# pandas.Index.isnull

`Index.``isnull`()

  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`).

  New in version 0.20.0.

  Returns:

  numpy.ndarray

  > 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], 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)

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