CLN: @doc - base.py

Author: HH-MWB

pandas/core/arrays/categorical.py

@@ -15,6 +15,7 @@
     Substitution,
     cache_readonly,
     deprecate_kwarg,
+    doc,
 )
 from pandas.util._validators import validate_bool_kwarg, validate_fillna_kwargs
 
@@ -51,7 +52,7 @@
     _extension_array_shared_docs,
     try_cast_to_ea,
 )
-from pandas.core.base import NoNewAttributesMixin, PandasObject, _shared_docs
+from pandas.core.base import IndexOpsMixin, NoNewAttributesMixin, PandasObject
 import pandas.core.common as com
 from pandas.core.construction import array, extract_array, sanitize_array
 from pandas.core.indexers import check_array_indexer, deprecate_ndim_indexing
@@ -1352,8 +1353,7 @@ def memory_usage(self, deep=False):
         """
         return self._codes.nbytes + self.dtype.categories.memory_usage(deep=deep)
 
-    @Substitution(klass="Categorical")
-    @Appender(_shared_docs["searchsorted"])
+    @doc(IndexOpsMixin.searchsorted, klass="Categorical")
     def searchsorted(self, value, side="left", sorter=None):
         # searchsorted is very performance sensitive. By converting codes
         # to same dtype as self.codes, we get much faster performance.

pandas/core/base.py

@@ -13,7 +13,7 @@
 from pandas.compat import PYPY
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
-from pandas.util._decorators import Appender, Substitution, cache_readonly, doc
+from pandas.util._decorators import cache_readonly, doc
 from pandas.util._validators import validate_bool_kwarg
 
 from pandas.core.dtypes.cast import is_nested_object
@@ -1402,26 +1402,26 @@ def memory_usage(self, deep=False):
     def factorize(self, sort=False, na_sentinel=-1):
         return algorithms.factorize(self, sort=sort, na_sentinel=na_sentinel)
 
-    _shared_docs[
-        "searchsorted"
-    ] = """
+    @doc(klass="Index")
+    def searchsorted(self, value, side="left", sorter=None) -> np.ndarray:
+        """
         Find indices where elements should be inserted to maintain order.
 
-        Find the indices into a sorted %(klass)s `self` such that, if the
+        Find the indices into a sorted {klass} `self` such that, if the
         corresponding elements in `value` were inserted before the indices,
         the order of `self` would be preserved.
 
         .. note::
 
-            The %(klass)s *must* be monotonically sorted, otherwise
+            The {klass} *must* be monotonically sorted, otherwise
             wrong locations will likely be returned. Pandas does *not*
             check this for you.
 
         Parameters
         ----------
         value : array_like
             Values to insert into `self`.
-        side : {'left', 'right'}, optional
+        side : {{'left', 'right'}}, optional
             If 'left', the index of the first suitable location found is given.
             If 'right', return the last such index.  If there is no suitable
             index, return either 0 or N (where N is the length of `self`).
@@ -1488,10 +1488,6 @@ def factorize(self, sort=False, na_sentinel=-1):
         >>> x.searchsorted(1)
         0  # wrong result, correct would be 1
         """
-
-    @Substitution(klass="Index")
-    @Appender(_shared_docs["searchsorted"])
-    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", inplace=False):

pandas/core/indexes/datetimelike.py

@@ -10,7 +10,7 @@
 from pandas._libs.tslibs import timezones
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError
-from pandas.util._decorators import Appender, cache_readonly
+from pandas.util._decorators import Appender, cache_readonly, doc
 
 from pandas.core.dtypes.common import (
     ensure_int64,
@@ -31,7 +31,7 @@
 from pandas.core import algorithms
 from pandas.core.arrays import DatetimeArray, PeriodArray, TimedeltaArray
 from pandas.core.arrays.datetimelike import DatetimeLikeArrayMixin
-from pandas.core.base import _shared_docs
+from pandas.core.base import IndexOpsMixin
 import pandas.core.indexes.base as ibase
 from pandas.core.indexes.base import Index, _index_shared_docs
 from pandas.core.indexes.extension import (
@@ -206,7 +206,7 @@ def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs):
             self, indices, axis, allow_fill, fill_value, **kwargs
         )
 
-    @Appender(_shared_docs["searchsorted"])
+    @doc(IndexOpsMixin.searchsorted, klass="Datetime Index")
     def searchsorted(self, value, side="left", sorter=None):
         if isinstance(value, str):
             raise TypeError(

pandas/core/series.py

@@ -2470,8 +2470,7 @@ def __rmatmul__(self, other):
         """
         return self.dot(np.transpose(other))
 
-    @Substitution(klass="Series")
-    @Appender(base._shared_docs["searchsorted"])
+    @doc(base.IndexOpsMixin.searchsorted, klass="Series")
     def searchsorted(self, value, side="left", sorter=None):
         return algorithms.searchsorted(self._values, value, side=side, sorter=sorter)