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Classe « Series »

Méthode pandas.Series.dropna

Signature de la méthode dropna

def dropna(self, *, axis: 'Axis' = 0, inplace: 'bool' = False, how: 'AnyAll | None' = None, ignore_index: 'bool' = False) -> 'Series | None' 

Description

help(Series.dropna)

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'}
    Unused. Parameter needed for compatibility with DataFrame.
inplace : bool, default False
    If True, do operation inplace and return None.
how : str, optional
    Not in use. Kept for compatibility.
ignore_index : bool, default ``False``
    If ``True``, the resulting axis will be labeled 0, 1, ..., n - 1.

    .. versionadded:: 2.0.0

Returns
-------
Series or None
    Series with NA entries dropped from it or None if ``inplace=True``.

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

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

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