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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def fishers_exact_plot(data, condition1, condition2, ax=None, condition1_value=None, alternative="two-sided", **kwargs):
 """ Perform a Fisher's exact test to compare to binary columns Parameters data: Pandas dataframe Dataframe to retrieve information from condition1: str First binary column to compare (and used for test sidedness) condition2: str Second binary column to compare ax : Axes, default None Axes to plot on condition1_value: If `condition1` is not a binary column, split on =/!= to condition1_value alternative: Specify the sidedness of the test: "two-sided", "less" or "greater" """

    plot = sb.barplot(
        x=condition1,
        y=condition2,
        ax=ax,
        data=data,
        **kwargs
    )

    plot.set_ylabel("Percent %s" % condition2)
    condition1_mask = get_condition_mask(data, condition1, condition1_value)
    count_table = pd.crosstab(data[condition1], data[condition2])
    print(count_table)
    oddsratio, p_value = fisher_exact(count_table, alternative=alternative)
    add_significance_indicator(plot=plot, significant=p_value <= 0.05)
    only_percentage_ticks(plot)

    if alternative != "two-sided":
        raise ValueError("We need to better understand the one-sided Fisher's Exact test")
    sided_str = "two-sided"
    print("Fisher's Exact Test: OR: {}, p-value={} ({})".format(oddsratio, p_value, sided_str))
    return FishersExactResults(oddsratio=oddsratio,
                               p_value=p_value,
                               sided_str=sided_str,
                               with_condition1_series=data[condition1_mask][condition2],
                               without_condition1_series=data[~condition1_mask][condition2],
                               plot=plot)