REF: IndexOpsMixin wrapping (#36848)

Author: jbrockmendel

pandas/core/base.py

@@ -4,7 +4,7 @@
 
 import builtins
 import textwrap
-from typing import Any, Callable, Dict, FrozenSet, List, Optional, Union, cast
+from typing import Any, Callable, Dict, FrozenSet, List, Optional, TypeVar, Union, cast
 
 import numpy as np
 
@@ -43,6 +43,8 @@
     duplicated="IndexOpsMixin",
 )
 
+_T = TypeVar("_T", bound="IndexOpsMixin")
+
 
 class PandasObject(DirNamesMixin):
     """
@@ -604,7 +606,7 @@ def _values(self) -> Union[ExtensionArray, np.ndarray]:
         # must be defined here as a property for mypy
         raise AbstractMethodError(self)
 
-    def transpose(self, *args, **kwargs):
+    def transpose(self: _T, *args, **kwargs) -> _T:
         """
         Return the transpose, which is by definition self.
 
@@ -851,10 +853,10 @@ def to_numpy(self, dtype=None, copy=False, na_value=lib.no_default, **kwargs):
         return result
 
     @property
-    def empty(self):
+    def empty(self) -> bool:
         return not self.size
 
-    def max(self, axis=None, skipna=True, *args, **kwargs):
+    def max(self, axis=None, skipna: bool = True, *args, **kwargs):
         """
         Return the maximum value of the Index.
 
@@ -899,7 +901,7 @@ def max(self, axis=None, skipna=True, *args, **kwargs):
         return nanops.nanmax(self._values, skipna=skipna)
 
     @doc(op="max", oppose="min", value="largest")
-    def argmax(self, axis=None, skipna=True, *args, **kwargs):
+    def argmax(self, axis=None, skipna: bool = True, *args, **kwargs) -> int:
         """
         Return int position of the {value} value in the Series.
 
@@ -954,7 +956,7 @@ def argmax(self, axis=None, skipna=True, *args, **kwargs):
         nv.validate_argmax_with_skipna(skipna, args, kwargs)
         return nanops.nanargmax(self._values, skipna=skipna)
 
-    def min(self, axis=None, skipna=True, *args, **kwargs):
+    def min(self, axis=None, skipna: bool = True, *args, **kwargs):
         """
         Return the minimum value of the Index.
 
@@ -999,7 +1001,7 @@ def min(self, axis=None, skipna=True, *args, **kwargs):
         return nanops.nanmin(self._values, skipna=skipna)
 
     @doc(argmax, op="min", oppose="max", value="smallest")
-    def argmin(self, axis=None, skipna=True, *args, **kwargs):
+    def argmin(self, axis=None, skipna=True, *args, **kwargs) -> int:
         nv.validate_minmax_axis(axis)
         nv.validate_argmax_with_skipna(skipna, args, kwargs)
         return nanops.nanargmin(self._values, skipna=skipna)
@@ -1054,6 +1056,9 @@ def hasnans(self):
         """
         return bool(isna(self).any())
 
+    def isna(self):
+        return isna(self._values)
+
     def _reduce(
         self,
         op,
@@ -1161,7 +1166,12 @@ def map_f(values, f):
         return new_values
 
     def value_counts(
-        self, normalize=False, sort=True, ascending=False, bins=None, dropna=True
+        self,
+        normalize: bool = False,
+        sort: bool = True,
+        ascending: bool = False,
+        bins=None,
+        dropna: bool = True,
     ):
         """
         Return a Series containing counts of unique values.
@@ -1500,20 +1510,9 @@ def searchsorted(self, value, side="left", sorter=None) -> np.ndarray:
         return algorithms.searchsorted(self._values, value, side=side, sorter=sorter)
 
     def drop_duplicates(self, keep="first"):
-        if isinstance(self, ABCIndexClass):
-            if self.is_unique:
-                return self._shallow_copy()
-
         duplicated = self.duplicated(keep=keep)
         result = self[np.logical_not(duplicated)]
         return result
 
     def duplicated(self, keep="first"):
-        if isinstance(self, ABCIndexClass):
-            if self.is_unique:
-                return np.zeros(len(self), dtype=bool)
-            return duplicated(self, keep=keep)
-        else:
-            return self._constructor(
-                duplicated(self, keep=keep), index=self.index
-            ).__finalize__(self, method="duplicated")
+        return duplicated(self._values, keep=keep)

pandas/core/indexes/base.py

@@ -2366,6 +2366,9 @@ def drop_duplicates(self, keep="first"):
         >>> idx.drop_duplicates(keep=False)
         Index(['cow', 'beetle', 'hippo'], dtype='object')
         """
+        if self.is_unique:
+            return self._shallow_copy()
+
         return super().drop_duplicates(keep=keep)
 
     def duplicated(self, keep="first"):
@@ -2422,6 +2425,9 @@ def duplicated(self, keep="first"):
         >>> idx.duplicated(keep=False)
         array([ True, False,  True, False,  True])
         """
+        if self.is_unique:
+            # fastpath available bc we are immutable
+            return np.zeros(len(self), dtype=bool)
         return super().duplicated(keep=keep)
 
     def _get_unique_index(self, dropna: bool = False):

pandas/core/series.py

@@ -2053,7 +2053,9 @@ def duplicated(self, keep="first") -> "Series":
         4     True
         dtype: bool
         """
-        return super().duplicated(keep=keep)
+        res = base.IndexOpsMixin.duplicated(self, keep=keep)
+        result = self._constructor(res, index=self.index)
+        return result.__finalize__(self, method="duplicated")
 
     def idxmin(self, axis=0, skipna=True, *args, **kwargs):
         """
@@ -4776,7 +4778,7 @@ def _convert_dtypes(
 
     @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
     def isna(self) -> "Series":
-        return super().isna()
+        return generic.NDFrame.isna(self)
 
     @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
     def isnull(self) -> "Series":