Python-Mann-Whitney置信区间

我有两个数据集（熊猫系列）——ds1和ds2——我想计算平均值（如果正常）或中位数（非正常）差异的95%置信区间

对于均值差异，我计算t检验统计量和CI，如下所示：

import statsmodels.api as sm
tstat, p_value, dof = sm.stats.ttest_ind(ds1, ds2)
CI = sm.stats.CompareMeans.from_data(ds1, ds2).tconfint_diff()

至于中位数，我有：

from scipy.stats import mannwhitneyu
U_stat, p_value = mannwhitneyu(ds1, ds2, True, "two-sided")

如何计算中位数差异的置信区间？

我看到一篇论文（计算一些非参数的置信区间） MICHAEL J CAMPBELL、MARTIN J GARDNER的分析给出了CI公式

基于此：

from scipy.stats import norm

ct1 = ds1.count()  #items in dataset 1
ct2 = ds2.count()  #items in dataset 2
alpha = 0.05       #95% confidence interval
N = norm.ppf(1 - alpha/2) # percent point function - inverse of cdf

# The confidence interval for the difference between the two population
# medians is derived through these nxm differences.
diffs = sorted([i-j for i in ds1 for j in ds2])

# For an approximate 100(1-a)% confidence interval first calculate K:
k = int(round(ct1*ct2/2 - (N * (ct1*ct2*(ct1+ct2+1)/12)**0.5)))

# The Kth smallest to the Kth largest of the n x m differences 
# ct1 and ct2 should be > ~20
CI = (diffs[k], diffs[len(diffs)-k])