Remove deprecation of fill_value for SparseDType[bool](pandas-dev#44955)

asv_bench/benchmarks/sparse.py

@@ -223,7 +223,7 @@ def setup(self, fill_value):
         fv_inds = np.unique(
             np.random.randint(low=0, high=N - 1, size=int(N * d), dtype=np.int32)
         )
-        b_arr[fv_inds] = not fill_value
+        b_arr[fv_inds] = True if pd.isna(fill_value) else not fill_value
         self.sp_b_arr = SparseArray(b_arr, dtype=np.bool8, fill_value=fill_value)
 
     def time_mask(self, fill_value):

doc/source/whatsnew/v1.4.0.rst

@@ -545,7 +545,6 @@ Other Deprecations
 - A deprecation warning is now shown for :meth:`DataFrame.to_latex` indicating the arguments signature may change and emulate more the arguments to :meth:`.Styler.to_latex` in future versions (:issue:`44411`)
 - Deprecated :meth:`Categorical.replace`, use :meth:`Series.replace` instead (:issue:`44929`)
 - Deprecated :meth:`Index.__getitem__` with a bool key; use ``index.values[key]`` to get the old behavior (:issue:`44051`)
-- Deprecated direct passing non boolean or non nan value to ``fill_value`` for :class:`SparseDType` when dtype is bool type (:pull:`44955`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/core/arrays/sparse/dtype.py

@@ -24,7 +24,6 @@
 )
 from pandas.core.dtypes.cast import astype_nansafe
 from pandas.core.dtypes.common import (
-    is_bool,
     is_bool_dtype,
     is_object_dtype,
     is_scalar,
@@ -154,13 +153,18 @@ def _check_fill_value(self):
             raise ValueError(
                 f"fill_value must be a scalar. Got {self._fill_value} instead"
             )
-        if self._is_boolean and not (
-            is_bool(self._fill_value) or isna(self._fill_value)
-        ):
-            raise ValueError(
-                "fill_value must be True, False or nan "
-                f"for boolean type. Got {self._fill_value} instead"
-            )
+        # TODO: Right now we can use Sparse boolean array
+        #       with any fill_value. Here was an attempt
+        #       to allow only 3 value: True, False or nan
+        #       but plenty test has failed.
+        # see pull 44955
+        # if self._is_boolean and not (
+        #    is_bool(self._fill_value) or isna(self._fill_value)
+        # ):
+        #    raise ValueError(
+        #        "fill_value must be True, False or nan "
+        #        f"for boolean type. Got {self._fill_value} instead"
+        #    )
 
     @property
     def _is_na_fill_value(self) -> bool:

pandas/tests/arrays/sparse/test_array.py

@@ -625,9 +625,11 @@ def test_set_fill_value(self):
         assert arr.fill_value
 
         # coerces to bool
-        msg = "fill_value must be True, False or nan"
-        with pytest.raises(ValueError, match=msg):
-            arr.fill_value = 0
+        # XXX: we can construct an sparse array of bool
+        #      type and use as fill_value any value
+        # msg = "fill_value must be True, False or nan"
+        # with pytest.raises(ValueError, match=msg):
+        #    arr.fill_value = 0
 
         # msg = "unable to set fill_value nan to bool dtype"
         # with pytest.raises(ValueError, match=msg):