Hey, I am looking for some GitHub repo to contribute this summer, if you have any projects related to Python, Backend (FastAPI), AI agent, then I will be happy to contribute in the long term.
I am specifically looking for an active less crowded repos.
Should i buy the course in 2026 . I already know python basics till oops . I saw the course structure from outside it looked good. Is it still revelent? Please drop your reviews and guide me
I had seen python mentioned in some unix books and some of my old Linux books. I thought it was an ancient language spoken only by high priests now departed. All of my projects (commercial embedded real-time) were in ansi C. The really old stuff was k&r. I'm assuming it executes i-code. I have gigs in my historic projects that I have reuse frequently now archived.
But is Python for me learning now?
These days, I can't open X anymore without seeing some supply chain attacks on PyPI or NPM. Things are really getting out of hand. One very simple yet effective approach to mitigate them is to use a dependency cooldown. That means that you don't install anything that's too new - e.g., every dependency needs to be at least a week old.
Why does this work? Because the community usually intercepts them in hours to days. Both uv and poetry support the definition of the cooldown period inside their config. pip is adding as support as well. I use 1 week to be on the safe side. They both support excluding a specific package from the rule so you can still apply critical fixes to dependencies ASAP.
I wrote about that and how to configure uv/poetry in my blog post: https://jangiacomelli.com/blog/mitigate-supply-chain-attacks-for-python-dependencies/
More about the dependency cooldown concept:
I don't know where to start and how for backend role in python. I am stucked in watching youtube and then thinking is this right path i should follow or I have to move another things to learn first. Suggest some things
Suggestion:
We should call python 3.14 pithon.
For those who don't understand.
Pi (the math thing) is 3.14
I don't have a formal degree in IT, but I've been diving deep into fields like RPA, AI agents, LLM fine-tuning, and Machine Learning. According to reports (like UiPath's), Python is pretty much the backbone for all of this.
If you were looking to land a Python Developer role starting from scratch today, would you prioritize certifications like the Python Institute’s PCAP, or would you take a different route?
I’d love to hear your personal stories and what worked for you!
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.
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
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
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’m trying to use the clump_find function in yt on my Parthenon .rhdf file, but it crashes immediately when it tries to generate the first contours. Has anyone had this problem? If so, how did you fix it?
To get around it, I regridded the data and it works, but I lose resolution.
Hi guys hope everyone’s having a wonderful weekend.
Just wanted to ask the experts what’s the best and absolutely free website to train for Coding assessment interview on Python
I have an interview coming up!! ;)
In Python, the actual app construction usually feels pretty straightforward. The hard part starts when you try to turn it into something real people can actually use.
Things like packaging, updates, installers, compatibility issues, access control, managing different systems etc. take a lot longer than I thought they would.
At first, I thought the hard part was finishing the code. But now it feels like everything afterwards is a whole new ball game.
Curious if anyone else who builds Python apps has felt the same.
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!
Let's build and grow together! Share your journey and learn from others. Happy coding! 🌟
Hej,
I've been working on a language called hica and Python has been one of its inspirations, especially working with lists. I have created a Hica vs. Python comparison at https://cladam.github.io/hica/docs/hica-vs-python and the verdict is ofc Python 🙂
From my conclusion:
>Python is the safe, proven choice with the largest ecosystem and lowest barrier to entry.
>hica emphasises foundations like immutability, type safety, pattern matching, and explicit error handling. Students who learn hica carry these patterns into Python, Rust, TypeScript, or whatever they use next.
>Why not both? Start with hica to build the foundations, then move to Python with a head start on the concepts that matter most.
What does this community think? My Python is a bit rusty, any feedback is welcome.
I needed to send ~80 personalized emails to a list of GitHub users a few weeks ago. Different name in each one, different repo reference, written like a person wrote them. The kind of thing you do when you're cold-reaching for a side project.
Every guide I found told me to sign up for SendGrid, Postmark or Resend. Verify a domain, set up DKIM, get on a free tier, hope you don't trip a spam classifier. Half a day of setup for something I needed to do once.
Then I remembered Gmail does this natively. Google has been running an SMTP server at smtp.gmail.com for twenty years and any language with a sockets library can talk to it. The only thing standing between you and sending email from your own Gmail is one settings page most people never visit.
Here's the whole thing.
A Gmail account with 2-Step Verification turned on. If you don't have 2FA on, go to myaccount.google.com/security and switch it on first, otherwise the next step doesn't exist.
Then go to myaccount.google.com/apppasswords and generate a new app password. Google shows you the 16-character string once, looks like abcd efgh ijkl mnop. Copy it immediately. The spaces are optional, Gmail accepts it either way. Treat this like a password — don't commit it, don't paste it in chat, don't put it in a Notion doc your team can read. Google's scanners catch app passwords leaked in public repos and auto-revoke them, but the lag is unspecified and you really don't want to find out the hard way.
That's it for setup. Now you can send email.
Ten lines of Python. No libraries beyond the standard library.
python
import smtplib
from email.message import EmailMessage
msg = EmailMessage()
msg["From"] = "you@gmail.com"
msg["To"] = "recipient@example.com"
msg["Subject"] = "hello"
msg.set_content("Body goes here.")
with smtplib.SMTP("smtp.gmail.com", 587) as smtp:
smtp.starttls()
smtp.login("you@gmail.com", "abcd efgh ijkl mnop")
smtp.send_message(msg)
Run it. An email leaves your Gmail and shows up in the recipient's inbox a few seconds later, looking exactly like one you typed by hand. That's the whole protocol. Everything else is wrapping that in a workflow.
If port 587 is blocked on your network (corporate Wi-Fi, some hotels), switch to port 465 with smtplib.SMTP_SSL instead of STARTTLS. Same protocol, different transport, one line change.
For real outreach you need three files: a .env for the Gmail address and app password, a recipients.csv with name and email columns, and a template.txt where the first line is the subject and the body uses {name} placeholders. The script reads all three, renders an email per recipient, has a dry-run flag that prints everything without sending, asks for a y confirmation if it's a live send, and then sends one at a time with a 4-second delay between each.
The dry-run flag matters more than it sounds. The number one mistake is a typo in your template — {nmae} instead of {name} — and Python's string formatter will quietly send the literal {nmae} to all 80 recipients. A dry-run that prints every rendered email to your terminal catches this in five seconds and saves you the apology email. Always dry-run first.
The whole script is about 120 lines of stdlib Python. I keep the working version saved as an npad here: https://npad.run/p/how-to-send-emails-using-gmail-programmatically-sgkbrkxaxs. If you have Claude Code or Cursor, paste that URL and tell your agent to set this up. It'll write the script, the env, the CSV format, and the template. One shot, no copy-pasting from this article.
A few things I learned the slightly painful way that aren't obvious from the docs.
Send one email per recipient, not one BCC'd to everyone. BCC blasts look like spam to filters, and some email clients reveal the BCC list anyway, which is how you accidentally show 50 strangers each other's addresses. Sending one at a time means each person sees only their own address and it looks like you actually wrote to them.
Put a real delay between sends. 4–5 seconds is the sweet spot. Faster and Gmail starts returning 421 4.7.0 errors that mean "you look like a bot, slow down." Don't try to be clever about parallelism — Gmail's free tier wants quiet, polite traffic, not a burst of fifty messages in three seconds.
Add a confirmation prompt before sending. "About to send to 80 recipients. Continue? [y/N]" is the cheapest insurance you'll ever write. The day you accidentally point the script at the wrong CSV, that prompt is what saves you.
Free Gmail will let you send around 500 emails per day before it starts pushing back. Workspace bumps that to 2,000. If you need more than that, you're at the volume where SendGrid or Postmark actually starts to make sense — they exist because at scale you do need bounce handling, deliverability monitoring, and a warmed-up sender reputation. But for under 500 emails a day of personalized outreach, Gmail is genuinely fine. Better than fine actually — it lands in inboxes more reliably than a cold ESP IP because Gmail has spent twenty years building sender trust on its own infrastructure.
btw if you really want to push past the daily cap, you can rotate keys across multiple Gmail accounts. hehe.
Disclaimer: I am not sure this post is appropriate for r/LearnPython since it's not a question of "how to do something in Python", rather I am looking for a lower-level discussion for why my Python application performs poorly on a significantly more powerful server. Hence I'm posting it here.
The problem:
I have a relatively complex data pipeline that is written in Polars. On my local machine with 12 cores, the pipeline finishes in about 1200ms. On my 128-core EC2, it takes 13000ms to complete. I have tried setting the POLARS_MAX_THREADS parameter to 12 on the EC2, and it's still slower.
I am using a TMPFS partition on both machines to read the data into the pipeline directly from RAM. Both my machine and the EC2 have DDR5 RAM so I think they should be comparable.
Anyone have any ideas why the pipeline would run much slower on the EC2?
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!
Share the knowledge, enrich the community. Happy learning! 🌟
I focus on the practical path to getting started quickly while still building a solid foundation. That includes setting up the Pulumi project, configuring the GCP provider, working with a Python-based infrastructure definition, and understanding how Pulumi tracks state and applies changes. I also cover the basic developer workflow for previewing updates, deploying resources, and making iterative changes safely. The goal is to make the first experience with Pulumi and GCP feel approachable, especially if you already know Python and want to use that skill set to manage cloud resources.
Hi, Pyrefly maintainer here. Today we are pleased to share that Pyrefly, a fast type checker and language server for Python, has reached stable v1.0 status, meaning we are confident that Pyrefly is ready for production use.
Pyrefly was first released as an alpha in mid-2025 and followed up with a beta in November of that year. Since then, we have shipped over 60 minor releases: fixing hundreds of bugs, adding the features you’ve been asking for, and improving performance to be one of the fastest tools out there.
This would not have been possible without our amazing open-source community. To everyone who filed GitHub issues, submitted pull requests, gave us feedback at conferences, or joined us on Discord: thank you. Your contributions shaped this release, we’re grateful for every one of them, and we hope you continue being a part of the journey for future releases too.
We've published a blog post explaining what v1.0 means exactly, and what's next for Pyrefly.
Below is a summary of the changes to Pyrefly since the Beta release. The full release notes for v1.0 can be read on our Github.
We've continued to push Pyrefly's performance since the speed improvements we shared in February. Since beta:
(Tested on an M4 Macbook Pro using open-source benchmarks from type_coverage_py and ty_benchmark.)
Compare the performance of Pyrefly and other Python type checkers on our regularly updated benchmarking suite, which runs against 53 popular Python packages.
A new preset configuration option provides named bundles of error severities and behavior settings.
| Preset | Description |
|---|---|
off |
Silences all diagnostics. Useful for IDE-only users or if you want total control of which errors are enabled. |
basic |
Low-noise, high-confidence diagnostics only (syntax errors, missing imports, unknown names, etc.). Ideal for unconfigured projects or IDE-first users. |
legacy |
For codebases migrating from mypy. Disables checks mypy doesn't have. pyrefly init now emits this preset automatically when migrating from a mypy config. |
default |
The standard Pyrefly experience. Equivalent to having no preset. |
strict |
Enables additional strict checks on top of the default preset. For users who want to avoid Any types in their codebase. |
See the configuration docs for details.
We’ve made improvements to the out-of-the-box experience for projects without a pyrefly.toml.
pyrefly init would commit to disk.)We've been hard at work making the type checker robust and feature-complete, with a focus on driving down false positives and improving type quality in real-world code bases. Here are some highlights:
source.fixAll.RootModel, pydantic-settings, and pydantic.dataclasses..ipynb IDE support has reached full parity with .py files, with rename, find references, code actions, and document symbols all supported.Pyrefly ships with tools to aid with adopting type checking in an existing codebase. Two new tools since beta:
pyrefly coverage report outputs a JSON report with annotation completeness and type completeness metrics per function, class, and module, so you can track coverage over time.Going forward, we’ll switch from a weekly to monthly cadence for minor (1.x.0) releases, with patch releases in between as-needed for critical fixes. We’ll continue providing release notes for minor versions, so you can see what’s new in each release.
Hi everyone,
I am a Data Science student and I am far away from considering myself a "good coder", as I think I miss many fundamental concepts and I do not know a lot of standard good practices. Hence, I wanted the opinion from people with greater expertise in the field. Is it only me who hates how LLMs write code? I am not able to understand it properly, and I always have the feeling it is overcomplicating logics and processes that are in actually very simple, making a script 5 times its actual lenght.
I often do group projects with colleagues of mine, which I am now pretty sure do not even check the code the agent writes for them and it is always very frustrating to navigate in the maze of newly created functions and files which always feel redundant and inefficient.
In particular, I hate these features:
- They tend to go on a newline everytime there is a list of parameters, even when it is not necessary.
- Tthey do not comment nothing and if they do the comments are too long and too technical
- They declare way too many functions. I was thought that the main goal of functions was reusability. What is the purpose of having a function that you only call once? Maybe they even call that function "run_analysis_xyz". What is the purpose of that? Why do you have to prefer a code which is nested and does not execute top to bottom, in the same way that a reader can follow it?
- The variables naming rarely reflct their actual purposes.
- Unecessaries safety checks, that are good for general code but they make no sense
I am starting to hate group projects for this reason. I just get nervous when I try to read code that is clearly generated. Am I wrong for using AI just as an assistant and not as a completely autonomous code writer?
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!
Let's keep the conversation going. Happy discussing! 🌟