Some reverts

Author: rhshadrach

pandas/core/algorithms.py

@@ -415,7 +415,7 @@ def unique(values):
 
     >>> pd.unique(pd.array([1 + 1j, 2, 3]))
     <NumpyExtensionArray>
-    [np.complex128(1+1j), np.complex128(2+0j), np.complex128(3+0j)]
+    [(1+1j), (2+0j), (3+0j)]
     Length: 3, dtype: complex128
     """
     return unique_with_mask(values)

pandas/core/arrays/base.py

@@ -1072,7 +1072,7 @@ def interpolate(
         ...     limit_area="inside",
         ... )
         <NumpyExtensionArray>
-        [np.float64(0.0), np.float64(1.0), np.float64(2.0), np.float64(3.0)]
+        [0.0, 1.0, 2.0, 3.0]
         Length: 4, dtype: float64
 
         Interpolating values in a FloatingArray:

pandas/core/arrays/interval.py

@@ -1776,7 +1776,7 @@ def to_tuples(self, na_tuple: bool = True) -> np.ndarray:
         Length: 2, dtype: interval[int64, right]
         >>> idx.to_tuples()
         array([(np.int64(0), np.int64(1)), (np.int64(1), np.int64(2))],
-          dtype=object)
+              dtype=object)
 
         For :class:`pandas.IntervalIndex`:
 

pandas/core/arrays/numpy_.py

@@ -83,7 +83,7 @@ class NumpyExtensionArray(  # type: ignore[misc]
     --------
     >>> pd.arrays.NumpyExtensionArray(np.array([0, 1, 2, 3]))
     <NumpyExtensionArray>
-    [np.int64(0), np.int64(1), np.int64(2), np.int64(3)]
+    [0, 1, 2, 3]
     Length: 4, dtype: int64
     """
 

pandas/core/base.py

@@ -558,7 +558,7 @@ def array(self) -> ExtensionArray:
 
         >>> pd.Series([1, 2, 3]).array
         <NumpyExtensionArray>
-        [np.int64(1), np.int64(2), np.int64(3)]
+        [1, 2, 3]
         Length: 3, dtype: int64
 
         For extension types, like Categorical, the actual ExtensionArray
@@ -1389,7 +1389,7 @@ def factorize(
         Categories (4, object): ['apple' < 'bread' < 'cheese' < 'milk']
 
         >>> ser.searchsorted('bread')
-        1
+        np.int64(1)
 
         >>> ser.searchsorted(['bread'], side='right')
         array([3])

pandas/core/construction.py

@@ -177,7 +177,7 @@ def array(
 
     >>> pd.array(["a", "b"], dtype=str)
     <NumpyExtensionArray>
-    [np.str_('a'), np.str_('b')]
+    ['a', 'b']
     Length: 2, dtype: str32
 
     This would instead return the new ExtensionArray dedicated for string
@@ -186,7 +186,7 @@ def array(
 
     >>> pd.array(["a", "b"], dtype=np.dtype("<U1"))
     <NumpyExtensionArray>
-    [np.str_('a'), np.str_('b')]
+    ['a', 'b']
     Length: 2, dtype: str32
 
     Finally, Pandas has arrays that mostly overlap with NumPy