我不知道我是如何弄乱代码属性的,但我最终得到了一个怪物:一个列表,其元素是元组,其元素是数组......
[(array([ 0.00773887, 0.00531894, 0.00533349, 0.00779727, 0.01482933,
0.01247594, 0.01274703, 0.02111097, 0.01800994, 0.01398229,
0.0098171 , 0.01218634, 0.00987849, 0.01082564, 0.00739867,
0.00930694, 0.01071009, 0.00871869, 0.00897236, nan,
0.06841951, 0.03529371, nan, 0.016373 , 0.0160713 ,
0.0182685 , nan, 0.01816914, 0.01156275, 0.01283952,
nan, 0.00962016, 0.00861139, 0.00776996, 0.0324668 ,
0.00745239]),), (array([ 0.0059394 , 0.00634485, 0.00588112, 0.00583169, 0.01051727,
0.01061778, 0.01368865, 0.01033937, 0.0105504 , 0.01073401,
0.01276537, 0.01121004, 0.00761677, 0.01370088, 0.01133099,
0.01176184, 0.00922666, 0.00655782, 0.00608386, 0.00686759,
0.00935311, 0.01204305, 0.00912691, 0.01046725, 0.01721009,
0.01446536, 0.01320765, 0.01304908, 0.01170495, 0.00884054,
0.00964988, 0.01170055, 0.00673198, 0.00543281, 0.00610345,
0.0072238 ]),), ... (array([ 0.00839741, 0.01946217, 0.01032584, 0.00857666, 0.01208251,
0.02800771, 0.02556111, 0.01417291, 0.01771353, 0.02820838,
0.01647876, 0.02092841, 0.0193186 , 0.03620055, 0.01485362,
0.01272976, 0.01202848, 0.01717644, 0.0149781 , 0.01955458,
0.0221506 , 0.0452485 , 0.03533813, nan, nan,
nan, 0.06147576, 0.02526941, 0.01906981, 0.02111215,
0.0184051 , 0.01271902, 0.00860239, 0.00950814, 0.01013899,
0.00810949]),)]
如何转换元素以获取SIMPLE列表列表?
答案 0 :(得分:3)
它可能不是最快的,但这应该非常强大:
lst = np.array(lst).tolist()
答案 1 :(得分:-1)
只需调用以下列表理解:
lst = [item.tolist() for item in lst]
运行如下:
>>> lst = [(array([ 0.00773887, 0.00531894, 0.00533349, 0.00779727, 0.01482933,
... 0.01247594, 0.01274703, 0.02111097, 0.01800994, 0.01398229,
... 0.0098171 , 0.01218634, 0.00987849, 0.01082564, 0.00739867,
... 0.00930694, 0.01071009, 0.00871869, 0.00897236, nan,
... 0.06841951, 0.03529371, nan, 0.016373 , 0.0160713 ,
... 0.0182685 , nan, 0.01816914, 0.01156275, 0.01283952,
... nan, 0.00962016, 0.00861139, 0.00776996, 0.0324668 ,
... 0.00745239]))]
>>>
>>> lst = [item.tolist() for item in lst]
>>>
>>> lst
[[0.00773887, 0.00531894, 0.00533349, 0.00779727, 0.01482933, 0.01247594, 0.01274703, 0.02111097, 0.01800994, 0.01398229, 0.0098171, 0.01218634, 0.00987849, 0.01082564, 0.00739867, 0.00930694, 0.01071009, 0.00871869, 0.00897236, nan, 0.06841951, 0.03529371, nan, 0.016373, 0.0160713, 0.0182685, nan, 0.01816914, 0.01156275, 0.01283952, nan, 0.00962016, 0.00861139, 0.00776996, 0.0324668, 0.00745239]]
>>>