def createMeanComparison()

in lib/report.py [0:0]

• 42 lines of code
• 5 McCabe index (conditional complexity)

  def createMeanComparison(self, segment, metric, metric_type):
    t = get_template("mean.html")

    datasets = []
    control=self.data["branches"][0]
      
    for branch in self.data["branches"]:
      n = int(self.data[branch][segment][metric_type][metric]["n"])
      n = f'{n:,}'
      mean = "{0:.1f}".format(self.data[branch][segment][metric_type][metric]["mean"])

      if branch != control:
        branch_mean = self.data[branch][segment][metric_type][metric]["mean"]
        control_mean = self.data[control][segment][metric_type][metric]["mean"]
        uplift = (branch_mean-control_mean)/control_mean*100.0
        uplift = "{0:.1f}".format(uplift)
      else:
        uplift = ""

      se   = "{0:.1f}".format(self.data[branch][segment][metric_type][metric]["se"])
      std  = "{0:.1f}".format(self.data[branch][segment][metric_type][metric]["std"])

      dataset = {
          "branch": branch,
          "mean": mean,
          "uplift": uplift,
          "n": n,
          "se": se,
          "std": std,
          "control": branch==control
      }
      
      if branch != control:
        for test in self.data[branch][segment][metric_type][metric]["tests"]:
          effect = "{0:.2f}".format(self.data[branch][segment][metric_type][metric]["tests"][test]["effect"])
          pval = "{0:.2g}".format(self.data[branch][segment][metric_type][metric]["tests"][test]["p-value"])
          dataset[test] = {
              "effect": effect,
              "pval": pval
          }

      datasets.append(dataset)

    context = {
        "segment": segment,
        "metric": metric,
        "branches": self.data["branches"],
        "datasets": datasets
    }
    self.doc(t.render(context))