DOC: Replace @appender decorator with inline docstrings for Index.take, Index.repeat, and Index.get_indexer_non_unique

Author: mateomramos

pandas/core/indexes/base.py

@@ -1170,6 +1170,52 @@ def astype(self, dtype: Dtype, copy: bool = True):
             result._references.add_index_reference(result)
         return result
 
+    _index_shared_docs["take"] = """
+        Return a new %(klass)s of the values selected by the indices.
+
+        For internal compatibility with numpy arrays.
+
+        Parameters
+        ----------
+        indices : array-like
+            Indices to be taken.
+        axis : int, optional
+            The axis over which to select values, always 0.
+        allow_fill : bool, default True
+            How to handle negative values in `indices`.
+
+            * False: negative values in `indices` indicate positional indices
+                from the right (the default). This is similar to
+                :func:`numpy.take`.
+
+            * True: negative values in `indices` indicate
+                missing values. These values are set to `fill_value`. Any other
+                other negative values raise a ``ValueError``.
+
+        fill_value : scalar, default None
+            If allow_fill=True and fill_value is not None, indices specified by
+            -1 are regarded as NA. If Index doesn't hold NA, raise ValueError.
+        **kwargs
+            Required for compatibility with numpy.
+
+        Returns
+        -------
+        Index
+            An index formed of elements at the given indices. Will be the same
+            type as self, except for RangeIndex.
+
+        See Also
+        --------
+        numpy.ndarray.take: Return an array formed from the
+            elements of a at the given indices.
+
+        Examples
+        --------
+        >>> idx = pd.Index(['a', 'b', 'c'])
+        >>> idx.take([2, 2, 1, 2])
+        Index(['c', 'c', 'b', 'c'], dtype='str')
+        """
+
     def take(
         self,
         indices,
@@ -5936,6 +5982,59 @@ def _should_fallback_to_positional(self) -> bool:
             "complex",
         }
 
+    _index_shared_docs["get_indexer_non_unique"] = """
+        Compute indexer and mask for new index given the current index.
+
+        The indexer should be then used as an input to ndarray.take to align the
+        current data to the new index.
+
+        Parameters
+        ----------
+        target : %(target_klass)s
+            An iterable containing the values to be used for computing indexer.
+
+        Returns
+        -------
+        indexer : np.ndarray[np.intp]
+            Integers from 0 to n - 1 indicating that the index at these
+            positions matches the corresponding target values. Missing values
+            in the target are marked by -1.
+        missing : np.ndarray[np.intp]
+            An indexer into the target of the values not found.
+            These correspond to the -1 in the indexer array.
+
+        See Also
+        --------
+        Index.get_indexer : Computes indexer and mask for new index given
+            the current index.
+        Index.get_indexer_for : Returns an indexer even when non-unique.
+
+        Examples
+        --------
+        >>> index = pd.Index(['c', 'b', 'a', 'b', 'b'])
+        >>> index.get_indexer_non_unique(['b', 'b'])
+        (array([1, 3, 4, 1, 3, 4]), array([], dtype=int64))
+
+        In the example below there are no matched values.
+
+        >>> index = pd.Index(['c', 'b', 'a', 'b', 'b'])
+        >>> index.get_indexer_non_unique(['q', 'r', 't'])
+        (array([-1, -1, -1]), array([0, 1, 2]))
+
+        For this reason, the returned ``indexer`` contains only integers equal to -1.
+        It demonstrates that there's no match between the index and the ``target``
+        values at these positions. The mask [0, 1, 2] in the return value shows that
+        the first, second, and third elements are missing.
+
+        Notice that the return value is a tuple contains two items. In the example
+        below the first item is an array of locations in ``index``. The second
+        item is a mask shows that the first and third elements are missing.
+
+        >>> index = pd.Index(['c', 'b', 'a', 'b', 'b'])
+        >>> index.get_indexer_non_unique(['f', 'b', 's'])
+        (array([-1,  1,  3,  4, -1]), array([0, 2]))
+        """
+
     def get_indexer_non_unique(
         self, target
     ) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]: