notnull(self) -> 'DataFrame'
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.
Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.
DataFrame.notnull is an alias for DataFrame.notna.
DataFrame.dropna
Omit axes labels with missing values.
DataFrame.isna
Boolean inverse of notna.
DataFrame.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.frame.DataFrame.notnull
pandas.core.frame.DataFrame.notna
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