Patrick Hoefler

Results 345 comments of Patrick Hoefler

FIX: REGR: setting numeric value in Categorical Series with enlargement raise internal error

Yeah this is also a bug then. Could you check if the categorical dtypes match and then return the dtype accordingly?

FIX: REGR: setting numeric value in Categorical Series with enlargement raise internal error

How does concat behave in that case?

FIX: REGR: setting numeric value in Categorical Series with enlargement raise internal error

Yeah I think t2 is off

FIX: REGR: setting numeric value in Categorical Series with enlargement raise internal error

Could you try t2 with categories specified as a,b, c?

FIX: REGR: setting numeric value in Categorical Series with enlargement raise internal error

No this make sense, the dtypes are not equal with different categories. But enlargement with a scalar is a different case, we should preserve categorical dtype there. We have to...

FIX: REGR: setting numeric value in Categorical Series with enlargement raise internal error

DataFrame cases don't work yet, this is correct. But this has multiple issues still, so lets focus on Series here

FIX: REGR: setting numeric value in Categorical Series with enlargement raise internal error

Could you also add a test where we enlarge with nan? This is not part of the categories but should probably work? Not sure

FIX: REGR: setting numeric value in Categorical Series with enlargement raise internal error

I was referring to the opposite. ``` result = Series([1, 2, 3], dtype=pd.CategoricalDtype(categories=pd.Index([1, 2, 3]))) result.loc[1] = np.nan # np.nan and pd.NA ``` this keeps categorical dtype, e.g. enlargement should...

FIX: REGR: setting numeric value in Categorical Series with enlargement raise internal error

Its called something like any_numeric_dtype

FIX: REGR: setting numeric value in Categorical Series with enlargement raise internal error

I think there exists one that includes numpy dtypes, but this would work too