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 heard about this online course for computational neuroscience, but i forgot the name of the website/app (I think it's a website, dik). Would any of the Redditors know more about the course or the website, which is a verified, full place to learn the field efficiently?
Oh!! It would be nice if the platform was free or cheap, cause I am broke

​

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?

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.

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?

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.

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/

Post all of your code/projects/showcases/AI slop here.

Recycles once a month.

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?

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! 🌟

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.

What are some common Python developer interview questions for someone with 3 years of experience?

I've been asked to hire Python developers and since I'm the only one with any kind of programming experience, I was asked. This will be a quick first round interview over zoom for 15-30 mins. For the second-round we will bring in outside consulting for more in-depth evaluation. Thanks in advance.

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

Weekly Thread: Meta Discussions and Free Talk Friday 🎙️

Welcome to Free Talk Friday on /r/Python! This is the place to discuss the r/Python community (meta discussions), Python news, projects, or anything else Python-related!

How it Works:

1. Open Mic: Share your thoughts, questions, or anything you'd like related to Python or the community.
2. Community Pulse: Discuss what you feel is working well or what could be improved in the /r/python community.
3. News & Updates: Keep up-to-date with the latest in Python and share any news you find interesting.

Guidelines:

• All topics should be related to Python or the /r/python community.
• Be respectful and follow Reddit's Code of Conduct.

Example Topics:

1. New Python Release: What do you think about the new features in Python 3.11?
2. Community Events: Any Python meetups or webinars coming up?
3. Learning Resources: Found a great Python tutorial? Share it here!
4. Job Market: How has Python impacted your career?
5. Hot Takes: Got a controversial Python opinion? Let's hear it!
6. Community Ideas: Something you'd like to see us do? tell us.

Let's keep the conversation going. Happy discussing! 🌟