parameters: (self)
return_statement: return self.size == 0
docstring: Indicator whether DataFrame or Series is empty.
  
          True if DataFrame/Series is entirely empty (no items),
          meaning any of the axes are of length 0.
  
          Returns
          -------
          out : bool
              If DataFrame/Series is empty, return True, if not return False.
  
          Notes
          -----
          If DataFrame/Series contains only `null` values, it is still not
          considered empty. See the example below.
  
          Examples
          --------
          >>> import cudf
          >>> df = cudf.DataFrame({'A' : []})
          >>> df
          Empty DataFrame
          Columns: [A]
          Index: []
          >>> df.empty
          True
  
          If we only have `null` values in our DataFrame, it is
          not considered empty! We will need to drop
          the `null`'s to make the DataFrame empty:
  
          >>> df = cudf.DataFrame({'A' : [None, None]})
          >>> df
                A
          0  <NA>
          1  <NA>
          >>> df.empty
          False
          >>> df.dropna().empty
          True
  
          Non-empty and empty Series example:
  
          >>> s = cudf.Series([1, 2, None])
          >>> s
          0       1
          1       2
          2    <NA>
          dtype: int64
          >>> s.empty
          False
          >>> s = cudf.Series([])
          >>> s
          Series([], dtype: float64)
          >>> s.empty
          True
docstring_summary: Indicator whether DataFrame or Series is empty.
docstring_tokens: Indicator whether DataFrame or Series is empty .
function: def empty(self):
          """
          Indicator whether DataFrame or Series is empty.
  
          True if DataFrame/Series is entirely empty (no items),
          meaning any of the axes are of length 0.
  
          Returns
          -------
          out : bool
              If DataFrame/Series is empty, return True, if not return False.
  
          Notes
          -----
          If DataFrame/Series contains only `null` values, it is still not
          considered empty. See the example below.
  
          Examples
          --------
          >>> import cudf
          >>> df = cudf.DataFrame({'A' : []})
          >>> df
          Empty DataFrame
          Columns: [A]
          Index: []
          >>> df.empty
          True
  
          If we only have `null` values in our DataFrame, it is
          not considered empty! We will need to drop
          the `null`'s to make the DataFrame empty:
  
          >>> df = cudf.DataFrame({'A' : [None, None]})
          >>> df
                A
          0  <NA>
          1  <NA>
          >>> df.empty
          False
          >>> df.dropna().empty
          True
  
          Non-empty and empty Series example:
  
          >>> s = cudf.Series([1, 2, None])
          >>> s
          0       1
          1       2
          2    <NA>
          dtype: int64
          >>> s.empty
          False
          >>> s = cudf.Series([])
          >>> s
          Series([], dtype: float64)
          >>> s.empty
          True
          """
          return self.size == 0
function_tokens: def empty ( self ) : return self . size == 0