More doc fixes

Author: rhshadrach

pandas/core/arrays/base.py

@@ -941,7 +941,7 @@ def argmin(self, skipna: bool = True) -> int:
         --------
         >>> arr = pd.array([3, 1, 2, 5, 4])
         >>> arr.argmin()
-        1
+        np.int64(1)
         """
         # Implementer note: You have two places to override the behavior of
         # argmin.
@@ -975,7 +975,7 @@ def argmax(self, skipna: bool = True) -> int:
         --------
         >>> arr = pd.array([3, 1, 2, 5, 4])
         >>> arr.argmax()
-        3
+        np.int64(3)
         """
         # Implementer note: You have two places to override the behavior of
         # argmax.
@@ -2182,9 +2182,9 @@ def _reduce(
         >>> pd.array([1, 2, 3])._reduce("sum")
         np.int64(6)
         >>> pd.array([1, 2, 3])._reduce("mean")
-        2.0
+        np.float64(2.0)
         >>> pd.array([1, 2, 3])._reduce("median")
-        2.0
+        np.float64(2.0)
         """
         meth = getattr(self, name, None)
         if meth is None:

pandas/core/arrays/masked.py

@@ -1382,25 +1382,25 @@ def any(
         skips NAs):
 
         >>> pd.array([True, False, True]).any()
-        True
+        np.True_
         >>> pd.array([True, False, pd.NA]).any()
-        True
+        np.True_
         >>> pd.array([False, False, pd.NA]).any()
-        False
+        np.False_
         >>> pd.array([], dtype="boolean").any()
-        False
+        np.False_
         >>> pd.array([pd.NA], dtype="boolean").any()
-        False
+        np.False_
         >>> pd.array([pd.NA], dtype="Float64").any()
-        False
+        np.False_
 
         With ``skipna=False``, the result can be NA if this is logically
         required (whether ``pd.NA`` is True or False influences the result):
 
         >>> pd.array([True, False, pd.NA]).any(skipna=False)
-        True
+        np.True_
         >>> pd.array([1, 0, pd.NA]).any(skipna=False)
-        True
+        np.True_
         >>> pd.array([False, False, pd.NA]).any(skipna=False)
         <NA>
         >>> pd.array([0, 0, pd.NA]).any(skipna=False)
@@ -1490,9 +1490,9 @@ def all(
         >>> pd.array([1, 1, pd.NA]).all(skipna=False)
         <NA>
         >>> pd.array([True, False, pd.NA]).all(skipna=False)
-        False
+        np.False_
         >>> pd.array([1, 0, pd.NA]).all(skipna=False)
-        False
+        np.False_
         """
         nv.validate_all((), kwargs)
 

pandas/core/base.py

@@ -1379,7 +1379,7 @@ def factorize(
         dtype: datetime64[s]
 
         >>> ser.searchsorted('3/14/2000')
-        3
+        np.int64(3)
 
         >>> ser = pd.Categorical(
         ...     ['apple', 'bread', 'bread', 'cheese', 'milk'], ordered=True

pandas/core/construction.py

@@ -186,7 +186,7 @@ def array(
 
     >>> pd.array(["a", "b"], dtype=np.dtype("<U1"))
     <NumpyExtensionArray>
-    ['a', 'b']
+    [np.str_('a'), np.str_('b')]
     Length: 2, dtype: str32
 
     Finally, Pandas has arrays that mostly overlap with NumPy

pandas/core/generic.py

@@ -945,7 +945,7 @@ def squeeze(self, axis: Axis | None = None) -> Scalar | Series | DataFrame:
         Squeezing all axes will project directly into a scalar:
 
         >>> df_0a.squeeze()
-        1
+        np.int64(1)
         """
         axes = range(self._AXIS_LEN) if axis is None else (self._get_axis_number(axis),)
         result = self.iloc[
@@ -7969,7 +7969,7 @@ def asof(self, where, subset=None):
         NaN, even though NaN is at the index location for ``30``.
 
         >>> s.asof(30)
-        2.0
+        np.float64(2.0)
 
         Take all columns into consideration
 

pandas/core/indexing.py

@@ -585,7 +585,7 @@ def loc(self) -> _LocIndexer:
         Single tuple for the index with a single label for the column
 
         >>> df.loc[("cobra", "mark i"), "shield"]
-        2
+        np.int64(2)
 
         Slice from index tuple to single label
 
@@ -677,7 +677,7 @@ def at(self) -> _AtIndexer:
         Get value within a Series
 
         >>> df.loc[5].at["B"]
-        np.int64(5)
+        np.int64(4)
         """
         return _AtIndexer("at", self)