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Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Personally polars' strictness is making me think about situations when in pandas we end up with object dtype, which we should probably avoid. Here's an example: https://github.com/pandas-dev/pandas/issues/50887 (polars would just error in such a case, which I think is the correct thing to do)
Public type stubs for pandas
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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scikit-learn: machine learning in Python
There's an issue here about that https://github.com/scikit-learn/scikit-learn/discussions/25450
Backlog of project ideas. I've been alternating emailing them to myself and putting them in issue trackers for years. Have wanted to make them more public for a long time, this seems like a good way to do it.
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.
Google BigQuery connector for pandas
I'm not sure if there is already support for all Arrow complex types in pandas 2.0, but we have some support of lists for sure, and I think structs too. For the bigquery part, I think you can ask this to the developers of this repo: https://github.com/googleapis/python-bigquery-pandas We basically wrap that library with the read_gbq() function. but there is not much big query specific in pandas other than that, so not much idea.
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