def drop(self, labels=None, axis=<NUM_LIT:0>, index=None, columns=None,<EOL>level=None, inplace=False, errors='<STR_LIT>'):
return super().drop(labels=labels, axis=axis, index=index,<EOL>columns=columns, level=level, inplace=inplace,<EOL>errors=errors)<EOL>
Return Series with specified index labels removed.

Remove elements of a Series based on specifying the index labels.
When using a multi-index, labels on different levels can be removed
by specifying the level.

Parameters
----------
labels : single label or list-like
    Index labels to drop.
axis : 0, default 0
    Redundant for application on Series.
index, columns : None
    Redundant for application on Series, but index can be used instead
    of labels.

    .. versionadded:: 0.21.0
level : int or level name, optional
    For MultiIndex, level for which the labels will be removed.
inplace : bool, default False
    If True, do operation inplace and return None.
errors : {'ignore', 'raise'}, default 'raise'
    If 'ignore', suppress error and only existing labels are dropped.

Returns
-------
Series
    Series with specified index labels removed.

Raises
------
KeyError
    If none of the labels are found in the index.

See Also
--------
Series.reindex : Return only specified index labels of Series.
Series.dropna : Return series without null values.
Series.drop_duplicates : Return Series with duplicate values removed.
DataFrame.drop : Drop specified labels from rows or columns.

Examples
--------
>>> s = pd.Series(data=np.arange(3), index=['A', 'B', 'C'])
>>> s
A  0
B  1
C  2
dtype: int64

Drop labels B en C

>>> s.drop(labels=['B', 'C'])
A  0
dtype: int64

Drop 2nd level label in MultiIndex Series

>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> s = pd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
...               index=midx)
>>> s
lama    speed      45.0
        weight    200.0
        length      1.2
cow     speed      30.0
        weight    250.0
        length      1.5
falcon  speed     320.0
        weight      1.0
        length      0.3
dtype: float64

>>> s.drop(labels='weight', level=1)
lama    speed      45.0
        length      1.2
cow     speed      30.0
        length      1.5
falcon  speed     320.0
        length      0.3
dtype: float64