從 python 的列表中提取數據

Question

我有以下列表：

[N               12.000000
 mean             2.011608
 median           2.021611
 std              0.572034
 relative std     0.284350
 dtype: float64,
 N               12.000000
 mean             2.011608
 median           2.021611
 std              0.571815
 relative std     0.284262
 dtype: float64,
 N               12.000000
 mean             2.011608
 median           2.021611
 std              0.572101
 relative std     0.284412
 dtype: float64,
 N               12.000000
 mean             2.011608
 median           2.021611
 std              0.572115
 relative std     0.284440
 dtype: float64,
 N               12.000000
 mean             2.011608
 median           2.021611
 std              0.571872
 relative std     0.284313
 dtype: float64]

我想從具有最小相對標准值的列表中提取數據（N、均值、標准、相對標准）。 上述列表中的output應該如下：

 N               12.000000
 mean             2.011608
 median           2.021611
 std              0.571815
 relative std     0.284262
 dtype: float64

到目前為止我嘗試了什么？

min(list)

但是會引發以下錯誤ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Answer 1

將min與 lambda function 一起用於 select 的Series值std

s1 = pd.Series([0,1,2], index=['std','min','max'])
s2 = pd.Series([4,1,2], index=['std','min','max'])
s3 = pd.Series([0.8,1,2], index=['std','min','max'])

L = [s1, s2, s3]

s = min(L, key=lambda x:x.loc['std'])
print (s)
std    0
min    1
max    2
dtype: int64

測試所有最小值：

print ([x.loc['std'] for x in L])
[0, 4, 0.8]

對於索引使用np.argmin ：

import numpy as np
print (np.argmin([x.loc['std'] for x in L]))
0