isnull(self) -> 'Series'
Detect missing values.
Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN
, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings ''
or numpy.inf
are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True
).
Mask of bool values for each element in Series that indicates whether an element is an NA value.
Series.isnull is an alias for Series.isna.
Series.dropna
Omit axes labels with missing values.
Series.isnull
Alias of isna.
Series.notna
Boolean inverse of isna.
isna
Top-level isna.
Show which entries in a DataFrame are 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.isna() age born name toy 0 False True False True 1 False False False False 2 True False False False
Show which entries in a Series are 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.isna() 0 False 1 False 2 True dtype: boolSee :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.series.Series.isnull
pandas.core.series.Series.isna
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