python不同维度list放在一起_python-在循环中附加具有不同维度的numpy nda...

我需要附加在每个循环中创建的数组,以便最后获得单个ndarray.代码结构如下：

for...:

.

.

.

for...:

list1 = array([some_math_here])

list2.append(list1)

#each loop creats a list, converting it to array() gives different shaped arrays:

array(list2).shape

(2939, 4)

(2942, 4)

(2027, 4)

(2030, 4)

#list3 collects all the generated results

list3.append(list2)

问：

我如何拥有n * 4列和不同行数的数组而不是list3？

我尝试通过创建一个空数组a = array([0.,1.]),然后追加一个(a,array(list_2)),但不起作用.我知道hstack vstack等,但不能在循环中将它们与append一起使用.有什么建议吗？

更新

?这是带有建议方法输出的实际代码：

files_ = glob.glob('D:\Test files\*.txt')

tfile_ = loadtxt('times.txt')

averages_, d = [], []

with open ('outfile.csv', 'wb') as outfile:

writer = csv.writer(outfile)

for fcount_, fname_ in enumerate(files_):

data = loadtxt(fname_ , usecols = (1,2,3,4))

average_, fcol = [], []

seg_len = 3

for x in range(0, len(data[:,0]), seg_len):

sample_means = [mean(data[x:x+seg_len,i]) for i in range(4)]

none_zeros = [x if x >= 0 else x == 0 for x in sample_means]

average_.append(none_zeros)

fcol = cumsum(array(average_)[:,0])

average_ = array([row + [col] for row, col in zip(average_, fcol)])

averages_.append(average_)

d = concatenate(array(averages_))

df = pd.DataFrame(d)

df.to_csv('pdtest2.csv')

输出：

0 1 2 3 4

0 0.037039 0.103792 0.136116 0.579297 0.037039

1 0.051183 0.104669 0.177728 0.593771 0.088222

2 0.059517 0.105437 0.174274 0.571402 0.147739

3 0.053212 0.102476 0.167530 0.645745 0.200950

4 0.054637 0.104450 0.165228 0.596622 0.054637

5 0.051622 0.101161 0.166708 0.595964 0.106259

6 0.057324 0.099077 0.168024 0.596841 0.163583

7 0.054692 0.103573 0.157168 0.598596 0.218275

8 0.066699 0.100612 0.145984 0.591578 0.284974

9 0.120866 5.527104 4.678589 2.401020 0.120866

10 0.113958 5.176220 4.669872 2.361985 0.234824

11 0.121469 4.879613 4.659017 2.359573 0.356293

12 0.122511 4.695618 4.642240 2.363959 0.478803

13 0.126650 4.621933 4.620447 2.347073 0.605453

14 0.132708 4.676868 4.517568 2.364617 0.132708

15 0.125087 4.693535 4.459672 2.381941 0.257795

16 0.132708 4.715246 4.444705 2.334353 0.390503

17 0.133476 4.745619 4.406300 2.317467 0.523979

当我想要时：

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

0 0.037038522 0.103792144 0.136115724 0.57929719 0.037038522 0.054637318 0.104450043 0.16522775 0.596621864 0.054637318 0.12086581 5.527104488 4.678589189 2.401020431 0.12086581 0.132707991 4.67686799 4.517567512 2.364616645 0.132707991

1 0.051183348 0.104669343 0.177727829 0.593770968 0.08822187 0.051621948 0.101160549 0.166708023 0.595963965 0.106259265 0.113957871 5.176219782 4.669871979 2.361985046 0.234823681 0.125087328 4.693534961 4.459672089 2.381941338 0.257795319

2 0.059516735 0.105436892 0.17427386 0.571402402 0.147738605 0.057323738 0.099077202 0.168023821 0.596841163 0.163583003 0.121468884 4.879613015 4.659016582 2.359572747 0.356292565 0.132707991 4.715245885 4.444704808 2.334353258 0.39050331

3 0.05321187 0.102476346 0.167530397 0.645744989 0.200950475 0.054692143 0.103572845 0.157168489 0.598595561 0.218275146 0.122510557 4.695618334 4.642240062 2.363958746 0.478803122 0.13347554 4.745619253 4.406299754 2.317467166 0.52397885

4 0 0 0 0 0 0.066698797 0.1006123 0.145984208 0.591577971 0.284973943 0.126649838 4.621932787 4.620447035 2.347072653 0.60545296 0 0 0 0 0