code: def load_extracted(src_dir: str,
                     patterns="*.npy",
                     vars_in_cols: bool = True,
                     index: pd.Series = None):
      
      def _load(path, index):
          if index is None:
              arr = np.load(str(path))
          else:
              arr = np.load(str(path), mmap_mode="r")[index]
          return arr
  
      src_dir = Path(src_dir)
      paths = []
      if isinstance(patterns, str):
          patterns = [patterns]
      for pat in patterns:
          paths += src_dir.glob(pat)
  
      if vars_in_cols:
          df_data = {}
          for path in paths:
              df_data[path.stem] = _load(path, index)
              df_data = pd.DataFrame(df_data)
              if index is not None:
                  df_data.index = index.index[index]
      else:
          df_data = []
          for path in paths:
              arr = _load(path, index)
              df_data.append(pd.DataFrame(np.expand_dims(arr, 0), index=[path.stem]))
          df_data = pd.concat(df_data)
          if index is not None:
              df_data.columns = index.index[index]
      return df_data
docs: Load data extracted and stored by :py:func:`extract`
  
      Arguments:
          src_dir {str} -- The directory where the data is stored.
  
      Keyword Arguments:
          patterns {str, or list of str} -- A pattern (str) or list of patterns (list)
              to identify the variables to be loaded.
              The default loads all variables, i.e. all .npy files. (default: {'*.npy'})
          vars_in_cols {bool} -- Return the variables in columns (``True``) or rows ``False``
              (default: {True})
          index {pd.Series} -- A boolean pandas Series which indicates with ``True`` which samples to
              load.
  
      Returns:
          pandas.DataFrame -- A dataframe with the data.