parameters: (self, index=True, deep=False)
return_statement: return v
docstring: Return the memory usage of the Series.
  
          The memory usage can optionally include the contribution of
          the index and of elements of `object` dtype.
  
          Parameters
          ----------
          index : bool, default True
              Specifies whether to include the memory usage of the Series index.
          deep : bool, default False
              If True, introspect the data deeply by interrogating
              `object` dtypes for system-level memory consumption, and include
              it in the returned value.
  
          Returns
          -------
          int
              Bytes of memory consumed.
  
          See Also
          --------
          numpy.ndarray.nbytes : Total bytes consumed by the elements of the
              array.
          DataFrame.memory_usage : Bytes consumed by a DataFrame.
  
          Examples
          --------
          >>> s = pd.Series(range(3))
          >>> s.memory_usage()
          104
  
          Not including the index gives the size of the rest of the data, which
          is necessarily smaller:
  
          >>> s.memory_usage(index=False)
          24
  
          The memory footprint of `object` values is ignored by default:
  
          >>> s = pd.Series(["a", "b"])
          >>> s.values
          array(['a', 'b'], dtype=object)
          >>> s.memory_usage()
          96
          >>> s.memory_usage(deep=True)
          212
docstring_summary: Return the memory usage of the Series.
docstring_tokens: Return the memory usage of the Series .
function: def memory_usage(self, index=True, deep=False):
          """
          Return the memory usage of the Series.
  
          The memory usage can optionally include the contribution of
          the index and of elements of `object` dtype.
  
          Parameters
          ----------
          index : bool, default True
              Specifies whether to include the memory usage of the Series index.
          deep : bool, default False
              If True, introspect the data deeply by interrogating
              `object` dtypes for system-level memory consumption, and include
              it in the returned value.
  
          Returns
          -------
          int
              Bytes of memory consumed.
  
          See Also
          --------
          numpy.ndarray.nbytes : Total bytes consumed by the elements of the
              array.
          DataFrame.memory_usage : Bytes consumed by a DataFrame.
  
          Examples
          --------
          >>> s = pd.Series(range(3))
          >>> s.memory_usage()
          104
  
          Not including the index gives the size of the rest of the data, which
          is necessarily smaller:
  
          >>> s.memory_usage(index=False)
          24
  
          The memory footprint of `object` values is ignored by default:
  
          >>> s = pd.Series(["a", "b"])
          >>> s.values
          array(['a', 'b'], dtype=object)
          >>> s.memory_usage()
          96
          >>> s.memory_usage(deep=True)
          212
          """
          v = super(Series, self).memory_usage(deep=deep)
          if index:
              v += self.index.memory_usage(deep=deep)
          return v
function_tokens: def memory_usage ( self , index = True , deep = False ) : v = super ( Series , self ) . memory_usage ( deep = deep ) if index : v += self . index . memory_usage ( deep = deep ) return v