Classe « Series »

Méthode pandas.Series.dropna

Signature de la méthode dropna

def dropna(self, axis=0, inplace=False, how=None) 

Description

dropna.__doc__

        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.
        how : str, optional
            Not in use. Kept for compatibility.

        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

        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