获取一维数组numpython的元素

Question

如何打印element_valnumpy数组的最后一个值。它是[ 2. 2. 30.]的第二个索引，即30。因此，这些值将类似于30,40,50...670,680。既然numpy数组是一维的，我怎么能做到呢

input.csv文件：

element,LNPT,SNPT,NLP,NSP,TNT,TPnL,MxPnL,MnPnL,MxU,MxD
[ 2.  2. 30.],0,0,4,4,8,-0.1,-0.0,-0.1,17127,-3
[ 2.  2. 40.],0,0,2,2,4,0.0,-0.0,-0.0,17141,-3
[ 2.  2. 50.],0,0,2,2,4,0.0,-0.0,-0.0,17139,-3
[ 2.  2. 60.],2,0,6,6,12,0.5,2.3,-1.9,17015,-3
[ 2.  2. 70.],1,0,4,4,8,0.3,0.3,-0.0,17011,-3

代码：

import numpy as np
import pandas as pd

my_data = pd.read_csv('input.csv').to_numpy()
element_vals = my_data[:,0]
print(element_vals)

输出：

['[ 2.  2. 30.]' '[ 2.  2. 40.]' '[ 2.  2. 50.]' ... '[  6.    7.5 660. ]'
 '[  6.    7.5 670. ]' '[  6.    7.5 680. ]']

Answer 1

使用^{}、^{}和^{}手动将df.element列转换为实numpy数组：

df = pd.read_csv('input.csv')
element_vals = df.element.str.strip('[]').str.split(expand=True).astype(float).to_numpy()

# array([[ 2.,  2., 30.],
#        [ 2.,  2., 40.],
#        [ 2.,  2., 50.],
#        [ 2.,  2., 60.],
#        [ 2.,  2., 70.]])

然后您可以按预期编制索引：

element_vals[:, 2]

# array([30., 40., 50., 60., 70.])