Fix indexing, reindex on all-sparse SparseArray. (#35287)

Author: TomAugspurger

doc/source/whatsnew/v1.1.0.rst

@@ -1163,7 +1163,7 @@ Sparse
 - Creating a :class:`SparseArray` from timezone-aware dtype will issue a warning before dropping timezone information, instead of doing so silently (:issue:`32501`)
 - Bug in :meth:`arrays.SparseArray.from_spmatrix` wrongly read scipy sparse matrix (:issue:`31991`)
 - Bug in :meth:`Series.sum` with ``SparseArray`` raises ``TypeError`` (:issue:`25777`)
-- Bug where :class:`DataFrame` containing :class:`SparseArray` filled with ``NaN`` when indexed by a list-like (:issue:`27781`, :issue:`29563`)
+- Bug where :class:`DataFrame` containing an all-sparse :class:`SparseArray` filled with ``NaN`` when indexed by a list-like (:issue:`27781`, :issue:`29563`)
 - The repr of :class:`SparseDtype` now includes the repr of its ``fill_value`` attribute. Previously it used ``fill_value``'s  string representation (:issue:`34352`)
 - Bug where empty :class:`DataFrame` could not be cast to :class:`SparseDtype` (:issue:`33113`)
 - Bug in :meth:`arrays.SparseArray` was returning the incorrect type when indexing a sparse dataframe with an iterable (:issue:`34526`, :issue:`34540`)

pandas/core/arrays/sparse/array.py

@@ -862,21 +862,26 @@ def _take_with_fill(self, indices, fill_value=None) -> np.ndarray:
             else:
                 raise IndexError("cannot do a non-empty take from an empty axes.")
 
+        # sp_indexer may be -1 for two reasons
+        # 1.) we took for an index of -1 (new)
+        # 2.) we took a value that was self.fill_value (old)
         sp_indexer = self.sp_index.lookup_array(indices)
+        new_fill_indices = indices == -1
+        old_fill_indices = (sp_indexer == -1) & ~new_fill_indices
 
-        if self.sp_index.npoints == 0:
+        if self.sp_index.npoints == 0 and old_fill_indices.all():
+            # We've looked up all valid points on an all-sparse array.
+            taken = np.full(
+                sp_indexer.shape, fill_value=self.fill_value, dtype=self.dtype.subtype
+            )
+
+        elif self.sp_index.npoints == 0:
             # Avoid taking from the empty self.sp_values
             _dtype = np.result_type(self.dtype.subtype, type(fill_value))
             taken = np.full(sp_indexer.shape, fill_value=fill_value, dtype=_dtype)
         else:
             taken = self.sp_values.take(sp_indexer)
 
-            # sp_indexer may be -1 for two reasons
-            # 1.) we took for an index of -1 (new)
-            # 2.) we took a value that was self.fill_value (old)
-            new_fill_indices = indices == -1
-            old_fill_indices = (sp_indexer == -1) & ~new_fill_indices
-
             # Fill in two steps.
             # Old fill values
             # New fill values

pandas/core/internals/blocks.py

@@ -1636,10 +1636,7 @@ def _holder(self):
     @property
     def fill_value(self):
         # Used in reindex_indexer
-        if is_sparse(self.values):
-            return self.values.dtype.fill_value
-        else:
-            return self.values.dtype.na_value
+        return self.values.dtype.na_value
 
     @property
     def _can_hold_na(self):

pandas/tests/arrays/sparse/test_array.py

@@ -281,6 +281,11 @@ def test_take(self):
         exp = SparseArray(np.take(self.arr_data, [0, 1, 2]))
         tm.assert_sp_array_equal(self.arr.take([0, 1, 2]), exp)
 
+    def test_take_all_empty(self):
+        a = pd.array([0, 0], dtype=pd.SparseDtype("int64"))
+        result = a.take([0, 1], allow_fill=True, fill_value=np.nan)
+        tm.assert_sp_array_equal(a, result)
+
     def test_take_fill_value(self):
         data = np.array([1, np.nan, 0, 3, 0])
         sparse = SparseArray(data, fill_value=0)

pandas/tests/extension/base/getitem.py

@@ -399,31 +399,3 @@ def test_item(self, data):
 
         with pytest.raises(ValueError, match=msg):
             s.item()
-
-    def test_boolean_mask_frame_fill_value(self, data):
-        # https://github.com/pandas-dev/pandas/issues/27781
-        df = pd.DataFrame({"A": data})
-
-        mask = np.random.choice([True, False], df.shape[0])
-        result = pd.isna(df.iloc[mask]["A"])
-        expected = pd.isna(df["A"].iloc[mask])
-        self.assert_series_equal(result, expected)
-
-        mask = pd.Series(mask, index=df.index)
-        result = pd.isna(df.loc[mask]["A"])
-        expected = pd.isna(df["A"].loc[mask])
-        self.assert_series_equal(result, expected)
-
-    def test_fancy_index_frame_fill_value(self, data):
-        # https://github.com/pandas-dev/pandas/issues/29563
-        df = pd.DataFrame({"A": data})
-
-        mask = np.random.choice(df.shape[0], df.shape[0])
-        result = pd.isna(df.iloc[mask]["A"])
-        expected = pd.isna(df["A"].iloc[mask])
-        self.assert_series_equal(result, expected)
-
-        mask = pd.Series(mask, index=df.index)
-        result = pd.isna(df.loc[mask]["A"])
-        expected = pd.isna(df["A"].loc[mask])
-        self.assert_series_equal(result, expected)

pandas/tests/extension/test_sparse.py

@@ -41,11 +41,6 @@ def data_for_twos(request):
     return SparseArray(np.ones(100) * 2)
 
 
-@pytest.fixture(params=[0, np.nan])
-def data_zeros(request):
-    return SparseArray(np.zeros(100, dtype=int), fill_value=request.param)
-
-
 @pytest.fixture(params=[0, np.nan])
 def data_missing(request):
     """Length 2 array with [NA, Valid]"""

pandas/tests/frame/indexing/test_sparse.py

@@ -49,3 +49,23 @@ def test_locindexer_from_spmatrix(self, spmatrix_t, dtype):
         result = df.loc[itr_idx].dtypes.values
         expected = np.full(cols, SparseDtype(dtype, fill_value=0))
         tm.assert_numpy_array_equal(result, expected)
+
+    def test_reindex(self):
+        # https://github.com/pandas-dev/pandas/issues/35286
+        df = pd.DataFrame(
+            {"A": [0, 1], "B": pd.array([0, 1], dtype=pd.SparseDtype("int64", 0))}
+        )
+        result = df.reindex([0, 2])
+        expected = pd.DataFrame(
+            {
+                "A": [0.0, np.nan],
+                "B": pd.array([0.0, np.nan], dtype=pd.SparseDtype("float64", 0.0)),
+            },
+            index=[0, 2],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_all_sparse(self):
+        df = pd.DataFrame({"A": pd.array([0, 0], dtype=pd.SparseDtype("int64"))})
+        result = df.loc[[0, 1]]
+        tm.assert_frame_equal(result, df)