我有1000张100x100像素的图像，我想将numpy数组转换为数据帧，其中numpy数组的第一个通道在前100列中，第二个通道在后100列中，依此类推。例如：
import numpy as np
my_images=np.random.uniform(0,1,(1000,100,100,3))
my_images[0].shape
(100, 100, 3)
# channel red chanel green channel blue
r1 r2 ... r100 g1 g2 ... g100 b1 b2 ... b100
image0 ...
image1 ...
...
image99 ...
我该怎么办？
谢谢
答案 0 :(得分：1)
这不是最快的解决方案，但可以为您提供所需的输出。另外，每个通道有100 * 100 = 10000像素，而不是您显示的100。
import numpy as np
import pandas as pd
my_images=np.random.uniform(0,1,(1000,100,100,3))
nums = my_images.shape[0]
w = 100
h = 100
c = 3
pd.DataFrame(data= [my_images[i].flatten().reshape(w*h*c) for i in range(nums)], # values
index = ['img' + str(i) for i in range(nums)], # 1st column as index
columns= ['r' + str(i+1) for i in range(100*100)] + ['g'+str(i+1) for i in range(100*100)] + ['b'+str(i+1) for i in range(100*100)] ) # 1st row as the column names
r1 r2 r3 r4 r5 r6 r7 r8 r9 r10 r11 r12 r13 r14 r15 r16 r17 r18 r19 r20 r21 r22 r23 r24 r25 r26 r27 r28 r29 r30 r31 r32 r33 r34 r35 r36 r37 r38 r39 r40 ... b9961 b9962 b9963 b9964 b9965 b9966 b9967 b9968 b9969 b9970 b9971 b9972 b9973 b9974 b9975 b9976 b9977 b9978 b9979 b9980 b9981 b9982 b9983 b9984 b9985 b9986 b9987 b9988 b9989 b9990 b9991 b9992 b9993 b9994 b9995 b9996 b9997 b9998 b9999 b10000
img0 0.486932 0.358114 0.378462 0.962743 0.967968 0.858799 0.355605 0.557928 0.180849 0.979154 0.058116 0.287494 0.479971 0.263553 0.881374 0.875206 0.352662 0.038444 0.120535 0.000326 0.978237 0.715863 0.350651 0.136343 0.633519 0.971797 0.194671 0.981690 0.301551 0.738752 0.910106 0.723529 0.567343 0.583138 0.925730 0.885722 0.417114 0.337083 0.140824 0.889483 ... 0.649809 0.513012 0.653982 0.177253 0.188145 0.494424 0.455103 0.024664 0.012357 0.956512 0.873941 0.977735 0.374367 0.101984 0.989629 0.785725 0.839505 0.944744 0.748421 0.781885 0.233788 0.829904 0.907176 0.157434 0.382110 0.460945 0.732198 0.130351 0.607728 0.200979 0.496550 0.272893 0.490625 0.529867 0.975578 0.688915 0.219779 0.253977 0.351022 0.738245
img1 0.045102 0.028449 0.777591 0.575000 0.868562 0.192366 0.925344 0.934355 0.476159 0.336614 0.058352 0.706517 0.700616 0.697568 0.931899 0.298687 0.325758 0.844186 0.975191 0.815392 0.819782 0.942618 0.119667 0.883005 0.125012 0.314823 0.161448 0.790827 0.534900 0.844786 0.975306 0.347407 0.661538 0.098459 0.867604 0.208455 0.729929 0.063040 0.798002 0.976301 ... 0.123317 0.469142 0.022312 0.238848 0.579594 0.233403 0.111962 0.180687 0.929240 0.283651 0.125432 0.484908 0.734587 0.788714 0.675700 0.663587 0.596222 0.216500 0.783715 0.330661 0.703489 0.780786 0.111286 0.804458 0.691808 0.709701 0.874952 0.278902 0.499596 0.863403 0.554768 0.432910 0.189384 0.196691 0.024404 0.492914 0.041698 0.782992 0.330925 0.681564
img2 0.895364 0.736759 0.034967 0.331774 0.179954 0.064767 0.431156 0.345568 0.516664 0.724786 0.283170 0.077229 0.382801 0.734160 0.964772 0.630064 0.304190 0.014286 0.466788 0.624111 0.593372 0.773352 0.018929 0.515174 0.665516 0.614239 0.184120 0.940892 0.520044 0.432633 0.465579 0.458798 0.623848 0.452827 0.442585 0.722681 0.631540 0.109865 0.263037 0.432471 ... 0.344212 0.161572 0.934545 0.761583 0.048125 0.004187 0.532132 0.275795 0.730705 0.714795 0.220308 0.624861 0.518679 0.838271 0.081648 0.528557 0.316329 0.301952 0.647050 0.141154 0.528354 0.733596 0.213516 0.876608 0.399335 0.785443 0.908612 0.977647 0.869134 0.438461 0.477976 0.191026 0.008590 0.734226 0.331556 0.277559 0.358993 0.429474 0.209247 0.741306
img3 0.042249 0.215090 0.609098 0.136100 0.480906 0.270167 0.275241 0.418473 0.119309 0.075258 0.476748 0.151002 0.013587 0.695470 0.062000 0.183760 0.685605 0.224058 0.354772 0.558781 0.801372 0.136810 0.585566 0.468125 0.754648 0.205976 0.204213 0.055228 0.715287 0.069664 0.682018 0.219368 0.629850 0.469663 0.971606 0.445856 0.130807 0.859502 0.395481 0.101970 ... 0.642661 0.627955 0.962984 0.436121 0.226712 0.343389 0.158353 0.032410 0.197190 0.487362 0.649477 0.227321 0.106230 0.635838 0.822605 0.181413 0.134469 0.536877 0.229766 0.014072 0.076003 0.715866 0.962098 0.693023 0.536637 0.628003 0.953686 0.471928 0.969996 0.777448 0.709374 0.043167 0.590571 0.264927 0.034542 0.947914 0.723615 0.555356 0.292524 0.860090
img4 0.342812 0.984872 0.595325 0.560725 0.322496 0.106175 0.623969 0.517042 0.202604 0.899584 0.357242 0.211253 0.336435 0.215450 0.651622 0.296562 0.932716 0.904923 0.832153 0.297773 0.079881 0.898914 0.862357 0.219088 0.874832 0.945539 0.854319 0.141670 0.450833 0.324066 0.157684 0.506456 0.650278 0.104939 0.918043 0.615409 0.487017 0.914478 0.525752 0.532596 ... 0.052621 0.648331 0.289361 0.357715 0.054250 0.827826 0.628299 0.704747 0.777197 0.948778 0.542223 0.111109 0.034345 0.818893 0.180993 0.809252 0.258662 0.440389 0.973193 0.629529 0.722974 0.164910 0.376697 0.417736 0.402239 0.203207 0.352650 0.082466 0.723988 0.192275 0.905494 0.271835 0.148498 0.357596 0.775139 0.631658 0.683252 0.444594 0.562559 0.270776
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
img995 0.838085 0.397261 0.846695 0.933139 0.620105 0.532176 0.797445 0.527347 0.239898 0.905126 0.958340 0.044355 0.066412 0.744415 0.909536 0.704189 0.798124 0.971813 0.284309 0.199504 0.707818 0.678898 0.184314 0.265278 0.286230 0.252277 0.698063 0.052999 0.163469 0.568355 0.949519 0.673165 0.845369 0.939780 0.536132 0.943460 0.154108 0.642619 0.215590 0.661864 ... 0.274670 0.237090 0.793010 0.993295 0.594697 0.767856 0.837942 0.237594 0.519923 0.396707 0.094800 0.460319 0.677104 0.054613 0.804510 0.874100 0.120772 0.383306 0.703549 0.015940 0.382384 0.117743 0.412058 0.374512 0.839501 0.533861 0.858508 0.014900 0.127974 0.784746 0.480787 0.754446 0.730582 0.213735 0.493690 0.590700 0.477921 0.767106 0.460363 0.898690
img996 0.171881 0.666083 0.567388 0.534970 0.110068 0.950682 0.738784 0.624271 0.147806 0.285420 0.009684 0.403910 0.345770 0.232941 0.261416 0.620245 0.504229 0.436067 0.822144 0.746396 0.853005 0.843115 0.587636 0.114130 0.502100 0.780388 0.819707 0.332244 0.592359 0.000251 0.156495 0.764803 0.509039 0.220428 0.829552 0.390561 0.074693 0.815926 0.113959 0.118536 ... 0.685953 0.517420 0.152370 0.961478 0.500367 0.779224 0.331803 0.628549 0.399974 0.404072 0.549934 0.142945 0.061841 0.713861 0.842240 0.740575 0.520969 0.094217 0.480275 0.609732 0.324852 0.355591 0.059871 0.571136 0.275547 0.463229 0.573066 0.628505 0.131310 0.916291 0.501336 0.734825 0.550189 0.852415 0.036260 0.163183 0.379861 0.151860 0.587352 0.558592
img997 0.260229 0.027160 0.024845 0.405843 0.153609 0.948280 0.833786 0.443430 0.733487 0.469510 0.492071 0.093200 0.748563 0.443063 0.103739 0.262570 0.326819 0.038774 0.654335 0.505306 0.025019 0.153099 0.495584 0.825881 0.127388 0.579630 0.622424 0.203833 0.264736 0.339902 0.958501 0.130448 0.809757 0.480960 0.456775 0.888074 0.660926 0.866277 0.489128 0.986085 ... 0.629622 0.532311 0.198096 0.512762 0.025338 0.179305 0.564835 0.844087 0.550143 0.044381 0.993802 0.231412 0.231025 0.069339 0.867986 0.877767 0.276351 0.438310 0.931497 0.382128 0.727942 0.755447 0.367368 0.672704 0.846903 0.960334 0.227707 0.921844 0.224192 0.481621 0.273852 0.521445 0.387418 0.594588 0.634387 0.752935 0.966900 0.286578 0.848091 0.761919
img998 0.137022 0.363908 0.840758 0.347840 0.119981 0.367948 0.039828 0.731761 0.959635 0.133150 0.344888 0.328250 0.969160 0.089202 0.044397 0.727128 0.707633 0.459997 0.049261 0.222913 0.586730 0.410247 0.689743 0.954076 0.067321 0.008652 0.250084 0.997298 0.653954 0.036369 0.209113 0.606945 0.375438 0.423460 0.874191 0.960310 0.002205 0.638605 0.722848 0.280049 ... 0.956299 0.122053 0.198945 0.632034 0.103117 0.480278 0.658445 0.703479 0.842846 0.589640 0.150563 0.580881 0.388750 0.084818 0.746425 0.590966 0.751141 0.515122 0.370861 0.233196 0.943156 0.965516 0.463034 0.055458 0.436112 0.722325 0.051160 0.443859 0.505781 0.547766 0.798339 0.890893 0.146561 0.803766 0.721480 0.386739 0.883938 0.182097 0.549544 0.276338
img999 0.744549 0.724202 0.889157 0.177200 0.120874 0.013192 0.432094 0.973115 0.825215 0.568912 0.608280 0.439619 0.067831 0.454721 0.987532 0.509805 0.760051 0.581479 0.331267 0.600631 0.780780 0.117506 0.583506 0.593765 0.976454 0.143185 0.344449 0.330625 0.188884 0.919397 0.623545 0.686960 0.423576 0.173410 0.325512 0.806801 0.492669 0.717346 0.988060 0.079150 ... 0.468972 0.621811 0.315449 0.698777 0.073734 0.252283 0.990783 0.652986 0.778134 0.263407 0.142558 0.558776 0.765833 0.641718 0.934577 0.545056 0.974763 0.392286 0.026937 0.546414 0.040816 0.939266 0.325401 0.760387 0.131496 0.495096 0.711066 0.192676 0.600273 0.266118 0.258884 0.771196 0.902020 0.572795 0.983741 0.886390 0.623550 0.236895 0.498307 0.757591
1000 rows × 30000 columns
答案 1 :(得分：1)
根据您要“展平”每种颜色的顺序，可以执行以下操作：
import numpy as np
import pandas as pd
N = 1000
d = 100
ims = np.random.uniform(0, 1, (N, d, d, 3))
cols = [f'r{i}' for i in range(d**2)] + [f'g{i}' for i in range(d**2)] + [f'b{i}' for i in range(d**2)]
df = pd.DataFrame(data = np.swapaxes(ims, 1,3).reshape(N, -1),
columns = cols,
index = [f'image{i}' for i in range(N)])
df
r0 r1 r2 ... b9997 b9998 b9999
image0 0.754808 0.830338 0.352381 ... 0.870754 0.654643 0.579047
image1 0.387347 0.197181 0.253846 ... 0.091151 0.900780 0.555289
image2 0.743756 0.465971 0.269931 ... 0.946937 0.117723 0.497232
image3 0.899940 0.891160 0.701490 ... 0.194214 0.819856 0.485955
image4 0.593754 0.161847 0.342187 ... 0.481527 0.213749 0.978097
... ... ... ... ... ... ...
image995 0.693697 0.297598 0.258053 ... 0.971533 0.430261 0.222101
image996 0.818448 0.658862 0.339770 ... 0.613745 0.776705 0.532044
image997 0.376489 0.180131 0.703525 ... 0.933989 0.326170 0.833145
image998 0.757319 0.652464 0.125789 ... 0.675353 0.860467 0.394837
image999 0.021546 0.670330 0.024574 ... 0.617004 0.156939 0.415484
[1000 rows x 30000 columns]