pandas.Series.dropna¶

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