diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index 4d9a45abe17cd..f97d0048287b5 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -750,6 +750,7 @@ Groupby/resample/rolling
- Bug in :meth:`.DataFrameGroupBy.quantile` when ``interpolation="nearest"`` is inconsistent with :meth:`DataFrame.quantile` (:issue:`47942`)
- Bug in :meth:`.Resampler.interpolate` on a :class:`DataFrame` with non-uniform sampling and/or indices not aligning with the resulting resampled index would result in wrong interpolation (:issue:`21351`)
- Bug in :meth:`DataFrame.ewm` and :meth:`Series.ewm` when passed ``times`` and aggregation functions other than mean (:issue:`51695`)
+- Bug in :meth:`DataFrame.sum`, :meth:`Series.sum`, :meth:`DataFrameGroupBy.sum` and :math:`SeriesGroupBy.sum` where in case of all-nan values for object dtype the result is incorrectly set to 0 instead of ``nan``. (:issue:`60229`)
- Bug in :meth:`DataFrameGroupBy.agg` that raises ``AttributeError`` when there is dictionary input and duplicated columns, instead of returning a DataFrame with the aggregation of all duplicate columns. (:issue:`55041`)
- Bug in :meth:`DataFrameGroupBy.apply` and :meth:`SeriesGroupBy.apply` for empty data frame with ``group_keys=False`` still creating output index using group keys. (:issue:`60471`)
- Bug in :meth:`DataFrameGroupBy.apply` that was returning a completely empty DataFrame when all return values of ``func`` were ``None`` instead of returning an empty DataFrame with the original columns and dtypes. (:issue:`57775`)
diff --git a/pandas/_libs/groupby.pyx b/pandas/_libs/groupby.pyx
index f65fa2368967a..e9935256a2430 100644
--- a/pandas/_libs/groupby.pyx
+++ b/pandas/_libs/groupby.pyx
@@ -725,8 +725,12 @@ def group_sum(
raise ValueError("len(index) != len(labels)")
nobs = np.zeros((<object>out).shape, dtype=np.int64)
- # the below is equivalent to `np.zeros_like(out)` but faster
- sumx = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
+ if sum_t is object:
+ # For object dtype, fill value should not be 0 (#60229)
+ sumx = np.full((<object>out).shape, NAN, dtype=object)
+ else:
+ # the below is equivalent to `np.zeros_like(out)` but faster
+ sumx = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
compensation = np.zeros((<object>out).shape, dtype=(<object>out).base.dtype)
N, K = (<object>values).shape
@@ -760,7 +764,10 @@ def group_sum(
if uses_mask:
isna_result = result_mask[lab, j]
else:
- isna_result = _treat_as_na(sumx[lab, j], is_datetimelike)
+ isna_result = (
+ _treat_as_na(sumx[lab, j], is_datetimelike) and
+ nobs[lab, j] > 0
+ )
if isna_result:
# If sum is already NA, don't add to it. This is important for
@@ -795,6 +802,7 @@ def group_sum(
compensation[lab, j] = 0
sumx[lab, j] = t
elif not skipna:
+ nobs[lab, j] += 1
if uses_mask:
result_mask[lab, j] = True
else:
diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index d1dc0ff809497..e671106a82db9 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -638,6 +638,13 @@ def nansum(
the_sum = values.sum(axis, dtype=dtype_sum)
the_sum = _maybe_null_out(the_sum, axis, mask, values.shape, min_count=min_count)
+ if dtype.kind == "O" and skipna and min_count == 0:
+ # GH#60229 For object dtype, sum of all-NA array should be nan
+ if isinstance(the_sum, np.ndarray):
+ the_sum[mask.sum(axis=axis) == mask.shape[axis]] = np.nan
+ elif mask.all():
+ the_sum = np.nan
+
return the_sum
diff --git a/pandas/tests/frame/test_arithmetic.py b/pandas/tests/frame/test_arithmetic.py
index 8239de3f39c20..c792bb69f18d4 100644
--- a/pandas/tests/frame/test_arithmetic.py
+++ b/pandas/tests/frame/test_arithmetic.py
@@ -1186,7 +1186,7 @@ def test_frame_single_columns_object_sum_axis_1():
}
df = DataFrame(data)
result = df.sum(axis=1)
- expected = Series(["A", 1.2, 0])
+ expected = Series(["A", 1.2, np.nan])
tm.assert_series_equal(result, expected)
diff --git a/pandas/tests/frame/test_reductions.py b/pandas/tests/frame/test_reductions.py
index 64e686d25faa7..1eeea60d184fa 100644
--- a/pandas/tests/frame/test_reductions.py
+++ b/pandas/tests/frame/test_reductions.py
@@ -420,10 +420,25 @@ def test_stat_operators_attempt_obj_array(self, method, df, axis):
assert df.values.dtype == np.object_
result = getattr(df, method)(axis=axis)
expected = getattr(df.astype("f8"), method)(axis=axis).astype(object)
+ if method == "sum":
+ # GH#60229 in case of all-NA object array, sum should be nan
+ expected[df.isna().all(axis=axis)] = np.nan
if axis in [1, "columns"] and method in ["min", "max"]:
expected[expected.isna()] = None
tm.assert_series_equal(result, expected)
+ def test_object_sum_allna(self):
+ # GH#60229
+ df = DataFrame({"a": [np.nan] * 5, "b": [pd.NA] * 5}, dtype=object)
+
+ result = df.sum(axis=0, skipna=True)
+ expected = Series([np.nan, np.nan], index=["a", "b"], dtype=object)
+ tm.assert_series_equal(result, expected)
+
+ result = df.sum(axis=0, skipna=False)
+ expected = Series([np.nan, pd.NA], index=["a", "b"], dtype=object)
+ tm.assert_series_equal(result, expected)
+
@pytest.mark.parametrize("op", ["mean", "std", "var", "skew", "kurt", "sem"])
def test_mixed_ops(self, op):
# GH#16116
diff --git a/pandas/tests/groupby/test_categorical.py b/pandas/tests/groupby/test_categorical.py
index e49be8c00b426..db63dff553c7c 100644
--- a/pandas/tests/groupby/test_categorical.py
+++ b/pandas/tests/groupby/test_categorical.py
@@ -358,6 +358,7 @@ def test_observed(request, using_infer_string, observed):
expected = cartesian_product_for_groupers(
expected, [cat1, cat2], list("AB"), fill_value=0
)
+ expected.loc[expected.C == 0, "C"] = np.nan
tm.assert_frame_equal(result, expected)
diff --git a/pandas/tests/groupby/test_reductions.py b/pandas/tests/groupby/test_reductions.py
index ea876cfdf4933..33306cc756fb1 100644
--- a/pandas/tests/groupby/test_reductions.py
+++ b/pandas/tests/groupby/test_reductions.py
@@ -514,6 +514,21 @@ def test_sum_skipna_object(skipna):
tm.assert_series_equal(result, expected)
+def test_sum_allnan_object(skipna):
+ # GH#60229
+ df = DataFrame(
+ {
+ "val": [np.nan] * 10,
+ "cat": ["A", "B"] * 5,
+ }
+ ).astype({"val": object})
+ expected = Series(
+ [np.nan, np.nan], index=pd.Index(["A", "B"], name="cat"), name="val"
+ ).astype(object)
+ result = df.groupby("cat")["val"].sum(skipna=skipna)
+ tm.assert_series_equal(result, expected)
+
+
@pytest.mark.parametrize(
"func, values, dtype, result_dtype",
[
diff --git a/pandas/tests/groupby/test_timegrouper.py b/pandas/tests/groupby/test_timegrouper.py
index 550efe9187fe8..63d0ca0de9bb1 100644
--- a/pandas/tests/groupby/test_timegrouper.py
+++ b/pandas/tests/groupby/test_timegrouper.py
@@ -113,7 +113,7 @@ def test_groupby_with_timegrouper(self):
unit=df.index.unit,
)
expected = DataFrame(
- {"Buyer": 0, "Quantity": 0},
+ {"Buyer": np.nan, "Quantity": 0},
index=exp_dti,
)
# Cast to object to avoid implicit cast when setting entry to "CarlCarlCarl"
diff --git a/pandas/tests/series/test_reductions.py b/pandas/tests/series/test_reductions.py
index 86ce60b1fc12b..b6f2d03405749 100644
--- a/pandas/tests/series/test_reductions.py
+++ b/pandas/tests/series/test_reductions.py
@@ -111,6 +111,20 @@ def test_prod_numpy16_bug():
assert not isinstance(result, Series)
+@pytest.mark.parametrize("nan_val", [np.nan, pd.NA])
+def test_object_sum_allna(nan_val):
+ # GH#60229
+ ser = Series([nan_val] * 5, dtype=object)
+
+ result = ser.sum(axis=0, skipna=True)
+ expected = np.nan
+ tm.assert_equal(result, expected)
+
+ result = ser.sum(axis=0, skipna=False)
+ expected = nan_val
+ tm.assert_equal(result, expected)
+
+
@pytest.mark.parametrize("func", [np.any, np.all])
@pytest.mark.parametrize("kwargs", [{"keepdims": True}, {"out": object()}])
def test_validate_any_all_out_keepdims_raises(kwargs, func):