The second concern of the issue #55114 was round() did not work for Series or Dataframe when using decimal.Decimal from python's default decimal module. It seems like this is because both NumPy and pandas give array-like structures of decimal.Decimal objects a dtype of object. When I tested np.array([decimal.Decimal("1.2234242333")]).round(), it raised an error.

If Series.round() and DataFrame.round() not raising an error on decimal.Decimal objects is still wanted, I thought a clean solution would be to make a new custom dtype for decimal.Decimal. However, that seemed like a pretty big change so wanted to check if there was another way I should be thinking about this bugfix.

i would expect this to attempt to operate pointwise (which would still raise on e.g. strings)

i would expect this to attempt to operate pointwise (which would still raise on e.g. strings)

Do you mean I should rewrite the code so that it attempts to round every column individually and then raise if there is a non-numeric column? As opposed to looking at the self.dtypes.values ?

Ohh because StringDtype also exists? and other non-numeric dtypes outside of object? Could I use pandas.api.types.is_numeric_dtype?

and other non-numeric dtypes outside of object?

im specifically thinking of object dtype columns containing numeric entries

Ah okay, makes sense.

I think the current behavior for series.round() is to raise when an object dtype column containing numeric entries is called though.

The test_round_dtype_object() test in pandas/tests/series/methods/test_round.py is this i think:

  def test_round_dtype_object(self):
        # GH#61206
        ser = Series([0.2], dtype="object")
        msg = "Expected numeric dtype, got object instead."
        with pytest.raises(TypeError, match=msg):
            ser.round()

Should I submit a PR to change this behavior first before implementing your pointwise solution?

@jbrockmendel Hi, no worries if too busy to look into this right now just curious if you had any insight on the above comment for making a different PR first, thanks!

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