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.
- inplacebool, default False
If True, do operation inplace and return None.
- howstr, optional
Not in use. Kept for compatibility.
- ignore_indexbool, 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