PERF: Faster SparseArray.__get_item__ for boolean masks (pandas-dev#23122)

Author: bdrum

pandas/core/arrays/sparse/array.py

@@ -52,6 +52,7 @@
 )
 from pandas.core.dtypes.common import (
     is_array_like,
+    is_bool,
     is_bool_dtype,
     is_datetime64_any_dtype,
     is_datetime64tz_dtype,
@@ -719,7 +720,11 @@ def isna(self):
         # If null fill value, we want SparseDtype[bool, true]
         # to preserve the same memory usage.
         dtype = SparseDtype(bool, self._null_fill_value)
-        return type(self)._simple_new(isna(self.sp_values), self.sp_index, dtype)
+        if self._null_fill_value:
+            return type(self)._simple_new(isna(self.sp_values), self.sp_index, dtype)
+        mask = np.full(len(self), False, dtype=np.bool8)
+        mask[self.sp_index.indices] = isna(self.sp_values)
+        return type(self)(mask, fill_value=False, dtype=dtype)
 
     def fillna(
         self: SparseArrayT,
@@ -963,13 +968,18 @@ def __getitem__(
             )
 
         else:
-            # TODO: I think we can avoid densifying when masking a
-            # boolean SparseArray with another. Need to look at the
-            # key's fill_value for True / False, and then do an intersection
-            # on the indices of the sp_values.
             if isinstance(key, SparseArray):
                 if is_bool_dtype(key):
-                    key = key.to_dense()
+                    if is_bool(key.fill_value):
+                        msk = np.full(
+                            shape=len(self),
+                            fill_value=key.fill_value,
+                            dtype=np.bool8,
+                        )
+                        msk[key.sp_index.indices] = not key.fill_value
+                        return self.take(np.arange(len(self), dtype=np.int32)[msk])
+                    else:
+                        key = key.to_dense()
                 else:
                     key = np.asarray(key)
 

pandas/tests/arrays/sparse/test_array.py

@@ -248,6 +248,16 @@ def test_scalar_with_index_infer_dtype(self, scalar, dtype):
         assert arr.dtype == dtype
         assert exp.dtype == dtype
 
+    # GH 23122
+    def test_get_item_bool_sparse_array(self):
+        spar_bool = SparseArray([False, True] * 5, dtype=np.bool8, fill_value=True)
+        exp = SparseArray([np.nan, 2, np.nan, 5, 6])
+        tm.assert_sp_array_equal(self.arr[spar_bool], exp)
+
+        spar_bool = SparseArray(~spar_bool.to_dense(), dtype=np.bool8, fill_value=False)
+        exp = SparseArray([np.nan, 1, 3, 4, np.nan])
+        tm.assert_sp_array_equal(self.arr[spar_bool], exp)
+
     def test_get_item(self):
 
         assert np.isnan(self.arr[1])

pandas/tests/extension/test_sparse.py

@@ -208,6 +208,12 @@ def test_isna(self, data_missing):
         expected = pd.Series([], dtype=expected_dtype)
         self.assert_series_equal(result, expected)
 
+        # test isna for arr without na
+        data_missing = data_missing.fillna(0)
+        expected_dtype = SparseDtype(bool, pd.isna(data_missing.dtype.fill_value))
+        expected = SparseArray([False, False], fill_value=False, dtype=expected_dtype)
+        self.assert_equal(pd.isna(data_missing), expected)
+
     def test_fillna_limit_pad(self, data_missing):
         with tm.assert_produces_warning(PerformanceWarning):
             super().test_fillna_limit_pad(data_missing)