code: def as_pandas(self, with_metadata=False):
          
          if with_metadata:
              cols = self._discover_meta_cols(**with_metadata) \
                  if isinstance(with_metadata, dict) else self.meta.columns
              return (
                  self.data
                  .set_index(META_IDX)
                  .join(self.meta[cols])
                  .reset_index()
              )
          else:
              return self.data.copy()
docs: Return this as a pd.DataFrame
  
          Parameters
          ----------
          with_metadata : bool, default False or dict
             if True, join data with all meta columns; if a dict, discover
             meaningful meta columns from values (in key-value)