Classe « DataFrame »

Méthode pandas.DataFrame.notna

Signature de la méthode notna

def notna(self) -> 'DataFrame' 

Description

notna.__doc__

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 :attr:`numpy.inf` are not considered NA values
(unless you set ``pandas.options.mode.use_inf_as_na = True``).
NA values, such as None or :attr:`numpy.NaN`, get mapped to False
values.

Returns
-------
DataFrame
    Mask of bool values for each element in DataFrame that
    indicates whether an element is not an NA value.

See Also
--------
DataFrame.notnull : Alias of notna.
DataFrame.isna : Boolean inverse of notna.
DataFrame.dropna : Omit axes labels with missing values.
notna : Top-level notna.

Examples
--------
Show which entries in a DataFrame are not NA.

>>> df = pd.DataFrame(dict(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