plots2 VS Pandas

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

Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail. Instead, we'll focus on what's necessary to make it run serverless.

there is pull request to integrate in both pydantic extra types and into pandas cose [1]

[1]: https://github.com/pandas-dev/pandas/issues/53999

Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts.

pandas: A library for data analysis in Python

Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks.

Pandas, a powerful data manipulation library in Python, has become an essential tool for data scientists and analysts. One of its key functions is read_csv(), which allows users to read data from CSV (Comma-Separated Values) files into a Pandas DataFrame. In this tutorial, brought to you by CodesWithPankaj.com, we will explore the intricacies of read_csv() with clear examples to help you harness its full potential.

So, what I'd like to do is write a documentation package in Python to recreate what I've lost. I plan to build upon the fantastic python-docx and docxtpl packages, and I'll probably rely on pandas from much of the tabular stuff. Here are the features I intend to include:

Weirdly most of my time spent with data analysis was in the C layers in pandas.

Explore here.