docs: def compare(eq_dfs, columns=None, selection='Adj Close'):
      """
      Get the relative performance of multiple equities.
  
      .. versionadded:: 0.5.0
  
      Parameters
      ----------
      eq_dfs : list or tuple of DataFrame
          Performance data for multiple equities over
          a consistent time frame.
      columns : iterable of str, default None
          Labels to use for the columns of the output DataFrame.
          The labels, if provided, should normally be the names
          of the equities whose performance is being compared.
      selection : str, default 'Adj Close'
          Column containing prices to be compared. Defaults
          to 'Adj Close'.
  
      Returns
      -------
      rel_perf : DataFrame
          A DataFrame whose columns contain normalized data
          for each equity represented in `eq_dfs`. The initial
          price for each equity will be normalized to 1.0.
  
      Examples
      --------
  
      .. code-block:: python
  
          import pynance as pn
  
          eqs = ('FSLR', 'SCTY', 'SPWR')
          eq_dfs = []
          for eq in eqs:
              eq_dfs.append(pn.data.get(eq, '2016'))
          rel_perf = pn.data.compare(eq_dfs, eqs)
  
      Notes
      -----
      Each set of data passed in `eq_dfs` is assumed to have
      the same start and end dates as the other data sets.
      """
      content = np.empty((eq_dfs[0].shape[0], len(eq_dfs)), dtype=np.float64)
      rel_perf = pd.DataFrame(content, eq_dfs[0].index, columns, dtype=np.float64)
      for i in range(len(eq_dfs)):
          rel_perf.iloc[:, i] = eq_dfs[i].loc[:, selection] / eq_dfs[i].iloc[0].loc[selection]
      return rel_perf