def dropna(self, axis=<NUM_LIT:0>, inplace=False, **kwargs):
inplace = validate_bool_kwarg(inplace, '<STR_LIT>')<EOL>kwargs.pop('<STR_LIT>', None)<EOL>if kwargs:<EOL><INDENT>raise TypeError('<STR_LIT>'<EOL>'<STR_LIT>'.format(list(kwargs.keys())[<NUM_LIT:0>]))<EOL><DEDENT>self._get_axis_number(axis or <NUM_LIT:0>)<EOL>if self._can_hold_na:<EOL><INDENT>result = remove_na_arraylike(self)<EOL>if inplace:<EOL><INDENT>self._update_inplace(result)<EOL><DEDENT>else:<EOL><INDENT>return result<EOL><DEDENT><DEDENT>else:<EOL><INDENT>if inplace:<EOL><INDENT>pass<EOL><DEDENT>else:<EOL><INDENT>return self.copy()<EOL><DEDENT><DEDENT>
Return a new Series with missing values removed.

See the :ref:`User Guide <missing_data>` for more on which values are
considered missing, and how to work with missing data.

Parameters
----------
axis : {0 or 'index'}, default 0
    There is only one axis to drop values from.
inplace : bool, default False
    If True, do operation inplace and return None.
**kwargs
    Not in use.

Returns
-------
Series
    Series with NA entries dropped from it.

See Also
--------
Series.isna: Indicate missing values.
Series.notna : Indicate existing (non-missing) values.
Series.fillna : Replace missing values.
DataFrame.dropna : Drop rows or columns which contain NA values.
Index.dropna : Drop missing indices.

Examples
--------
>>> ser = pd.Series([1., 2., np.nan])
>>> ser
0    1.0
1    2.0
2    NaN
dtype: float64

Drop NA values from a Series.

>>> ser.dropna()
0    1.0
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.

>>> ser = pd.Series([np.NaN, 2, pd.NaT, '', None, 'I stay'])
>>> ser
0       NaN
1         2
2       NaT
3
4      None
5    I stay
dtype: object
>>> ser.dropna()
1         2
3
5    I stay
dtype: object