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Classe « Index »

Méthode pandas.Index.isna

Signature de la méthode isna

def isna(self) -> 'npt.NDArray[np.bool_]' 

Description

help(Index.isna)

Detect missing values.

Return a boolean same-sized object indicating if the values are NA.
NA values, such as ``None``, :attr:`numpy.NaN` or :attr:`pd.NaT`, get
mapped to ``True`` values.
Everything else get mapped to ``False`` values. Characters such as
empty strings `''` or :attr:`numpy.inf` are not considered NA values.

Returns
-------
numpy.ndarray[bool]
    A boolean array of whether my values are NA.

See Also
--------
Index.notna : Boolean inverse of isna.
Index.dropna : Omit entries with missing values.
isna : Top-level isna.
Series.isna : Detect missing values in Series object.

Examples
--------
Show which entries in a pandas.Index are NA. The result is an
array.

>>> idx = pd.Index([5.2, 6.0, np.nan])
>>> idx
Index([5.2, 6.0, nan], dtype='float64')
>>> idx.isna()
array([False, False,  True])

Empty strings are not considered NA values. None is considered an NA
value.

>>> idx = pd.Index(['black', '', 'red', None])
>>> idx
Index(['black', '', 'red', None], dtype='object')
>>> idx.isna()
array([False, False, False,  True])

For datetimes, `NaT` (Not a Time) is considered as an NA value.

>>> idx = pd.DatetimeIndex([pd.Timestamp('1940-04-25'),
...                         pd.Timestamp(''), None, pd.NaT])
>>> idx
DatetimeIndex(['1940-04-25', 'NaT', 'NaT', 'NaT'],
              dtype='datetime64[ns]', freq=None)
>>> idx.isna()
array([False,  True,  True,  True])

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