parameters: (self, data, index=None, dtype=None)
return_statement: return s._data, s.index
docstring: Derive the "_data" and "index" attributes of a new Series from a
          dictionary input.
  
          Parameters
          ----------
          data : dict or dict-like
              Data used to populate the new Series.
          index : Index or index-like, default None
              Index for the new Series: if None, use dict keys.
          dtype : dtype, default None
              The dtype for the new Series: if None, infer from data.
  
          Returns
          -------
          _data : BlockManager for the new Series
          index : index for the new Series
docstring_summary: Derive the "_data" and "index" attributes of a new Series from a
          dictionary input.
docstring_tokens: Derive the _data and index attributes of a new Series from a dictionary input .
function: def _init_dict(self, data, index=None, dtype=None):
          """
          Derive the "_data" and "index" attributes of a new Series from a
          dictionary input.
  
          Parameters
          ----------
          data : dict or dict-like
              Data used to populate the new Series.
          index : Index or index-like, default None
              Index for the new Series: if None, use dict keys.
          dtype : dtype, default None
              The dtype for the new Series: if None, infer from data.
  
          Returns
          -------
          _data : BlockManager for the new Series
          index : index for the new Series
          """
          # Looking for NaN in dict doesn't work ({np.nan : 1}[float('nan')]
          # raises KeyError), so we iterate the entire dict, and align
          if data:
              keys, values = zip(*data.items())
              values = list(values)
          elif index is not None:
              # fastpath for Series(data=None). Just use broadcasting a scalar
              # instead of reindexing.
              values = na_value_for_dtype(dtype)
              keys = index
          else:
              keys, values = [], []
  
          # Input is now list-like, so rely on "standard" construction:
  
          # TODO: passing np.float64 to not break anything yet. See GH-17261
          s = create_series_with_explicit_dtype(
              values, index=keys, dtype=dtype, dtype_if_empty=np.float64
          )
  
          # Now we just make sure the order is respected, if any
          if data and index is not None:
              s = s.reindex(index, copy=False)
          return s._data, s.index
function_tokens: def _init_dict ( self , data , index = None , dtype = None ) : # Looking for NaN in dict doesn't work ({np.nan : 1}[float('nan')] # raises KeyError), so we iterate the entire dict, and align if data : keys , values = zip ( * data . items ( ) ) values = list ( values ) elif index is not None : # fastpath for Series(data=None). Just use broadcasting a scalar # instead of reindexing. values = na_value_for_dtype ( dtype ) keys = index else : keys , values = [ ] , [ ] # Input is now list-like, so rely on "standard" construction: # TODO: passing np.float64 to not break anything yet. See GH-17261 s = create_series_with_explicit_dtype ( values , index = keys , dtype = dtype , dtype_if_empty = np . float64 ) # Now we just make sure the order is respected, if any if data and index is not None : s = s . reindex ( index , copy = False ) return s . _data , s . index