docstrings, extending docs

Author: andrewgsavage

doc/source/development/extending.rst

@@ -185,12 +185,9 @@ Implement the following:
 
         def _validate_setitem_value(self, value):
             if pandas.api.types.is_list_like(value):
-                return [self._validate_scalar(v) for v in value]
+                return np.array([self._validate_scalar(v) for v in value], dtype = self.dtype)
             return self._validate_scalar(value)
 
-        def _validate_searchsorted_value(self, value):
-            return self._validate_setitem_value(value)
-
 
 To support 2D arrays, use the ``_from_backing_data`` helper function when a
 method is called on multi-dimensional data of the same dtype as ``_ndarray``.

pandas/core/arrays/_mixins.py

@@ -92,6 +92,12 @@ def method(self, *args, **kwargs):
 class NDArrayBackedExtensionArray(NDArrayBacked, ExtensionArray):
     """
     ExtensionArray that is backed by a single NumPy ndarray.
+
+    Examples
+    --------
+    Please see the following:
+
+    https://pandas.pydata.org/docs/development/extending.html#NDArrayBackedExtensionArray
     """
 
     _ndarray: np.ndarray
@@ -442,21 +448,8 @@ def _where(self: Self, mask: npt.NDArray[np.bool_], value) -> Self:
     # ------------------------------------------------------------------------
     # Index compat methods
 
+    @doc(ExtensionArray.insert)
     def insert(self, loc: int, item) -> Self:
-        """
-        Make new ExtensionArray inserting new item at location.
-
-        Follows Python list.append semantics for negative values.
-
-        Parameters
-        ----------
-        loc : int
-        item : object
-
-        Returns
-        -------
-        type(self)
-        """
         loc = validate_insert_loc(loc, len(self))
 
         code = self._validate_scalar(item)
@@ -475,19 +468,8 @@ def insert(self, loc: int, item) -> Self:
     #  These are not part of the EA API, but we implement them because
     #  pandas assumes they're there.
 
+    @doc(ExtensionArray.value_counts)
     def value_counts(self, dropna: bool = True) -> Series:
-        """
-        Return a Series containing counts of unique values.
-
-        Parameters
-        ----------
-        dropna : bool, default True
-            Don't include counts of NA values.
-
-        Returns
-        -------
-        Series
-        """
         if self.ndim != 1:
             raise NotImplementedError
 

pandas/tests/api/test_api.py

@@ -357,6 +357,7 @@ def test_api_indexers(self):
     def test_api_extensions(self):
         self.check(api_extensions, self.allowed_api_extensions)
 
+
 class TestTesting(Base):
     funcs = [
         "assert_frame_equal",