# pandas.isnull

`pandas.``isnull`(obj)

  Detect missing values for an array-like object.

  This function takes a scalar or array-like object and indicates whether values are missing (`NaN` in numeric arrays, `None` or `NaN` in object arrays, `NaT` in datetimelike).

  Parameters:

  obj : scalar or array-like

  > Object to check for null or missing values.

  Returns:

  bool or array-like of bool

  > For scalar input, returns a scalar boolean. For array input, returns an array of boolean indicating whether each corresponding element is missing.

  See also

  `notna`
    Boolean inverse of pandas.isna.
  `Series.isna`
    Detect missing values in a Series.
  `DataFrame.isna`
    Detect missing values in a DataFrame.
  `Index.isna`
    Detect missing values in an Index.

  Examples

  Scalar arguments (including strings) result in a scalar boolean.

      >>> pd.isna('dog')
      False

      >>> pd.isna(np.nan)
      True

  ndarrays result in an ndarray of booleans.

      >>> array = np.array([[1, np.nan, 3], [4, 5, np.nan]])
      >>> array
      array([[ 1., nan,  3.],
             [ 4.,  5., nan]])
      >>> pd.isna(array)
      array([[False,  True, False],
             [False, False,  True]])

  For indexes, an ndarray of booleans is returned.

      >>> index = pd.DatetimeIndex(["2017-07-05", "2017-07-06", None,
      ...                           "2017-07-08"])
      >>> index
      DatetimeIndex(['2017-07-05', '2017-07-06', 'NaT', '2017-07-08'],
                    dtype='datetime64[ns]', freq=None)
      >>> pd.isna(index)
      array([False, False,  True, False])

  For Series and DataFrame, the same type is returned, containing booleans.

      >>> df = pd.DataFrame([['ant', 'bee', 'cat'], ['dog', None, 'fly']])
      >>> df
           0     1    2
      0  ant   bee  cat
      1  dog  None  fly
      >>> pd.isna(df)
             0      1      2
      0  False  False  False
      1  False   True  False

      >>> pd.isna(df[1])
      0    False
      1     True
      Name: 1, dtype: bool

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