pandas.Series.dropna

Series.dropna(axis=0, inplace=False, how=None)

Return a new Series with missing values removed.

See the User Guide 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.

inplacebool, default False

If True, do operation inplace and return None.

howstr, 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

pandas.Series.droplevel pandas.Series.dt