notna(self) -> 'Series'
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.
Mask of bool values for each element in Series that indicates whether an element is not an NA value.
Detect existing (non-missing) values.
Series.dropna
Omit axes labels with missing values.
Series.isna
Boolean inverse of notna.
Series.notnull
Alias of notna.
notna
Top-level notna.
Show which entries in a DataFrame are not NA.
This example is valid syntax, but we were not able to check execution>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],This example is valid syntax, but we were not able to check execution
... 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.
This example is valid syntax, but we were not able to check execution>>> ser = pd.Series([5, 6, np.NaN])This example is valid syntax, but we were not able to check execution
... ser 0 5.0 1 6.0 2 NaN dtype: float64
>>> ser.notna() 0 True 1 True 2 False dtype: boolSee :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.series.Series.dropna
pandas.core.dtypes.missing.notna
pandas.core.series.Series.isnull
pandas.core.series.Series.notnull
pandas.core.series.Series.isna
pandas.core.series.Series.notna
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