pandas-stubs
bench-warmers
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| pandas-stubs | bench-warmers | |
|---|---|---|
| 4 | 6 | |
| 210 | 54 | |
| 3.3% | - | |
| 8.6 | 9.7 | |
| 7 days ago | 15 days ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pandas-stubs
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We are the developers behind pandas, currently preparing for the 2.0 release :) AMA
There is a typing effort that is led by some core members (unfortunately none of them takes part today). You can check the stubs package out at https://github.com/pandas-dev/pandas-stubs. I am not really familiar with the progress there
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Is there a way to have Eglot pickup the virtual env?
I think the issue is that pandas provides its stubs via a separate package namely pandas-stubs. It seems some packages provide their stubs in a separate package i.a.w PEP561. If I install this package I get completions etc. as you might expect.
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TypeScript is terrible for library developers
It gets a lot more complicated than that. One of many, many examples: https://github.com/pandas-dev/pandas-stubs/blob/v1.4.3.22082...
bench-warmers
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What to do next?
i have more ideas than I know what to do with, help yourself: https://github.com/dmarx/bench-warmers
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Any ideas for NLP end-to-end projects or blogs for a beginner with a linguistics background to boost their CV?
you're welcome to help yourself to my ideas (no guarantees that they're any good or even comprehensible, I do a lot of my brainstorming while high). here's my brainstorming space, scroll down for a categorized ToC: https://github.com/dmarx/bench-warmers
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[R] LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
I've decided to just lean into it and am literally just giving my ideas away. https://github.com/dmarx/bench-warmers
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Using Github to write my notes has helped me retain knowledge immensely.
it might sound like a lot, but it's actually really lightweight and easy to use. Check it out: https://github.com/dmarx/bench-warmers
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We are the developers behind pandas, currently preparing for the 2.0 release :) AMA
you've sort of become victims of your own success: as another pandas dev mentioned, you want to preserve backwards compatibility and this significantly complicates any restructuring. I'm sympathetic and am not sure what the best solution here would be. I had this idea last night but i'm not sure I like this approach either.
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Need help on finding an area where machine learning is applicable on day-to-day life but not implemented already
To be clear, i'm talking about e.g. vision impaired, hearing impaired, etc. Here's an example of a project idea in this space (possibly a bit more ambitious than what you're looking for but if you think you could tackle this I encourage you to take a stab at it): https://github.com/dmarx/bench-warmers/blob/main/automated-video-description.md
What are some alternatives?
django-stubs - PEP-484 stubs for Django
khoj - Your AI second brain. A copilot to get answers to your questions, whether they be from your own notes or from the internet. Use powerful, online (e.g gpt4) or private, local (e.g mistral) LLMs. Self-host locally or use our web app. Access from Obsidian, Emacs, Desktop app, Web or Whatsapp.
lxml-stubs - Type stubs for the lxml package
notes
pfun - Functional, composable, asynchronous, type-safe Python.
LLaMA-Adapter - [ICLR 2024] Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters
returns - Make your functions return something meaningful, typed, and safe!
python-bigquery-pandas - Google BigQuery connector for pandas
bulk-exif-to-excel - Bulk exif data export to excel
obsidian-omnisearch - A search engine that "just works" for Obsidian. Supports OCR and PDF indexing.
emacs-pet - Tracks down the correct Python tooling executables from your virtualenvs so you can glue the binaries to Emacs and delete code in init.el
scikit-learn - scikit-learn: machine learning in Python