pandas.Index.isnull¶
-
Index.
isnull
()[source]¶ Detect missing values.
Return a boolean same-sized object indicating if the values are NA. NA values, such as
None
,numpy.NaN
orpd.NaT
, get mapped toTrue
values. Everything else get mapped toFalse
values. Characters such as empty strings ‘’ ornumpy.inf
are not considered NA values (unless you setpandas.options.mode.use_inf_as_na = True
).New in version 0.20.0.
Returns: numpy.ndarray
A boolean array of whether my values are NA
See also
pandas.Index.notna
- boolean inverse of isna.
pandas.Index.dropna
- omit entries with missing values.
pandas.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], dtype=bool)
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], dtype=bool)
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], dtype=bool)