def isnull(self) -> 'DataFrame':
    "\n        Detects missing values for items in the current Dataframe.\n\n        Return a boolean same-sized Dataframe indicating if the values are NA.\n        NA values, such as None or numpy.NaN, gets mapped to True values.\n        Everything else gets mapped to False values.\n\n        See Also\n        --------\n        DataFrame.notnull\n\n        Examples\n        --------\n        >>> df = ps.DataFrame([(.2, .3), (.0, None), (.6, None), (.2, .1)])\n        >>> df.isnull()\n               0      1\n        0  False  False\n        1  False   True\n        2  False   True\n        3  False  False\n\n        >>> df = ps.DataFrame([[None, 'bee', None], ['dog', None, 'fly']])\n        >>> df.isnull()\n               0      1      2\n        0   True  False   True\n        1  False   True  False\n        "
    return self._apply_series_op((lambda psser: psser.isnull()))
Detects missing values for items in the current Dataframe.

Return a boolean same-sized Dataframe indicating if the values are NA.
NA values, such as None or numpy.NaN, gets mapped to True values.
Everything else gets mapped to False values.

See Also
--------
DataFrame.notnull

Examples
--------
>>> df = ps.DataFrame([(.2, .3), (.0, None), (.6, None), (.2, .1)])
>>> df.isnull()
       0      1
0  False  False
1  False   True
2  False   True
3  False  False

>>> df = ps.DataFrame([[None, 'bee', None], ['dog', None, 'fly']])
>>> df.isnull()
       0      1      2
0   True  False   True
1  False   True  False
def isnull(self) -> 'DataFrame':
    "\n        Detects missing values for items in the current Dataframe.\n\n        Return a boolean same-sized Dataframe indicating if the values are NA.\n        NA values, such as None or numpy.NaN, gets mapped to True values.\n        Everything else gets mapped to False values.\n\n        See Also\n        --------\n        DataFrame.notnull\n\n        Examples\n        --------\n        >>> df = ps.DataFrame([(.2, .3), (.0, None), (.6, None), (.2, .1)])\n        >>> df.isnull()\n               0      1\n        0  False  False\n        1  False   True\n        2  False   True\n        3  False  False\n\n        >>> df = ps.DataFrame([[None, 'bee', None], ['dog', None, 'fly']])\n        >>> df.isnull()\n               0      1      2\n        0   True  False   True\n        1  False   True  False\n        "
    return self._apply_series_op((lambda psser: psser.isnull()))