pandas.Series.dropna

Series.dropna(*, axis=0, inplace=False, how=None, ignore_index=False)[source]

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’}

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.

Added in version 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