diff --git a/doc/source/whatsnew/v2.3.0.rst b/doc/source/whatsnew/v2.3.0.rst index 8bdddb5b7f85d..54dccb97bc9eb 100644 --- a/doc/source/whatsnew/v2.3.0.rst +++ b/doc/source/whatsnew/v2.3.0.rst @@ -107,6 +107,7 @@ Conversion Strings ^^^^^^^ +- Bug in :meth:`DataFrame.sum` with ``axis=1``, :meth:`.DataFrameGroupBy.sum` or :meth:`.SeriesGroupBy.sum` with ``skipna=True``, and :meth:`.Resampler.sum` on :class:`StringDtype` with all NA values resulted in ``0`` and is now the empty string ``""`` (:issue:`60229`) - Bug in :meth:`Series.__pos__` and :meth:`DataFrame.__pos__` did not raise for :class:`StringDtype` with ``storage="pyarrow"`` (:issue:`60710`) - Bug in :meth:`Series.rank` for :class:`StringDtype` with ``storage="pyarrow"`` incorrectly returning integer results in case of ``method="average"`` and raising an error if it would truncate results (:issue:`59768`) - Bug in :meth:`Series.replace` with :class:`StringDtype` when replacing with a non-string value was not upcasting to ``object`` dtype (:issue:`60282`) diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py index 33745438e2aea..dbf2090e53579 100644 --- a/pandas/core/arrays/base.py +++ b/pandas/core/arrays/base.py @@ -2628,7 +2628,15 @@ def _groupby_op( if op.how not in ["any", "all"]: # Fail early to avoid conversion to object op._get_cython_function(op.kind, op.how, np.dtype(object), False) - npvalues = self.to_numpy(object, na_value=np.nan) + + arr = self + if op.how == "sum": + # https://github.com/pandas-dev/pandas/issues/60229 + # All NA should result in the empty string. + assert "skipna" in kwargs + if kwargs["skipna"] and min_count == 0: + arr = arr.fillna("") + npvalues = arr.to_numpy(object, na_value=np.nan) else: raise NotImplementedError( f"function is not implemented for this dtype: {self.dtype}" diff --git a/pandas/tests/frame/test_reductions.py b/pandas/tests/frame/test_reductions.py index 64e686d25faa7..127f0fc50a747 100644 --- a/pandas/tests/frame/test_reductions.py +++ b/pandas/tests/frame/test_reductions.py @@ -835,6 +835,16 @@ def test_axis_1_empty(self, all_reductions, index): expected = Series([], index=index, dtype=expected_dtype) tm.assert_series_equal(result, expected) + @pytest.mark.parametrize("min_count", [0, 1]) + def test_axis_1_sum_na(self, string_dtype_no_object, skipna, min_count): + # https://github.com/pandas-dev/pandas/issues/60229 + dtype = string_dtype_no_object + df = DataFrame({"a": [pd.NA]}, dtype=dtype) + result = df.sum(axis=1, skipna=skipna, min_count=min_count) + value = "" if skipna and min_count == 0 else pd.NA + expected = Series([value], dtype=dtype) + tm.assert_series_equal(result, expected) + @pytest.mark.parametrize("method, unit", [("sum", 0), ("prod", 1)]) @pytest.mark.parametrize("numeric_only", [None, True, False]) def test_sum_prod_nanops(self, method, unit, numeric_only): diff --git a/pandas/tests/groupby/test_reductions.py b/pandas/tests/groupby/test_reductions.py index ea876cfdf4933..45047fe004aa0 100644 --- a/pandas/tests/groupby/test_reductions.py +++ b/pandas/tests/groupby/test_reductions.py @@ -955,6 +955,20 @@ def test_min_empty_string_dtype(func, string_dtype_no_object): tm.assert_frame_equal(result, expected) +@pytest.mark.parametrize("min_count", [0, 1]) +def test_string_dtype_empty_sum(string_dtype_no_object, skipna, min_count): + # https://github.com/pandas-dev/pandas/issues/60229 + dtype = string_dtype_no_object + df = DataFrame({"a": ["x"], "b": [pd.NA]}, dtype=dtype) + gb = df.groupby("a") + result = gb.sum(skipna=skipna, min_count=min_count) + value = "" if skipna and min_count == 0 else pd.NA + expected = DataFrame( + {"b": value}, index=pd.Index(["x"], name="a", dtype=dtype), dtype=dtype + ) + tm.assert_frame_equal(result, expected) + + def test_max_nan_bug(): df = DataFrame( { diff --git a/pandas/tests/resample/test_base.py b/pandas/tests/resample/test_base.py index b2d9f6c0e3eb0..0db5c0c82d4d4 100644 --- a/pandas/tests/resample/test_base.py +++ b/pandas/tests/resample/test_base.py @@ -223,6 +223,31 @@ def test_resample_empty_series(freq, index, resample_method): assert result.index.freq == expected.index.freq +@pytest.mark.parametrize("min_count", [0, 1]) +def test_resample_empty_sum_string(string_dtype_no_object, min_count): + # https://github.com/pandas-dev/pandas/issues/60229 + dtype = string_dtype_no_object + ser = Series( + pd.NA, + index=DatetimeIndex( + [ + "2000-01-01 00:00:00", + "2000-01-01 00:00:10", + "2000-01-01 00:00:20", + "2000-01-01 00:00:30", + ] + ), + dtype=dtype, + ) + rs = ser.resample("20s") + result = rs.sum(min_count=min_count) + + value = "" if min_count == 0 else pd.NA + index = date_range(start="2000-01-01", freq="20s", periods=2, unit="s") + expected = Series(value, index=index, dtype=dtype) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( "freq", [ diff --git a/pandas/tests/resample/test_resampler_grouper.py b/pandas/tests/resample/test_resampler_grouper.py index e7850f96b3b0f..7870c5a9d3e17 100644 --- a/pandas/tests/resample/test_resampler_grouper.py +++ b/pandas/tests/resample/test_resampler_grouper.py @@ -494,6 +494,26 @@ def test_resample_groupby_agg_object_dtype_all_nan(consolidate): tm.assert_frame_equal(result, expected) +@pytest.mark.parametrize("min_count", [0, 1]) +def test_groupby_resample_empty_sum_string( + string_dtype_no_object, test_frame, min_count +): + # https://github.com/pandas-dev/pandas/issues/60229 + dtype = string_dtype_no_object + test_frame = test_frame.assign(B=pd.array([pd.NA] * len(test_frame), dtype=dtype)) + gbrs = test_frame.groupby("A").resample("40s") + result = gbrs.sum(min_count=min_count) + + index = pd.MultiIndex( + levels=[[1, 2, 3], [pd.to_datetime("2000-01-01", unit="ns")]], + codes=[[0, 1, 2], [0, 0, 0]], + names=["A", None], + ) + value = "" if min_count == 0 else pd.NA + expected = DataFrame({"B": value}, index=index, dtype=dtype) + tm.assert_frame_equal(result, expected) + + def test_groupby_resample_with_list_of_keys(): # GH 47362 df = DataFrame(