Pandas support using new API #645
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I would be very interested to support this. Maybe you can describe here how you envision this. But I would really like to see this happening. |
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684: Add pandas support r=hgrecco a=znicholls This pull request adds pandas support to pint (hence is related to #645, #401 and pandas-dev/pandas#10349). An example can be seen in `example-notebooks/basic-example.ipynb`. It's a little bit hacksih, feedback would be greatly appreciated by me and @andrewgsavage. One obvious example is that we have to run all the interface tests with `pytest` to fit with `pandas` test suite, which introduces a dependency for the CI and currently gives us this awkward testing setup (see the alterations we had to make to `testsuite`). This also means that our code coverage tests are fiddly too. If you'd like us to squash the commits, that can be done. If pint has a linter, it would be good to run that over this pull request too as we're a little bit all over the place re style. Things to discuss: - [x] general feedback and changes - [x] test setup, especially need for pytest for pandas tests and hackish way to get around automatic discovery - [x] squashing/rebasing - [x] linting/other code style (related to #664 and #628: we're happy with whatever, I've found using an automatic linter e.g. black and/or flake8 has made things much simpler in other projects) - [x] including notebooks in the repo (if we want to, I'm happy to put them under CI so we can make sure they run) - [x] setting up the docs correctly Co-authored-by: Zebedee Nicholls <zebedee.nicholls@climate-energy-college.org> Co-authored-by: andrewgsavage <andrewgsavage@gmail.com>
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@hgrecco is this closed as wontfix, works already, or refer to pint-pandas? |
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The most recent pandas release (0.23) introduces extension types which I believe provide a natural way to integrate pint types into pandas dataframes on a per row/column basis. Is this a feature that could be merged here (assuming no breakage when older/no pandas is installed)?
If so, I'd be interested in researching that possibility further and maybe even implementing it myself. As a disclaimer, I've only started using pint quite recently and am not very proficient with pandas either and I'm not sure when I really get the time to do this. In any case, if someone else beats me to coding the feature, that'd be perfect, too :)
Related issues (which I haven't read fully):
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