pandas.Series.dropna¶
-
Series.
dropna
(axis=0, inplace=False, **kwargs)[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’}, 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