plots2 VS NumPy

Citizen science! It's great when people realize they can answer their own questions with observation and data, and for activism because data is a powerful story. One friend of mine started https://publiclab.org to feed this, and another is doing data journalism to highlight holes in the government's environmental data. https://www.muckrock.com/project/

My first contribution ever was to PublicLab's plots2 back in 2018. I had no idea what I was doing or what plots2 was. What attracted me was how welcoming they were (and still are) to first time contributors. With them: I opened my first PR, discussed in PR's conversation, and pushed the changes requested. Back then, that was a lot!

Hmm so to your first question: That pattern of selecting from a pre-populated list is often used with a tag or chip system where you can select and deselect one or more items from the list, something like this: https://github.com/publiclab/plots2/issues/6026 in this case the search CTA acts as the final decision to search while the selections are populating the search criteria. It sounds your system design is a bit different though. Sounds to me like an issue of heuristics of UI https://www.nngroup.com/articles/ten-usability-heuristics/ the third heuristic states:

Maybe public lab would be a good home? https://publiclab.org/

https://github.com/publiclab/plots2 A collaborative knowledge-exchange platform in Rails; we welcome first-time contributors! balloon

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]