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DataFrameGroupBy.quantile raises for non-numeric dtypes rather than dropping columns #27892
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Unfortunately, we can't just exclude object dtype. We apparently used to try to do the quantile, and caught any exceptions # in 0.24.2
In [3]: pd.DataFrame({"A": ['a', 'b']}, dtype=object).groupby([0, 0]).quantile()
Out[3]:
Empty DataFrame
Columns: []
Index: [] I don't know if that behavior is worth preserving. |
What is the desired behavior here? Some possibilities:
|
Ideally, we would match the 0.24.2 behavior. That can roughly be described as "attempt the quantile, but skip any columns that raise an error". But that may not be easily doable with the new quantile implementation. |
I'm not sure anyone will get to this before 0.25.1. I'll leave it at that milestone, but we can push if needed. |
When will 0.25.1 be released? I will try to understand what is happening with the quantile function. |
0.25.1 is targeted for this Wednesday.
…On Mon, Aug 19, 2019 at 12:52 PM Guilherme Salomé ***@***.***> wrote:
When will 0.25.1 be released? I will try to understand what is happening
with the quantile function.
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@WillAyd do you think this is doable for 1.0? You had a recent refactor for quantile right? |
Haven’t looked at this. I don’t object to pushing unless a community PR picks it up
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On Nov 12, 2019, at 9:20 AM, Tom Augspurger ***@***.***> wrote:
@WillAyd do you think this is doable for 1.0? You had a recent refactor for quantile right?
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looks like 0.25.3 still has the non-numeric issue... |
Pushing. |
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In pandas 0.24.x, we had
In 0.25.0, we have
This is most relevant for mixed dataframes
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