Python 3.x PythonPandas：按行而不是按整个数据帧设置引导置信限

我想做的是，不管行数多少，按行获取引导置信限，并从输出中生成一个新的数据帧。我目前可以对整个数据帧执行此操作，但不能按行执行。我在实际程序中的数据与下面的数据相似：

    0   1   2
0   1   2   3
1   4   1   4
2   1   2   3
3   4   1   4

我希望新的数据帧看起来像这样，具有置信下限和置信上限：

    0   1   
0   1   2   
1   1   5.5 
2   1   4.5 
3   1   4.2 

当前生成的输出如下所示：

     0   1
 0  2.0 2.75

下面的python 3代码生成一个模拟数据帧，并为整个数据帧生成引导置信限。结果是一个新的数据帧，只有2个值，一个置信上限和一个置信下限，而不是每行2个1的4组

import pandas as pd
import numpy as np
import scikits.bootstrap as sci

zz = pd.DataFrame([[[1,2],[2,3],[3,6]],[[4,2],[1,4],[4,6]],
               [[1,2],[2,3],[3,6]],[[4,2],[1,4],[4,6]]])
print(zz)

x= zz.dtypes
print(x)

a = pd.DataFrame(np.array(zz.values.tolist())[:, :, 0],zz.index, zz.columns)
print(a)
b = sci.ci(a)
b = pd.DataFrame(b)
b = b.T
print(b)

---

感谢您的帮助。

下面是我最后想出的按行创建引导ci的答案

import pandas as pd
import numpy as np
import numpy.random as npr

zz = pd.DataFrame([[[1,2],[2,3],[3,6]],[[4,2],[1,4],[4,6]],
                  [[1,2],[2,3],[3,6]],[[4,2],[1,4],[4,6]]])

x= zz.dtypes

a = pd.DataFrame(np.array(zz.values.tolist())[:, :, 0],zz.index, zz.columns)
print(a)

def bootstrap(data, num_samples, statistic, alpha):
    n = len(data)
    idx = npr.randint(0, n, (num_samples, n))
    samples = data[idx]
    stat = np.sort(statistic(samples, 1))
    return (stat[int((alpha/2.0)*num_samples)],
            stat[int((1-alpha/2.0)*num_samples)])

cc = list(a.index.values) # informs generator of the number of rows

def bootbyrow(cc):
    for xx in range(1):
            xx = list(a.index.values)
            for xx in range(len(cc)):
                k = a.apply(lambda y: y[xx])
                k = k.values
                for xx in range(1):
                    kk = list(bootstrap(k,10000,np.mean,0.05))   
                    yield list(kk)


abc = pd.DataFrame(list(bootbyrow(cc))) #bootstrap ci by row

# the next 4 just show that its working correctly
a0 = bootstrap((a.loc[0,].values),10000,np.mean,0.05)   
a1 = bootstrap((a.loc[1,].values),10000,np.mean,0.05)
a2 = bootstrap((a.loc[2,].values),10000,np.mean,0.05)  
a3 = bootstrap((a.loc[3,].values),10000,np.mean,0.05)  

print(abc)
print(a0)
print(a1)
print(a2)
print(a3)

bootstrap的工作原理是假设数据样本是按行而不是按列排列的。如果想要相反的行为，只需使用转置和不合并列的statfunction

import pandas as pd
import numpy as np
import scikits.bootstrap as sci

zz = pd.DataFrame([[[1,2],[2,3],[3,6]],[[4,2],[1,4],[4,6]],
               [[1,2],[2,3],[3,6]],[[4,2],[1,4],[4,6]]])
print(zz)

x= zz.dtypes
print(x)

a = pd.DataFrame(np.array(zz.values.tolist())[:, :, 0],zz.index, zz.columns)
print(a)
b = sci.ci(a.T, statfunction=lambda x: np.average(x, axis=0))
print(b.T)