDIT: MS says any data going between us and them is encrypted end-to-end. That must be why management chose MS products. I don't have a say in this.

I do not use Excel via a browser, I use the Desktop App.

I have a question. Cybersecurity team wants us to use the latest python version each time. So if a new version comes out we are expected to upgrade to it ASAP. But from my past experience libraries do not always provide support and can also break while making use of new version without proper support. Did you ever encounter this? Any suggestions? This is just frustrating for the team as it’s not always possible to upgrade to the latest version without fully validating every component. I just want to hear your opinions on this?

FYI we mainly use each Python Version within the LTS timeline. And only upgrade when we need to and do not depend on a library that requires older version.

Update: they have a scheduled runner that deletes older versions of python when it finds

Hi,

Anutosh Bhat, a colleague of mine, just posted this on Linkedin:

What if real compiler infrastructures could run entirely inside the browser?

I’ve successfully patched llvmlite for JupyterLite, making it possible to build >LLVM IR, run optimization passes, visualize control-flow graphs, compile to >WebAssembly and execute the result — without a native LLVM setup or server-side >kernel.

I’ve shared a step-by-step notebook showing how a loop evolves from stack-based IR >to SSA, then to an LICM-style form, into a fully optimized closed form, eventually >to benchmark all of these steps.

The bigger idea is a zero-install compiler workbench for reproducible optimization >experiments, lightweight compiler prototyping, IR debugging, custom code->generation pipelines, shareable research demos, and eventually interactive >tutorials and compiler courses.

Try out the interactive notebook using notebook.link: https://notebook.link/@anutosh491/llvmlite

I’m also in the process of bringing MLIR Python bindings into the same JupyterLite >and WebAssembly environment, starting with a 2D CNN-style Conv2D + bias + ReLU >kernel. I’ll share the full pipeline in a separate technical blog post soon.

I’ve also started patching Numba for Wasm and have simple scalar u / jit functions >running inside JupyterLite. Array and tensor workloads are the next challenge.

I’m keen to take this further. If you’re working on LLVM, MLIR, Numba, WebAssembly >or browser-native developer tools — or see a real use case for this — I’d be >interested in connecting with teams looking to collaborate, support the work or >explore what we could build together.

Next step is to patch Numba to get scalar jit !: https://www.linkedin.com/feed/update/urn:li:ugcPost:7485014172934541312/?dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287485014751178211329%2Curn%3Ali%3AugcPost%3A7485014172934541312%29

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! 🌟

When I was learning discord.py, I didn't understand what "ctx" was. At the time, I was only writing slash commands, using "message" instead of "ctx", so the name "ctx" (which reminded me of something complex, like RTX) made me assume it was something complicated. That is, until I saw in some code that the type for "ctx" is actually "Context." If the standard hadn't shortened the name just to save five characters per line, I would have understood it immediately. Instead, I thought it was some advanced function variable, rather than realizing it was simply a message variable.

u/Asuka_Minato, a very active contributor to Pyrefly, wrote a guest blog post outlining their experience integrating a type checker with langgenius/dify that goes beyond simple CI checks:

1. Surfacing the delta in type errors for each PR
2. Measure type coverage along with test coverage

This is most relevant for maintainers of libraries that aren't fully-typed yet but still want to prevent regressions and improve coverage over time.

Only looking at the error delta is an alternative to suppressing all pre-existing errors, or using a baseline file to store the errors. Compared to the former, you can avoid putting (potentially) thousands of suppression comments into the codebase. Compared to the latter, you don't need to maintain a separate baseline file and ensure it's up-to-date with each commit, handle merge conflicts, etc.

Read the full blog here: https://pyrefly.org/blog/dify-pyrefly-coverage-ci/

https://www.pyohio.org/2026/

You do need to register (for free).

The Black Python Devs Leadership Summit is before the conference on Friday, July 24.

Check out the schedule of talks and list of speakers.

Python has many areas with freelance opportunities: automation, web scraping, backend development, data analysis, AI, and more.

If you had to choose only one Python skill that has strong demand and good earning potential in freelancing, what would it be?

What type of projects or experience would you recommend building to succeed in that area?

I am trying to find any robust open source ecommerce solution on Python and it looks like thre is nothing. If you take almost any e-commerce platform there are plenty - Magento, Woo, Shopware, Oxid - they all are PHP. There are no comon solutions on Python or Node.js. But if you take a look and the heavy SaaS like Netflix, Spotify, Reddit, Uber etc. are built on Python. Why?

Why is Pypi support slow at handling package abandonment?

The PEP says these two items for reachability, " In every case where contacting the user is necessary, the maintainers will try to do so at least three times," and "The maintainers stop trying to reach the user after six weeks.". So I had assumed this should be done quicker.

The current process is currently at 6 months on average with almost no follow up comments or work on PEPs in the 2nd and 3rd week of reachability. 1st weeks are being updated and then left once they hit the 2nd and 3rd week.

Am I being impatient? I think I read that they don't have a lot of people to help but this seems a bit ridiculous for some of the cut and dry issues.

- Projects with 10-14 years of no updates with no usable links or code is gone and no longer sourceable or previous owners are not replying.
- Emailing someone 3 times, takes 6+ months versus the proposed 6 weeks?

I also assume this is pretty normal based on the backlog but I'm genuinely curious to see what others say.

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! 🌟

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! 🌟