code: def bootstrap_fit(
          rv_cont, data, n_iter=10, quant=95, print_params=True, **kwargs
  ):
      
      fit_params = bootstrap_params(rv_cont, data, n_iter)
      par, lo, up = param_describe(fit_params, quant=quant)
      names = param_names(rv_cont)
      maxlen = max([len(s) for s in names])
      print("--------------")
      print(rv_cont.name)
      print("--------------")
      for i, name in enumerate(names):
          print(
              "{nam:>{fill}}: {mean:+.3f} ∈ "
              "[{lo:+.3f}, {up:+.3f}] ({q}%)".format(
                  nam=name,
                  fill=maxlen,
                  mean=par[i],
                  lo=lo[i],
                  up=up[i],
                  q=quant
              )
          )
      out = {
          'mean': par,
          'lower limit': lo,
          'upper limit': up,
      }
      return out
docs: Bootstrap a distribution fit + get confidence intervals for the params.
  
      Parameters
      ==========
      rv_cont: scipy.stats.rv_continuous instance
          The distribution which to fit.
      data: array-like, 1d
          The data on which to fit.
      n_iter: int [default=10]
          Number of bootstrap iterations.
      quant: int [default=95]
          percentile of the confidence limits (default is 95, i.e. 2.5%-97.5%)
      print_params: bool [default=True]
          Print a fit summary.