pandas.Index.isna#

final Index.isna()[source]#

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as None, numpy.NaN or pd.NaT, get mapped to True values. Everything else get 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).

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
Float64Index([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])