DOC: remove inplace usage from docstring examples

Author: phofl

pandas/core/arrays/categorical.py

@@ -1673,13 +1673,6 @@ def sort_values(
         [5, 2, 2, 1, 1]
         Categories (3, int64): [1, 2, 5]
 
-        Inplace sorting can be done as well:
-
-        >>> c.sort_values(inplace=True)
-        >>> c
-        [1, 1, 2, 2, 5]
-        Categories (3, int64): [1, 2, 5]
-        >>>
         >>> c = pd.Categorical([1, 2, 2, 1, 5])
 
         'sort_values' behaviour with NaNs. Note that 'na_position'

pandas/core/frame.py

@@ -4512,17 +4512,6 @@ def eval(self, expr: str, *, inplace: bool = False, **kwargs) -> Any | None:
         3  4   4
         4  5   2
 
-        Use ``inplace=True`` to modify the original DataFrame.
-
-        >>> df.eval('C = A + B', inplace=True)
-        >>> df
-           A   B   C
-        0  1  10  11
-        1  2   8  10
-        2  3   6   9
-        3  4   4   8
-        4  5   2   7
-
         Multiple columns can be assigned to using multi-line expressions:
 
         >>> df.eval(
@@ -6372,13 +6361,6 @@ def dropna(
                name        toy       born
         1    Batman  Batmobile 1940-04-25
         2  Catwoman   Bullwhip        NaT
-
-        Keep the DataFrame with valid entries in the same variable.
-
-        >>> df.dropna(inplace=True)
-        >>> df
-             name        toy       born
-        1  Batman  Batmobile 1940-04-25
         """
         if (how is not no_default) and (thresh is not no_default):
             raise TypeError(

pandas/core/indexes/base.py

@@ -1745,13 +1745,6 @@ def set_names(
                     ( 'cobra', 2018),
                     ( 'cobra', 2019)],
                    )
-        >>> idx.set_names(['kind', 'year'], inplace=True)
-        >>> idx
-        MultiIndex([('python', 2018),
-                    ('python', 2019),
-                    ( 'cobra', 2018),
-                    ( 'cobra', 2019)],
-                   names=['kind', 'year'])
         >>> idx.set_names('species', level=0)
         MultiIndex([('python', 2018),
                     ('python', 2019),

pandas/core/series.py

@@ -1494,17 +1494,6 @@ def reset_index(
         3    4
         Name: foo, dtype: int64
 
-        To update the Series in place, without generating a new one
-        set `inplace` to True. Note that it also requires ``drop=True``.
-
-        >>> s.reset_index(inplace=True, drop=True)
-        >>> s
-        0    1
-        1    2
-        2    3
-        3    4
-        Name: foo, dtype: int64
-
         The `level` parameter is interesting for Series with a multi-level
         index.
 
@@ -2242,11 +2231,9 @@ def drop_duplicates(
         Name: animal, dtype: object
 
         The value ``False`` for parameter 'keep' discards all sets of
-        duplicated entries. Setting the value of 'inplace' to ``True`` performs
-        the operation inplace and returns ``None``.
+        duplicated entries.
 
-        >>> s.drop_duplicates(keep=False, inplace=True)
-        >>> s
+        >>> s.drop_duplicates(keep=False)
         1       cow
         3    beetle
         5     hippo
@@ -3490,17 +3477,6 @@ def sort_values(
         0     NaN
         dtype: float64
 
-        Sort values inplace
-
-        >>> s.sort_values(ascending=False, inplace=True)
-        >>> s
-        3    10.0
-        4     5.0
-        2     3.0
-        1     1.0
-        0     NaN
-        dtype: float64
-
         Sort values putting NAs first
 
         >>> s.sort_values(na_position='first')
@@ -3750,16 +3726,6 @@ def sort_index(
         1    c
         dtype: object
 
-        Sort Inplace
-
-        >>> s.sort_index(inplace=True)
-        >>> s
-        1    c
-        2    b
-        3    a
-        4    d
-        dtype: object
-
         By default NaNs are put at the end, but use `na_position` to place
         them at the beginning
 
@@ -5601,14 +5567,6 @@ def dropna(
         1    2.0
         dtype: float64
 
-        Keep the Series with valid entries in the same variable.
-
-        >>> ser.dropna(inplace=True)
-        >>> ser
-        0    1.0
-        1    2.0
-        dtype: float64
-
         Empty strings are not considered NA values. ``None`` is considered an
         NA value.