Backport PR pandas-dev#49886 on branch 1.5.x (CI: Remove deprecated numpy dtype aliases) (pandas-dev#49887)

Backport PR pandas-dev#49886: CI: Remove deprecated numpy dtype aliases

Co-authored-by: Patrick Hoefler <[email protected]>

Author: meeseeksmachine

asv_bench/benchmarks/sparse.py

@@ -219,12 +219,12 @@ def setup(self, fill_value):
         d = 1e-5
         arr = make_array(N, d, np.nan, np.float64)
         self.sp_arr = SparseArray(arr)
-        b_arr = np.full(shape=N, fill_value=fill_value, dtype=np.bool8)
+        b_arr = np.full(shape=N, fill_value=fill_value, dtype=np.bool_)
         fv_inds = np.unique(
             np.random.randint(low=0, high=N - 1, size=int(N * d), dtype=np.int32)
         )
         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)
+        self.sp_b_arr = SparseArray(b_arr, dtype=np.bool_, fill_value=fill_value)
 
     def time_mask(self, fill_value):
         self.sp_arr[self.sp_b_arr]

pandas/core/arrays/sparse/array.py

@@ -728,7 +728,7 @@ def isna(self):
         dtype = SparseDtype(bool, self._null_fill_value)
         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 = np.full(len(self), False, dtype=np.bool_)
         mask[self.sp_index.indices] = isna(self.sp_values)
         return type(self)(mask, fill_value=False, dtype=dtype)
 
@@ -1043,7 +1043,7 @@ def __getitem__(
                     if not key.fill_value:
                         return self.take(key.sp_index.indices)
                     n = len(self)
-                    mask = np.full(n, True, dtype=np.bool8)
+                    mask = np.full(n, True, dtype=np.bool_)
                     mask[key.sp_index.indices] = False
                     return self.take(np.arange(n)[mask])
                 else:

pandas/core/interchange/column.py

@@ -315,7 +315,7 @@ def _get_validity_buffer(self) -> tuple[PandasBuffer, Any]:
             valid = invalid == 0
             invalid = not valid
 
-            mask = np.zeros(shape=(len(buf),), dtype=np.bool8)
+            mask = np.zeros(shape=(len(buf),), dtype=np.bool_)
             for i, obj in enumerate(buf):
                 mask[i] = valid if isinstance(obj, str) else invalid
 

pandas/tests/arrays/sparse/test_indexing.py

@@ -85,7 +85,7 @@ def test_boolean_slice_empty(self):
 
     def test_getitem_bool_sparse_array(self):
         # GH 23122
-        spar_bool = SparseArray([False, True] * 5, dtype=np.bool8, fill_value=True)
+        spar_bool = SparseArray([False, True] * 5, dtype=np.bool_, fill_value=True)
         exp = SparseArray([np.nan, 2, np.nan, 5, 6])
         tm.assert_sp_array_equal(arr[spar_bool], exp)
 
@@ -95,7 +95,7 @@ def test_getitem_bool_sparse_array(self):
         tm.assert_sp_array_equal(res, exp)
 
         spar_bool = SparseArray(
-            [False, True, np.nan] * 3, dtype=np.bool8, fill_value=np.nan
+            [False, True, np.nan] * 3, dtype=np.bool_, fill_value=np.nan
         )
         res = arr[spar_bool]
         exp = SparseArray([np.nan, 3, 5])

pandas/tests/arrays/sparse/test_reductions.py

@@ -142,7 +142,7 @@ def test_sum_min_count(self, arr, fill_value, min_count, expected):
             assert result == expected
 
     def test_bool_sum_min_count(self):
-        spar_bool = SparseArray([False, True] * 5, dtype=np.bool8, fill_value=True)
+        spar_bool = SparseArray([False, True] * 5, dtype=np.bool_, fill_value=True)
         res = spar_bool.sum(min_count=1)
         assert res == 5
         res = spar_bool.sum(min_count=11)

pandas/tests/arrays/sparse/test_unary.py

@@ -59,9 +59,9 @@ def test_abs_operator(self):
         tm.assert_sp_array_equal(exp, res)
 
     def test_invert_operator(self):
-        arr = SparseArray([False, True, False, True], fill_value=False, dtype=np.bool8)
+        arr = SparseArray([False, True, False, True], fill_value=False, dtype=np.bool_)
         exp = SparseArray(
-            np.invert([False, True, False, True]), fill_value=True, dtype=np.bool8
+            np.invert([False, True, False, True]), fill_value=True, dtype=np.bool_
         )
         res = ~arr
         tm.assert_sp_array_equal(exp, res)

pandas/tests/io/excel/test_writers.py

@@ -496,15 +496,14 @@ def test_float_types(self, np_type, path):
 
         tm.assert_frame_equal(df, recons)
 
-    @pytest.mark.parametrize("np_type", [np.bool8, np.bool_])
-    def test_bool_types(self, np_type, path):
-        # Test np.bool8 and np.bool_ values read come back as float.
-        df = DataFrame([1, 0, True, False], dtype=np_type)
+    def test_bool_types(self, path):
+        # Test np.bool_ values read come back as float.
+        df = DataFrame([1, 0, True, False], dtype=np.bool_)
         df.to_excel(path, "test1")
 
         with ExcelFile(path) as reader:
             recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype(
-                np_type
+                np.bool_
             )
 
         tm.assert_frame_equal(df, recons)