change example as suggested

[rafarui]

Co-authored-by: @marielenferreira

Checklist for the pandas documentation sprint (ignore this if you are doing
an unrelated PR):

• PR title is "DOC: update the docstring"
• The validation script passes: scripts/validate_docstrings.py <your-function-or-method>
• The PEP8 style check passes: git diff upstream/master -u -- "*.py" | flake8 --diff
• The html version looks good: python doc/make.py --single <your-function-or-method>
• It has been proofread on language by another sprint participant

Please include the output of the validation script below between the "```" ticks:

################################################################################
############## Docstring (pandas.core.series.Series.sort_values)  ##############
################################################################################

Sort by the Series values.

Sort (or order) a Series in ascending or descending order by some
criterion.

Parameters
----------
axis : {0 or ‘index’}, default 0
    Axis to direct sorting. The value `index` is accepted for
    compatibility with DataFrame.sort_values.
ascending : bool, default True
    If  `True` sort values in ascending order, otherwise descending.
inplace : bool, default False
    If True, perform operation in-place.
kind : {‘quicksort’, ‘mergesort’ or ‘heapsort’}, default ‘quicksort’
    Choice of sorting algorithm. See also :func:`np.sort` for more
     information. `mergesort` is the only stable  algorithm.
na_position : {'first' or 'last'}, default 'last'
     Argument `first` puts NaNs at the beginning, `last` puts NaNs at
     the end.

Returns
-------
Series
    Series ordered by values.

See Also
--------
Series.sort_index : Sort by the Series indices.
DataFrame.sort_index : Sort DataFrame by indices.
DataFrame.sort_values : Sort by the values along either axis.

Examples
--------
>>> s = pd.Series([np.nan, 1, 3, 10, 5])
>>> s
0     NaN
1     1.0
2     3.0
3     10.0
4     5.0
dtype: float64

Sort values ascending order (default behaviour)

>>> s.sort_values(ascending=True)
1     1.0
2     3.0
4     5.0
3    10.0
0     NaN
dtype: float64

Sort values descending order

>>> s.sort_values(ascending=False)
3    10.0
4     5.0
2     3.0
1     1.0
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')
0     NaN
1     1.0
2     3.0
4     5.0
3    10.0
dtype: float64

Sort a series of strings

>>> s = pd.Series(['z', 'b', 'd', 'a', 'c'])
>>> s
0    z
1    b
2    d
3    a
4    c
dtype: object

>>> s.sort_values()
3    a
1    b
4    c
2    d
0    z
dtype: object

################################################################################
################################## Validation ##################################
################################################################################

Docstring for "pandas.core.series.Series.sort_values" correct. :)

If the validation script still gives errors, but you think there is a good reason
to deviate in this case (and there are certainly such cases), please state this
explicitly.