code: def roc_curve_plot(data, value_column, outcome_column, bootstrap_samples=100, ax=None):
      
      scores = bootstrap_auc(df=data,
                             col=value_column,
                             pred_col=outcome_column,
                             n_bootstrap=bootstrap_samples)
      mean_bootstrap_auc = scores.mean()
      print("{}, Bootstrap (samples = {}) AUC:{}, std={}".format(
          value_column, bootstrap_samples, mean_bootstrap_auc, scores.std()))
  
      outcome = data[outcome_column].astype(int)
      values = data[value_column]
      fpr, tpr, thresholds = roc_curve(outcome, values)
  
      if ax is None:
          ax = plt.gca()
  
      roc_plot = ax.plot(fpr, tpr, lw=1, label=value_column)
  
      ax.set_xlim([-0.05, 1.05])
      ax.set_ylim([-0.05, 1.05])
      ax.set_xlabel('False Positive Rate')
      ax.set_ylabel('True Positive Rate')
      ax.legend(loc=2, borderaxespad=0.)
      ax.set_title('{} ROC Curve (n={})'.format(value_column, len(values)))
  
      return (mean_bootstrap_auc, roc_plot)
docs: Create a ROC curve and compute the bootstrap AUC for the given variable and outcome
  
      Parameters
      ----------
      data : Pandas dataframe
          Dataframe to retrieve information from
      value_column : str
          Column to retrieve the values from
      outcome_column : str
          Column to use as the outcome variable
      bootstrap_samples : int, optional
          Number of bootstrap samples to use to compute the AUC
      ax : Axes, default None
          Axes to plot on
  
      Returns
      -------
      (mean_bootstrap_auc, roc_plot) : (float, matplotlib plot)
          Mean AUC for the given number of bootstrap samples and the plot