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(