DOC: np.bool -> np.bool_

asv_bench/benchmarks/groupby.py

@@ -627,7 +627,7 @@ class TransformBools:
     def setup(self):
         N = 120000
         transition_points = np.sort(np.random.choice(np.arange(N), 1400))
-        transitions = np.zeros(N, dtype=np.bool)
+        transitions = np.zeros(N, dtype=np.bool_)
         transitions[transition_points] = True
         self.g = transitions.cumsum()
         self.df = DataFrame({"signal": np.random.rand(N)})

doc/source/user_guide/basics.rst

@@ -2229,7 +2229,7 @@ Convert certain columns to a specific dtype by passing a dict to :meth:`~DataFra
 .. ipython:: python
 
    dft1 = pd.DataFrame({"a": [1, 0, 1], "b": [4, 5, 6], "c": [7, 8, 9]})
-   dft1 = dft1.astype({"a": np.bool, "c": np.float64})
+   dft1 = dft1.astype({"a": np.bool_, "c": np.float64})
    dft1
    dft1.dtypes
 

doc/source/user_guide/cookbook.rst

@@ -1406,7 +1406,7 @@ Often it's useful to obtain the lower (or upper) triangular form of a correlatio
     df = pd.DataFrame(np.random.random(size=(100, 5)))
 
     corr_mat = df.corr()
-    mask = np.tril(np.ones_like(corr_mat, dtype=np.bool), k=-1)
+    mask = np.tril(np.ones_like(corr_mat, dtype=np.bool_), k=-1)
 
     corr_mat.where(mask)