code: def mann_whitney_plot(data,
                        condition,
                        distribution,
                        ax=None,
                        condition_value=None,
                        alternative="two-sided",
                        skip_plot=False,
                        **kwargs):
      
      condition_mask = get_condition_mask(data, condition, condition_value)
      U, p_value = mannwhitneyu(
          data[condition_mask][distribution],
          data[~condition_mask][distribution],
          alternative=alternative
      )
  
      plot = None
      if not skip_plot:
          plot = stripboxplot(
              x=condition,
              y=distribution,
              data=data,
              ax=ax,
              significant=p_value <= 0.05,
              **kwargs
          )
  
      sided_str = sided_str_from_alternative(alternative, condition)
      print("Mann-Whitney test: U={}, p-value={} ({})".format(U, p_value, sided_str))
      return MannWhitneyResults(U=U,
                                p_value=p_value,
                                sided_str=sided_str,
                                with_condition_series=data[condition_mask][distribution],
                                without_condition_series=data[~condition_mask][distribution],
                                plot=plot)
docs: Create a box plot comparing a condition and perform a
      Mann Whitney test to compare the distribution in condition A v B
  
      Parameters
      ----------
      data: Pandas dataframe
          Dataframe to retrieve information from
  
      condition: str
          Column to use as the splitting criteria
  
      distribution: str
          Column to use as the Y-axis or distribution in the test
  
      ax : Axes, default None
          Axes to plot on
  
      condition_value:
          If `condition` is not a binary column, split on =/!= to condition_value
  
      alternative:
          Specify the sidedness of the Mann-Whitney test: "two-sided", "less"
          or "greater"
  
      skip_plot:
          Calculate the test statistic and p-value, but don't plot.