<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def mann_whitney_plot(data, condition, distribution, ax=None, condition_value=None, alternative="two-sided", skip_plot=False, **kwargs):
 """ 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. """

    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)