DOC: fix EX03 errors in docstrings - pandas.Index.rename, pandas.Index.isin, pandas.IndexSlice (#56870)

Co-authored-by: Marc Garcia <garcia.marc@gmail.com>

Author: jordan-d-murphy

ci/code_checks.sh

@@ -89,12 +89,9 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.DataFrame.to_sql \
         pandas.read_stata \
         pandas.plotting.scatter_matrix \
-        pandas.Index.rename \
         pandas.Index.droplevel \
-        pandas.Index.isin \
         pandas.MultiIndex.names \
         pandas.MultiIndex.droplevel \
-        pandas.IndexSlice \
         pandas.Grouper \
         pandas.io.formats.style.Styler.map \
         pandas.io.formats.style.Styler.apply_index \

pandas/core/indexes/base.py

@@ -1953,7 +1953,7 @@ def rename(self, name, inplace: bool = False) -> Self | None:
 
         >>> idx = pd.MultiIndex.from_product([['python', 'cobra'],
         ...                                   [2018, 2019]],
-        ...                                   names=['kind', 'year'])
+        ...                                  names=['kind', 'year'])
         >>> idx
         MultiIndex([('python', 2018),
                     ('python', 2019),
@@ -6575,7 +6575,7 @@ def isin(self, values, level=None) -> npt.NDArray[np.bool_]:
 
         Examples
         --------
-        >>> idx = pd.Index([1,2,3])
+        >>> idx = pd.Index([1, 2, 3])
         >>> idx
         Index([1, 2, 3], dtype='int64')
 
@@ -6584,7 +6584,7 @@ def isin(self, values, level=None) -> npt.NDArray[np.bool_]:
         >>> idx.isin([1, 4])
         array([ True, False, False])
 
-        >>> midx = pd.MultiIndex.from_arrays([[1,2,3],
+        >>> midx = pd.MultiIndex.from_arrays([[1, 2, 3],
         ...                                  ['red', 'blue', 'green']],
         ...                                  names=('number', 'color'))
         >>> midx

pandas/core/indexing.py

@@ -121,7 +121,7 @@ class _IndexSlice:
 
     Examples
     --------
-    >>> midx = pd.MultiIndex.from_product([['A0','A1'], ['B0','B1','B2','B3']])
+    >>> midx = pd.MultiIndex.from_product([['A0', 'A1'], ['B0', 'B1', 'B2', 'B3']])
     >>> columns = ['foo', 'bar']
     >>> dfmi = pd.DataFrame(np.arange(16).reshape((len(midx), len(columns))),
     ...                     index=midx, columns=columns)