pandas.Series.notnull — pandas 0.24.0rc1 documentation (original) (raw)
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or numpy.NaN, get mapped to False values.
| Returns: | Series Mask of bool values for each element in Series that indicates whether an element is not an NA value. |
|---|
Examples
Show which entries in a DataFrame are not NA.
df = pd.DataFrame({'age': [5, 6, np.NaN], ... 'born': [pd.NaT, pd.Timestamp('1939-05-27'), ... pd.Timestamp('1940-04-25')], ... 'name': ['Alfred', 'Batman', ''], ... 'toy': [None, 'Batmobile', 'Joker']}) df age born name toy 0 5.0 NaT Alfred None 1 6.0 1939-05-27 Batman Batmobile 2 NaN 1940-04-25 Joker
df.notna() age born name toy 0 True False True False 1 True True True True 2 False True True True
Show which entries in a Series are not NA.
ser = pd.Series([5, 6, np.NaN]) ser 0 5.0 1 6.0 2 NaN dtype: float64
ser.notna() 0 True 1 True 2 False dtype: bool