print word vectors simply instead of obtaining them as array

print word vectors simply instead of obtaining them as array - python

A very simple task but I don't seem to do it. I want to obtain my vectors like this:
the -0.038194 -0.24487 0.72812 -0.39961 0.083172 0.043953 -0.39141 0.3344 -0.57545 0.087459 0.28787 -0.06731 0.30906 -0.26384 -0.13231 -0.20757 0.33395 -0.33848 -0.31743 -0.48336 0.1464 -0.37304 0.34577 0.052041 0.44946 -0.46971 0.02628 -0.54155 -0.15518 -0.14107 -0.039722 0.28277 0.14393 0.23464 -0.31021 0.086173 0.20397 0.52624 0.17164 -0.082378 -0.71787 -0.41531 0.20335 -0.12763 0.41367 0.55187 0.57908 -0.33477 -0.36559 -0.54857 -0.062892 0.26584 0.30205 0.99775 -0.80481 -3.0243 0.01254 -0.36942 2.2167 0.72201 -0.24978 0.92136 0.034514 0.46745 1.1079 -0.19358 -0.074575 0.23353 -0.052062 -0.22044 0.057162 -0.15806 -0.30798 -0.41625 0.37972 0.15006 -0.53212 -0.2055 -1.2526 0.071624 0.70565 0.49744 -0.42063 0.26148 -1.538 -0.30223 -0.073438 -0.28312 0.37104 -0.25217 0.016215 -0.017099 -0.38984 0.87424 -0.72569 -0.51058 -0.52028 -0.1459 0.8278 0.27062
, -0.10767 0.11053 0.59812 -0.54361 0.67396 0.10663 0.038867 0.35481 0.06351 -0.094189 0.15786 -0.81665 0.14172 0.21939 0.58505 -0.52158 0.22783 -0.16642 -0.68228 0.3587 0.42568 0.19021 0.91963 0.57555 0.46185 0.42363 -0.095399 -0.42749 -0.16567 -0.056842 -0.29595 0.26037 -0.26606 -0.070404 -0.27662 0.15821 0.69825 0.43081 0.27952 -0.45437 -0.33801 -0.58184 0.22364 -0.5778 -0.26862 -0.20425 0.56394 -0.58524 -0.14365 -0.64218 0.0054697 -0.35248 0.16162 1.1796 -0.47674 -2.7553 -0.1321 -0.047729 1.0655 1.1034 -0.2208 0.18669 0.13177 0.15117 0.7131 -0.35215 0.91348 0.61783 0.70992 0.23955 -0.14571 -0.37859 -0.045959 -0.47368 0.2385 0.20536 -0.18996 0.32507 -1.1112 -0.36341 0.98679 -0.084776 -0.54008 0.11726 -1.0194 -0.24424 0.12771 0.013884 0.080374 -0.35414 0.34951 -0.7226 0.37549 0.4441 -0.99059 0.61214 -0.35111 -0.83155 0.45293 0.082577
. -0.33979 0.20941 0.46348 -0.64792 -0.38377 0.038034 0.17127 0.15978 0.46619 -0.019169 0.41479 -0.34349 0.26872 0.04464 0.42131 -0.41032 0.15459 0.022239 -0.64653 0.25256 0.043136 -0.19445 0.46516 0.45651 0.68588 0.091295 0.21875 -0.70351 0.16785 -0.35079 -0.12634 0.66384 -0.2582 0.036542 -0.13605 0.40253 0.14289 0.38132 -0.12283 -0.45886 -0.25282 -0.30432 -0.11215 -0.26182 -0.22482 -0.44554 0.2991 -0.85612 -0.14503 -0.49086 0.0082973 -0.17491 0.27524 1.4401 -0.21239 -2.8435 -0.27958 -0.45722 1.6386 0.78808 -0.55262 0.65 0.086426 0.39012 1.0632 -0.35379 0.48328 0.346 0.84174 0.098707 -0.24213 -0.27053 0.045287 -0.40147 0.11395 0.0062226 0.036673 0.018518 -1.0213 -0.20806 0.64072 -0.068763 -0.58635 0.33476 -1.1432 -0.1148 -0.25091 -0.45907 -0.096819 -0.17946 -0.063351 -0.67412 -0.068895 0.53604 -0.87773 0.31802 -0.39242 -0.23394 0.47298 -0.028803
of -0.1529 -0.24279 0.89837 0.16996 0.53516 0.48784 -0.58826 -0.17982 -1.3581 0.42541 0.15377 0.24215 0.13474 0.41193 0.67043 -0.56418 0.42985 -0.012183 -0.11677 0.31781 0.054177 -0.054273 0.35516 -0.30241 0.31434 -0.33846 0.71715 -0.26855 -0.15837 -0.47467 0.051581 -0.33252 0.15003 -0.1299 -0.54617 -0.37843 0.64261 0.82187 -0.080006 0.078479 -0.96976 -0.57741 0.56491 -0.39873 -0.057099 0.19743 0.065706 -0.48092 -0.20125 -0.40834 0.39456 -0.02642 -0.11838 1.012 -0.53171 -2.7474 -0.042981 -0.74849 1.7574 0.59085 0.04885 0.78267 0.38497 0.42097 0.67882 0.10337 0.6328 -0.026595 0.58647 -0.44332 0.33057 -0.12022 -0.55645 0.073611 0.20915 0.43395 -0.012761 0.089874 -1.7991 0.084808 0.77112 0.63105 -0.90685 0.60326 -1.7515 0.18596 -0.50687 -0.70203 0.66578 -0.81304 0.18712 -0.018488 -0.26757 0.727 -0.59363 -0.34839 -0.56094 -0.591 1.0039 0.20664
to -0.1897 0.050024 0.19084 -0.049184 -0.089737 0.21006 -0.54952 0.098377 -0.20135 0.34241 -0.092677 0.161 -0.13268 -0.2816 0.18737 -0.42959 0.96039 0.13972 -1.0781 0.40518 0.50539 -0.55064 0.4844 0.38044 -0.0029055 -0.34942 -0.099696 -0.78368 1.0363 -0.2314 -0.47121 0.57126 -0.21454 0.35958 -0.48319 1.0875 0.28524 0.12447 -0.039248 -0.076732 -0.76343 -0.32409 -0.5749 -1.0893 -0.41811 0.4512 0.12112 -0.51367 -0.13349 -1.1378 -0.28768 0.16774 0.55804 1.5387 0.018859 -2.9721 -0.24216 -0.92495 2.1992 0.28234 -0.3478 0.51621 -0.43387 0.36852 0.74573 0.072102 0.27931 0.92569 -0.050336 -0.85856 -0.1358 -0.92551 -0.33991 -1.0394 -0.067203 -0.21379 -0.4769 0.21377 -0.84008 0.052536 0.59298 0.29604 -0.67644 0.13916 -1.5504 -0.20765 0.7222 0.52056 -0.076221 -0.15194 -0.13134 0.058617 -0.31869 -0.61419 -0.62393 -0.41548 -0.038175 -0.39804 0.47647 -0.15983
and -0.071953 0.23127 0.023731 -0.50638 0.33923 0.1959 -0.32943 0.18364 -0.18057 0.28963 0.20448 -0.5496 0.27399 0.58327 0.20468 -0.49228 0.19974 -0.070237 -0.88049 0.29485 0.14071 -0.1009 0.99449 0.36973 0.44554 0.28998 -0.1376 -0.56365 -0.029365 -0.4122 -0.25269 0.63181 -0.44767 0.24363 -0.10813 0.25164 0.46967 0.3755 -0.23613 -0.14129 -0.44537 -0.65737 -0.042421 -0.28636 -0.28811 0.063766 0.20281 -0.53542 0.41307 -0.59722 -0.38614 0.19389 -0.17809 1.6618 -0.011819 -2.3737 0.058427 -0.2698 1.2823 0.81925 -0.22322 0.72932 -0.053211 0.43507 0.85011 -0.42935 0.92664 0.39051 1.0585 -0.24561 -0.18265 -0.5328 0.059518 -0.66019 0.18991 0.28836 -0.2434 0.52784 -0.65762 -0.14081 1.0491 0.5134 -0.23816 0.69895 -1.4813 -0.2487 -0.17936 -0.059137 -0.08056 -0.48782 0.014487 -0.6259 -0.32367 0.41862 -1.0807 0.46742 -0.49931 -0.71895 0.86894 0.19539
in 0.085703 -0.22201 0.16569 0.13373 0.38239 0.35401 0.01287 0.22461 -0.43817 0.50164 -0.35874 -0.34983 0.055156 0.69648 -0.17958 0.067926 0.39101 0.16039 -0.26635 -0.21138 0.53698 0.49379 0.9366 0.66902 0.21793 -0.46642 0.22383 -0.36204 -0.17656 0.1748 -0.20367 0.13931 0.019832 -0.10413 -0.20244 0.55003 -0.1546 0.98655 -0.26863 -0.2909 -0.32866 -0.34188 -0.16943 -0.42001 -0.046727 -0.16327 0.70824 -0.74911 -0.091559 -0.96178 -0.19747 0.10282 0.55221 1.3816 -0.65636 -3.2502 -0.31556 -1.2055 1.7709 0.4026 -0.79827 1.1597 -0.33042 0.31382 0.77386 0.22595 0.52471 -0.034053 0.32048 0.079948 0.17752 -0.49426 -0.70045 -0.44569 0.17244 0.20278 0.023292 -0.20677 -1.0158 0.18325 0.56752 0.31821 -0.65011 0.68277 -0.86585 -0.059392 -0.29264 -0.55668 -0.34705 -0.32895 0.40215 -0.12746 -0.20228 0.87368 -0.545 0.79205 -0.20695 -0.074273 0.75808 -0.34243
a -0.27086 0.044006 -0.02026 -0.17395 0.6444 0.71213 0.3551 0.47138 -0.29637 0.54427 -0.72294 -0.0047612 0.040611 0.043236 0.29729 0.10725 0.40156 -0.53662 0.033382 0.067396 0.64556 -0.085523 0.14103 0.094539 0.74947 -0.194 -0.68739 -0.41741 -0.22807 0.12 -0.48999 0.80945 0.045138 -0.11898 0.20161 0.39276 -0.20121 0.31354 0.75304 0.25907 -0.11566 -0.029319 0.93499 -0.36067 0.5242 0.23706 0.52715 0.22869 -0.51958 -0.79349 -0.20368 -0.50187 0.18748 0.94282 -0.44834 -3.6792 0.044183 -0.26751 2.1997 0.241 -0.033425 0.69553 -0.64472 -0.0072277 0.89575 0.20015 0.46493 0.61933 -0.1066 0.08691 -0.4623 0.18262 -0.15849 0.020791 0.19373 0.063426 -0.31673 -0.48177 -1.3848 0.13669 0.96859 0.049965 -0.2738 -0.035686 -1.0577 -0.24467 0.90366 -0.12442 0.080776 -0.83401 0.57201 0.088945 -0.42532 -0.018253 -0.079995 -0.28581 -0.01089 -0.4923 0.63687 0.23642
" -0.30457 -0.23645 0.17576 -0.72854 -0.28343 -0.2564 0.26587 0.025309 -0.074775 -0.3766 -0.057774 0.12159 0.34384 0.41928 -0.23236 -0.31547 0.60939 0.25117 -0.68667 0.70873 1.2162 -0.1824 -0.48442 -0.33445 0.30343 1.086 0.49992 -0.20198 0.27959 0.68352 -0.33566 -0.12405 0.059656 0.33617 0.37501 0.56552 0.44867 0.11284 -0.16196 -0.94346 -0.67961 0.18581 0.060653 0.43776 0.13834 -0.48207 -0.56141 -0.25422 -0.52445 0.097003 -0.48925 0.19077 0.21481 1.4969 -0.86665 -3.2846 0.56854 0.41971 1.2294 0.78522 -0.29369 0.63803 -1.5926 -0.20437 1.5306 0.13548 0.50722 0.18742 0.48552 -0.28995 0.19573 0.0046515 0.092879 -0.42444 0.64987 0.52839 0.077908 0.8263 -1.2208 -0.34955 0.49855 -0.64155 -0.72308 0.26566 -1.3643 -0.46364 -0.52048 -1.0525 0.22895 -0.3456 -0.658 -0.16735 0.35158 0.74337 0.26074 0.061104 -0.39079 -0.84557 -0.035432 0.17036
's 0.58854 -0.2025 0.73479 -0.68338 -0.19675 -0.1802 -0.39177 0.34172 -0.60561 0.63816 -0.26695 0.36486 -0.40379 -0.1134 -0.58718 0.2838 0.8025 -0.35303 0.30083 0.078935 0.44416 -0.45906 0.79294 0.50365 0.32805 0.28027 -0.4933 -0.38482 -0.039284 -0.2483 -0.1988 1.1469 0.13228 0.91691 -0.36739 0.89425 0.5426 0.61738 -0.62205 -0.31132 -0.50933 0.23335 1.0826 -0.044637 -0.12767 0.27628 -0.032617 -0.27397 0.77764 -0.50861 0.038307 -0.33679 0.42344 1.2271 -0.53826 -3.2411 0.42626 0.025189 1.3948 0.65085 0.03325 0.37141 0.4044 0.35558 0.98265 -0.61724 0.53901 0.76219 0.30689 0.33065 0.30956 -0.15161 -0.11313 -0.81281 0.6145 -0.44341 -0.19163 -0.089551 -1.5927 0.37405 0.85857 0.54613 -0.31928 0.52598 -1.4802 -0.97931 -0.2939 -0.14724 0.25803 -0.1817 1.0149 0.77649 0.12598 0.54779 -1.0316 0.064599 -0.37523 -0.94475 0.61802 0.39591
for -0.14401 0.32554 0.14257 -0.099227 0.72536 0.19321 -0.24188 0.20223 -0.89599 0.15215 0.035963 -0.59513 -0.051635 -0.014428 0.35475 -0.31859 0.76984 -0.087369 -0.24762 0.65059 -0.15138 -0.42703 0.18813 0.091562 0.15192 0.11303 -0.15222 -0.62786 -0.23923 0.096009 -0.46147 0.41526 -0.30475 0.1371 0.16758 0.53301 -0.043658 0.85924 -0.41192 -0.21394 -0.51228 -0.31945 0.12662 -0.3151 0.0031429 0.27129 0.17328 -1.3159 -0.42414 -0.69126 0.019017 -0.13375 -0.096057 1.7069 -0.65291 -2.6111 0.26518 -0.61178 2.095 0.38148 -0.55823 0.2036 -0.33704 0.37354 0.6951 -0.001637 0.81885 0.51793 0.27746 -0.37177 -0.43345 -0.42732 -0.54912 -0.30715 0.18101 0.2709 -0.29266 0.30834 -1.4624 -0.18999 0.92277 -0.099217 -0.25165 0.49197 -1.525 0.15326 0.2827 0.12102 -0.36766 -0.61275 -0.18884 0.10907 0.12315 0.090066 -0.65447 -0.17252 2.6336e-05 0.25398 1.1078 -0.073074
Here's the full link for the text file so u can see the format completely: https://www.kaggle.com/terenceliu4444/glove6b100dtxt
And Here's my code:
with codecs.open('data/{}.tsv'.format(lcode), 'w', 'utf-8') as fout:
for i, word in enumerate(model.index2word):
fout.write(u"{}\t{}\t{}\n".format(str(i), word.encode('utf8').decode('utf8'),
np.array_str(model[word])
))
and my output is like this:
the [ 0.28177965 -1.3835016 -0.85463244 0.5744817 -0.42041674 0.4850773
-0.18238722 0.9088641 1.6516979 -0.24690722 0.5303408 -0.8106607
0.27385864 0.6186187 -2.061754 1.2491482 0.44255176 -0.25498274
-0.11942661 -0.1751283 0.2187617 -1.2942451 -0.79252934 1.8655167
-1.4975996 -0.02266688 0.26935738 -0.36034968 -1.5055205 0.0860498
1.0129709 1.1270534 -1.3774556 -0.02182451 -0.52671534 -0.7581365
-0.16326018 -0.2763609 0.5690212 -1.355627 0.43560007 2.4623177
-0.46482357 0.85816354 -0.5735287 -0.99033487 0.646639 -0.18928614
-0.6105273 -0.94887084 -0.39465773 0.38946334 -0.5338978 -0.0211645
-0.06462063 1.1689087 -0.88438195 -0.60245454 1.0320075 0.75902534
-1.9108475 -0.8921491 0.57644296 1.8618042 -0.5125161 -1.4219466
0.45342374 0.25558227 1.0577608 0.48511812 0.76758397 -1.0726306
1.5792096 0.01924564 1.8321682 -0.4707404 -0.41836467 0.07758982
0.50893927 -0.71105474 -0.33766833 1.4899743 0.60877067 -0.09521568
0.6654671 -0.0286361 -0.17863822 0.8811929 -1.330545 -1.104361
0.51000476 0.2639544 1.2233694 -0.10699744 -1.1367066 0.6225027
0.5847332 -0.03609625 2.3312287 0.1025821 ]
a [ 1.0829129 0.84877855 -1.1785074 0.13858096 2.008711 0.44480678
0.41152284 -0.9221507 1.5342509 0.8937895 -0.12867515 1.2286083
-1.6460084 0.96246207 0.11606621 -0.7079361 0.7204446 -2.17121
0.21708168 -1.029137 -0.53540015 0.40489924 -0.52271795 1.7237337
-1.3921518 -1.4322941 1.392808 0.7498414 -1.4813395 1.655896
1.0292306 -0.10302904 -0.09161732 0.9659639 0.13209064 -0.5149641
-0.11515223 -1.6309028 -1.1918032 0.34248984 0.6209429 1.0181456
-0.65688735 0.80660087 -0.6315423 -0.68773484 -0.44171524 -0.8294182
0.62340856 -1.0040073 0.40221572 -0.30175862 0.02053229 0.31205446
-0.16386059 0.18476132 0.18067418 -0.28932625 1.0893115 0.11680666
0.1104597 -0.30494598 -0.06541535 0.75524884 3.3038845 -0.5918715
1.0957772 -0.51271206 1.3486993 0.6190552 -1.365369 -2.9811475
1.3973937 1.4510086 0.45045042 0.61286205 1.7809817 -0.639005
-0.22986257 -1.4068168 0.34073976 0.38807136 -0.10908178 -0.9710727
-0.2207968 0.66323316 2.2619925 1.8806032 -0.06102083 0.86097974
-0.07785034 0.3742449 1.800688 -0.92509884 0.1773087 0.38380435
0.44551063 0.5976865 1.8766458 0.23904268]
I tried to remove array from my code and still couldn't print those word vectors in that format. Im using Genism by the way to obtain vectors.

That looks like roughly the format (minus one leading line) that's already written-out by the .save_word2vec_format() method on gensim word-vector classes.
You should try using that, perhaps with the write_first_line parameter as False, or simply editing the file afterwards. For example:
model.save_word2vec_format(your_filename, binary=False, write_first_line=False)
(I'd also note: your example format, and this method, will only use single spaces between fields, **not* tabs, so your existing file-suffic of .tsv for 'tab-separated-values' would be misleading.)

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How to get a certain value from a text file

I want to get a value from an API. However I am unable to tell Python what I want to do.
This is my current code:
response = requests.get('https://sms-service-online.com/api/getServicesAndCost/?country=0&operator=any&lang=en')
data = response.json()
plain = data["data"]
However the data which I want to display in my script is in the ["data"] part of the json. It is kind of a json inside a json.
For example: with
print(plain)
I would recieve this data:
[{"cost":"0.45","id":0,"servise":"VK.com","serviseImg":"bg-vk","number":"Numbers: 3047","value":"vk"},{"cost":"0.07","id":1,"servise":"OK.ru","serviseImg":"bg-ok","number":"Numbers: 14397","value":"ok"},{"cost":"0.15","id":2,"servise":"Whatsapp","serviseImg":"bg-wa","number":"Numbers: 17610","value":"wa"},{"cost":"0.18","id":3,"servise":"Viber","serviseImg":"bg-vi","number":"Numbers: 413","value":"vi"},{"cost":"0.00","id":4,"servise":"Telegram","serviseImg":"bg-tg","number":"Numbers: 0","value":"tg"},{"cost":"0.30","id":5,"servise":"WeChat","serviseImg":"bg-wb","number":"Numbers: 28389","value":"wb"},{"cost":"0.15","id":6,"servise":"Google,youtube,Gmail","serviseImg":"bg-go","number":"Numbers: 15419","value":"go"},{"cost":"0.11","id":7,"servise":"avito","serviseImg":"bg-av","number":"Numbers: 17379","value":"av"},{"cost":"0.07","id":8,"servise":"facebook","serviseImg":"bg-fb","number":"Numbers: 21636","value":"fb"},{"cost":"0.03","id":9,"servise":"Twitter","serviseImg":"bg-tw","number":"Numbers: 26864","value":"tw"},{"cost":"0.05","id":10,"servise":"Uber","serviseImg":"bg-ub","number":"Numbers: 20975","value":"ub"},{"cost":"0.22","id":11,"servise":"Qiwi","serviseImg":"bg-qw","number":"Numbers: 20371","value":"qw"},{"cost":"0.02","id":12,"servise":"Gett","serviseImg":"bg-gt","number":"Numbers: 28501","value":"gt"},{"cost":"0.00","id":13,"servise":"OLX","serviseImg":"bg-sn","number":"Numbers: 0","value":"sn"},{"cost":"0.14","id":14,"servise":"Instagram","serviseImg":"bg-ig","number":"Numbers: 3799","value":"ig"},{"cost":"0.00","id":15,"servise":"Hezzl","serviseImg":"bg-ss","number":"Numbers: 0","value":"ss"},{"cost":"0.07","id":16,"servise":"\u042e\u043b\u0430","serviseImg":"bg-ym","number":"Numbers: 17597","value":"ym"},{"cost":"0.03","id":17,"servise":"Mail.ru","serviseImg":"bg-ma","number":"Numbers: 17936","value":"ma"},{"cost":"0.03","id":18,"servise":"Microsoft","serviseImg":"bg-mm","number":"Numbers: 3830","value":"mm"},{"cost":"0.03","id":19,"servise":"Airbnb","serviseImg":"bg-uk","number":"Numbers: 28266","value":"uk"},{"cost":"0.00","id":20,"servise":"Line messenger","serviseImg":"bg-me","number":"Numbers: 0","value":"me"},{"cost":"0.03","id":21,"servise":"Yahoo","serviseImg":"bg-mb","number":"Numbers: 19077","value":"mb"},{"cost":"0.00","id":22,"servise":"Drugvokrug.ru","serviseImg":"bg-we","number":"Numbers: 0","value":"we"},{"cost":"0.05","id":23,"servise":"5ka.ru","serviseImg":"bg-bd","number":"Numbers: 27541","value":"bd"},{"cost":"0.00","id":24,"servise":"HQ Trivia","serviseImg":"bg-kp","number":"Numbers: 0","value":"kp"},{"cost":"0.65","id":25,"servise":"Delivery Club","serviseImg":"bg-dt","number":"Numbers: 26812","value":"dt"},{"cost":"0.03","id":26,"servise":"Yandex","serviseImg":"bg-ya","number":"Numbers: 20944","value":"ya"},{"cost":"0.05","id":27,"servise":"Steam","serviseImg":"bg-mt","number":"Numbers: 28121","value":"mt"},{"cost":"0.04","id":28,"servise":"Tinder","serviseImg":"bg-oi","number":"Numbers: 27478","value":"oi"},{"cost":"0.02","id":29,"servise":"Mamba, MeetMe","serviseImg":"bg-fd","number":"Numbers: 25328","value":"fd"},{"cost":"0.00","id":30,"servise":"Dent","serviseImg":"bg-zz","number":"Numbers: 0","value":"zz"},{"cost":"0.07","id":31,"servise":"KakaoTalk","serviseImg":"bg-kt","number":"Numbers: 27823","value":"kt"},{"cost":"0.04","id":32,"servise":"AOL","serviseImg":"bg-pm","number":"Numbers: 23369","value":"pm"},{"cost":"0.02","id":33,"servise":"LinkedIN","serviseImg":"bg-tn","number":"Numbers: 32192","value":"tn"},{"cost":"0.03","id":34,"servise":"Tencent QQ","serviseImg":"bg-qq","number":"Numbers: 27883","value":"qq"},{"cost":"0.06","id":35,"servise":"Magnit","serviseImg":"bg-mg","number":"Numbers: 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0","value":"jb"},{"cost":"0.00","id":218,"servise":"Yaay","serviseImg":"bg-vn","number":"Numbers: 0","value":"vn"},{"cost":"0.00","id":219,"servise":"GameArena","serviseImg":"bg-wn","number":"Numbers: 0","value":"wn"},{"cost":"0.04","id":220,"servise":"Vita express","serviseImg":"bg-bj","number":"Numbers: 27861","value":"bj"},{"cost":"0.00","id":221,"servise":"Auchan","serviseImg":"bg-st","number":"Numbers: 0","value":"st"},{"cost":"0.00","id":222,"servise":"Picpay","serviseImg":"bg-ev","number":"Numbers: 0","value":"ev"},{"cost":"0.00","id":223,"servise":"Blued","serviseImg":"bg-qn","number":"Numbers: 0","value":"qn"},{"cost":"0.05","id":224,"servise":"Not on the list","serviseImg":"bg-ot","number":"Numbers: 19352","value":"ot"}]
The problem is that after my knowledge this is a wrong formatted json file, even if I remove "[" and "]".
I though of just looking at it as a text file to get a specific value but I was unable to find something on the internet about this topic.
I want to get the following value:
From:
{"cost":"0.00","id":123,"servise":"Nike","serviseImg":"bg-ew","number":"Numbers: 0","value":"ew"}
I want to get:
Numbers: 0

Use json.loads to become a dictionary query:
import requests
import json
response = requests.get('https://sms-service-online.com/api/getServicesAndCost/?country=0&operator=any&lang=en')
data = response.json()
plain = data["data"]
for d in json.loads(plain):
if d['id'] == 123:
print(d)

You have a list of dict in your first example. So first you need to select which dict you want. For instance, if your query is called 'json_list'
json_list[2]
will get you the third item in that list.
For what you are asking, try:
j = {"cost":"0.00","id":123,"servise":"Nike","serviseImg":"bg-ew","number":"Numbers: 0","value":"ew"}
j["number"].split()[-1]
Assuming you want to check all numbers in all dict, try:
json_list=[{"cost":"0.45","id":0,"servise":"VK.com","serviseImg":"bg-vk","number":"Numbers: 3047","value":"vk"},{"cost":"0.07","id":1,"servise":"OK.ru","serviseImg":"bg-ok","number":"Numbers: 14397","value":"ok"},{"cost":"0.15","id":2,"servise":"Whatsapp","serviseImg":"bg-wa","number":"Numbers: 17610","value":"wa"},{"cost":"0.18","id":3,"servise":"Viber","serviseImg":"bg-vi","number":"Numbers: 413","value":"vi"},{"cost":"0.00","id":4,"servise":"Telegram","serviseImg":"bg-tg","number":"Numbers: 0","value":"tg"},{"cost":"0.30","id":5,"servise":"WeChat","serviseImg":"bg-wb","number":"Numbers: 28389","value":"wb"},{"cost":"0.15","id":6,"servise":"Google,youtube,Gmail","serviseImg":"bg-go","number":"Numbers: 15419","value":"go"},{"cost":"0.11","id":7,"servise":"avito","serviseImg":"bg-av","number":"Numbers: 17379","value":"av"},{"cost":"0.07","id":8,"servise":"facebook","serviseImg":"bg-fb","number":"Numbers: 21636","value":"fb"},{"cost":"0.03","id":9,"servise":"Twitter","serviseImg":"bg-tw","number":"Numbers: 26864","value":"tw"},{"cost":"0.05","id":10,"servise":"Uber","serviseImg":"bg-ub","number":"Numbers: 20975","value":"ub"},{"cost":"0.22","id":11,"servise":"Qiwi","serviseImg":"bg-qw","number":"Numbers: 20371","value":"qw"},{"cost":"0.02","id":12,"servise":"Gett","serviseImg":"bg-gt","number":"Numbers: 28501","value":"gt"},{"cost":"0.00","id":13,"servise":"OLX","serviseImg":"bg-sn","number":"Numbers: 0","value":"sn"},{"cost":"0.14","id":14,"servise":"Instagram","serviseImg":"bg-ig","number":"Numbers: 3799","value":"ig"},{"cost":"0.00","id":15,"servise":"Hezzl","serviseImg":"bg-ss","number":"Numbers: 0","value":"ss"},{"cost":"0.07","id":16,"servise":"\u042e\u043b\u0430","serviseImg":"bg-ym","number":"Numbers: 17597","value":"ym"},{"cost":"0.03","id":17,"servise":"Mail.ru","serviseImg":"bg-ma","number":"Numbers: 17936","value":"ma"},{"cost":"0.03","id":18,"servise":"Microsoft","serviseImg":"bg-mm","number":"Numbers: 3830","value":"mm"},{"cost":"0.03","id":19,"servise":"Airbnb","serviseImg":"bg-uk","number":"Numbers: 28266","value":"uk"},{"cost":"0.00","id":20,"servise":"Line messenger","serviseImg":"bg-me","number":"Numbers: 0","value":"me"},{"cost":"0.03","id":21,"servise":"Yahoo","serviseImg":"bg-mb","number":"Numbers: 19077","value":"mb"},{"cost":"0.00","id":22,"servise":"Drugvokrug.ru","serviseImg":"bg-we","number":"Numbers: 0","value":"we"},{"cost":"0.05","id":23,"servise":"5ka.ru","serviseImg":"bg-bd","number":"Numbers: 27541","value":"bd"},{"cost":"0.00","id":24,"servise":"HQ Trivia","serviseImg":"bg-kp","number":"Numbers: 0","value":"kp"},{"cost":"0.65","id":25,"servise":"Delivery Club","serviseImg":"bg-dt","number":"Numbers: 26812","value":"dt"},{"cost":"0.03","id":26,"servise":"Yandex","serviseImg":"bg-ya","number":"Numbers: 20944","value":"ya"},{"cost":"0.05","id":27,"servise":"Steam","serviseImg":"bg-mt","number":"Numbers: 28121","value":"mt"},{"cost":"0.04","id":28,"servise":"Tinder","serviseImg":"bg-oi","number":"Numbers: 27478","value":"oi"},{"cost":"0.02","id":29,"servise":"Mamba, MeetMe","serviseImg":"bg-fd","number":"Numbers: 25328","value":"fd"},{"cost":"0.00","id":30,"servise":"Dent","serviseImg":"bg-zz","number":"Numbers: 0","value":"zz"},{"cost":"0.07","id":31,"servise":"KakaoTalk","serviseImg":"bg-kt","number":"Numbers: 27823","value":"kt"},{"cost":"0.04","id":32,"servise":"AOL","serviseImg":"bg-pm","number":"Numbers: 23369","value":"pm"},{"cost":"0.02","id":33,"servise":"LinkedIN","serviseImg":"bg-tn","number":"Numbers: 32192","value":"tn"},{"cost":"0.03","id":34,"servise":"Tencent QQ","serviseImg":"bg-qq","number":"Numbers: 27883","value":"qq"},{"cost":"0.06","id":35,"servise":"Magnit","serviseImg":"bg-mg","number":"Numbers: 993","value":"mg"},{"cost":"0.00","id":36,"servise":"pof.com","serviseImg":"bg-pf","number":"Numbers: 0","value":"pf"},{"cost":"0.03","id":37,"servise":"Yalla","serviseImg":"bg-yl","number":"Numbers: 27822","value":"yl"},{"cost":"0.00","id":38,"servise":"kolesa.kz","serviseImg":"bg-kl","number":"Numbers: 0","value":"kl"},{"cost":"0.03","id":39,"servise":"premium.one","serviseImg":"bg-po","number":"Numbers: 25869","value":"po"},{"cost":"0.07","id":40,"servise":"Naver","serviseImg":"bg-nv","number":"Numbers: 27582","value":"nv"},{"cost":"0.03","id":41,"servise":"Netflix","serviseImg":"bg-nf","number":"Numbers: 27715","value":"nf"},{"cost":"0.00","id":42,"servise":"icq","serviseImg":"bg-iq","number":"Numbers: 0","value":"iq"},{"cost":"0.00","id":43,"servise":"Onlinerby","serviseImg":"bg-ob","number":"Numbers: 0","value":"ob"},{"cost":"0.00","id":44,"servise":"kufarby","serviseImg":"bg-kb","number":"Numbers: 0","value":"kb"},{"cost":"0.03","id":45,"servise":"Imo","serviseImg":"bg-im","number":"Numbers: 25198","value":"im"},{"cost":"0.00","id":46,"servise":"Michat","serviseImg":"bg-mc","number":"Numbers: 0","value":"mc"},{"cost":"0.03","id":47,"servise":"Discord","serviseImg":"bg-ds","number":"Numbers: 22011","value":"ds"},{"cost":"0.00","id":48,"servise":"Seosprint","serviseImg":"bg-vv","number":"Numbers: 0","value":"vv"},{"cost":"0.00","id":49,"servise":"Monobank","serviseImg":"bg-ji","number":"Numbers: 0","value":"ji"},{"cost":"0.03","id":50,"servise":"TikTok\/Douyin","serviseImg":"bg-lf","number":"Numbers: 26299","value":"lf"},{"cost":"0.00","id":51,"servise":"Ukrnet","serviseImg":"bg-hu","number":"Numbers: 0","value":"hu"},{"cost":"0.00","id":52,"servise":"Skout","serviseImg":"bg-wg","number":"Numbers: 0","value":"wg"},{"cost":"0.00","id":53,"servise":"EasyPay","serviseImg":"bg-rz","number":"Numbers: 0","value":"rz"},{"cost":"0.00","id":54,"servise":"Q12 Trivia","serviseImg":"bg-vf","number":"Numbers: 0","value":"vf"},{"cost":"0.00","id":55,"servise":"Pyro Music","serviseImg":"bg-ny","number":"Numbers: 0","value":"ny"},{"cost":"0.00","id":56,"servise":"Wolt","serviseImg":"bg-rr","number":"Numbers: 0","value":"rr"},{"cost":"0.00","id":57,"servise":"CliQQ","serviseImg":"bg-fe","number":"Numbers: 0","value":"fe"},{"cost":"0.00","id":58,"servise":"ssoidnet","serviseImg":"bg-la","number":"Numbers: 0","value":"la"},{"cost":"0.00","id":59,"servise":"Zoho","serviseImg":"bg-zh","number":"Numbers: 0","value":"zh"},{"cost":"0.00","id":60,"servise":"Ticketmaster","serviseImg":"bg-gp","number":"Numbers: 0","value":"gp"},{"cost":"0.05","id":61,"servise":"Amazon","serviseImg":"bg-am","number":"Numbers: 25313","value":"am"},{"cost":"0.00","id":62,"servise":"Olacabs","serviseImg":"bg-ly","number":"Numbers: 0","value":"ly"},{"cost":"0.00","id":63,"servise":"Rambler","serviseImg":"bg-tc","number":"Numbers: 0","value":"tc"},{"cost":"0.02","id":64,"servise":"ProtonMail","serviseImg":"bg-dp","number":"Numbers: 30668","value":"dp"},{"cost":"0.00","id":65,"servise":"NRJ Music Awards","serviseImg":"bg-pg","number":"Numbers: 0","value":"pg"},{"cost":"0.03","id":66,"servise":"Citymobil","serviseImg":"bg-yf","number":"Numbers: 26997","value":"yf"},{"cost":"0.00","id":67,"servise":"MIRATORG","serviseImg":"bg-op","number":"Numbers: 0","value":"op"},{"cost":"0.03","id":68,"servise":"PGbonus","serviseImg":"bg-fx","number":"Numbers: 27563","value":"fx"},{"cost":"0.03","id":69,"servise":"MEGA","serviseImg":"bg-qr","number":"Numbers: 27848","value":"qr"},{"cost":"0.03","id":70,"servise":"SportMaster","serviseImg":"bg-yk","number":"Numbers: 26584","value":"yk"},{"cost":"0.03","id":71,"servise":"Careem","serviseImg":"bg-ls","number":"Numbers: 27873","value":"ls"},{"cost":"0.03","id":72,"servise":"BIGO LIVE","serviseImg":"bg-bl","number":"Numbers: 25421","value":"bl"},{"cost":"0.03","id":73,"servise":"MyMusicTaste","serviseImg":"bg-mu","number":"Numbers: 27898","value":"mu"},{"cost":"0.03","id":74,"servise":"Snapchat","serviseImg":"bg-fu","number":"Numbers: 27758","value":"fu"},{"cost":"0.00","id":75,"servise":"Keybase","serviseImg":"bg-bf","number":"Numbers: 0","value":"bf"},{"cost":"0.03","id":76,"servise":"OZON","serviseImg":"bg-sg","number":"Numbers: 26595","value":"sg"},{"cost":"0.03","id":77,"servise":"Wildberries","serviseImg":"bg-uu","number":"Numbers: 27220","value":"uu"},{"cost":"0.05","id":78,"servise":"BlaBlaCar","serviseImg":"bg-ua","number":"Numbers: 24367","value":"ua"},{"cost":"0.09","id":79,"servise":"Alibaba","serviseImg":"bg-ab","number":"Numbers: 17818","value":"ab"},{"cost":"0.00","id":80,"servise":"Inboxlv","serviseImg":"bg-iv","number":"Numbers: 0","value":"iv"},{"cost":"0.00","id":81,"servise":"Nttgame","serviseImg":"bg-zy","number":"Numbers: 0","value":"zy"},{"cost":"0.00","id":82,"servise":"Surveytime","serviseImg":"bg-gd","number":"Numbers: 0","value":"gd"},{"cost":"0.00","id":83,"servise":"Mylove","serviseImg":"bg-fy","number":"Numbers: 0","value":"fy"},{"cost":"0.02","id":84,"servise":"mosru","serviseImg":"bg-ce","number":"Numbers: 27173","value":"ce"},{"cost":"0.00","id":85,"servise":"Truecaller","serviseImg":"bg-tl","number":"Numbers: 0","value":"tl"},{"cost":"0.00","id":86,"servise":"Globus","serviseImg":"bg-hm","number":"Numbers: 0","value":"hm"},{"cost":"0.03","id":87,"servise":"Bolt","serviseImg":"bg-tx","number":"Numbers: 27378","value":"tx"},{"cost":"0.00","id":88,"servise":"Shopee","serviseImg":"bg-ka","number":"Numbers: 0","value":"ka"},{"cost":"0.03","id":89,"servise":"Perekrestok.ru","serviseImg":"bg-pl","number":"Numbers: 27883","value":"pl"},{"cost":"0.03","id":90,"servise":"Burger King","serviseImg":"bg-ip","number":"Numbers: 27764","value":"ip"},{"cost":"0.00","id":91,"servise":"Prom","serviseImg":"bg-cm","number":"Numbers: 0","value":"cm"},{"cost":"0.03","id":92,"servise":"AliPay","serviseImg":"bg-hw","number":"Numbers: 27166","value":"hw"},{"cost":"0.00","id":93,"servise":"Karusel","serviseImg":"bg-de","number":"Numbers: 0","value":"de"},{"cost":"0.00","id":94,"servise":"IVI","serviseImg":"bg-jc","number":"Numbers: 0","value":"jc"},{"cost":"0.03","id":95,"servise":"inDriver","serviseImg":"bg-rl","number":"Numbers: 27534","value":"rl"},{"cost":"0.03","id":96,"servise":"Happn","serviseImg":"bg-df","number":"Numbers: 27785","value":"df"},{"cost":"0.00","id":97,"servise":"RuTube","serviseImg":"bg-ui","number":"Numbers: 0","value":"ui"},{"cost":"0.03","id":98,"servise":"Magnolia","serviseImg":"bg-up","number":"Numbers: 27884","value":"up"},{"cost":"0.03","id":99,"servise":"Foodpanda","serviseImg":"bg-nz","number":"Numbers: 27493","value":"nz"},{"cost":"0.00","id":100,"servise":"Weibo","serviseImg":"bg-kf","number":"Numbers: 0","value":"kf"},{"cost":"0.00","id":101,"servise":"BillMill","serviseImg":"bg-ri","number":"Numbers: 0","value":"ri"},{"cost":"0.00","id":102,"servise":"Quipp","serviseImg":"bg-cc","number":"Numbers: 0","value":"cc"},{"cost":"0.00","id":103,"servise":"Okta","serviseImg":"bg-lr","number":"Numbers: 0","value":"lr"},{"cost":"0.03","id":104,"servise":"JDcom","serviseImg":"bg-za","number":"Numbers: 27891","value":"za"},{"cost":"0.05","id":105,"servise":"MTS CashBack","serviseImg":"bg-da","number":"Numbers: 12860","value":"da"},{"cost":"0.00","id":106,"servise":"Fiqsy","serviseImg":"bg-ug","number":"Numbers: 0","value":"ug"},{"cost":"0.00","id":107,"servise":"KuCoinPlay","serviseImg":"bg-sq","number":"Numbers: 0","value":"sq"},{"cost":"0.00","id":108,"servise":"Papara","serviseImg":"bg-zr","number":"Numbers: 0","value":"zr"},{"cost":"0.00","id":109,"servise":"Wish","serviseImg":"bg-xv","number":"Numbers: 0","value":"xv"},{"cost":"0.00","id":110,"servise":"Icrypex","serviseImg":"bg-cx","number":"Numbers: 0","value":"cx"},{"cost":"0.00","id":111,"servise":"PaddyPower","serviseImg":"bg-cw","number":"Numbers: 0","value":"cw"},{"cost":"0.05","id":112,"servise":"Baidu","serviseImg":"bg-li","number":"Numbers: 27813","value":"li"},{"cost":"0.00","id":113,"servise":"Dominos Pizza","serviseImg":"bg-dz","number":"Numbers: 0","value":"dz"},{"cost":"0.00","id":114,"servise":"paycell","serviseImg":"bg-xz","number":"Numbers: 0","value":"xz"},{"cost":"0.03","id":115,"servise":"Lenta","serviseImg":"bg-rd","number":"Numbers: 27651","value":"rd"},{"cost":"0.00","id":116,"servise":"Payberry","serviseImg":"bg-qb","number":"Numbers: 0","value":"qb"},{"cost":"0.03","id":117,"servise":"Drom","serviseImg":"bg-hz","number":"Numbers: 27673","value":"hz"},{"cost":"0.00","id":118,"servise":"GlobalTel","serviseImg":"bg-gl","number":"Numbers: 0","value":"gl"},{"cost":"0.00","id":119,"servise":"Deliveroo","serviseImg":"bg-zk","number":"Numbers: 0","value":"zk"},{"cost":"0.00","id":120,"servise":"Socios","serviseImg":"bg-ia","number":"Numbers: 0","value":"ia"},{"cost":"0.00","id":121,"servise":"Wmaraci","serviseImg":"bg-xl","number":"Numbers: 0","value":"xl"},{"cost":"0.00","id":122,"servise":"Yemeksepeti","serviseImg":"bg-yi","number":"Numbers: 0","value":"yi"},{"cost":"0.00","id":123,"servise":"Nike","serviseImg":"bg-ew","number":"Numbers: 0","value":"ew"},{"cost":"0.02","id":124,"servise":"myGLO","serviseImg":"bg-ae","number":"Numbers: 31671","value":"ae"},{"cost":"0.02","id":125,"servise":"YouStar","serviseImg":"bg-gb","number":"Numbers: 31133","value":"gb"},{"cost":"0.15","id":126,"servise":"\u0420\u0421\u0410","serviseImg":"bg-cy","number":"Numbers: 25178","value":"cy"},{"cost":"0.00","id":127,"servise":"RosaKhutor","serviseImg":"bg-qm","number":"Numbers: 0","value":"qm"},{"cost":"0.00","id":128,"servise":"eBay","serviseImg":"bg-dh","number":"Numbers: 0","value":"dh"},{"cost":"0.00","id":129,"servise":"Pay.kvartplata.ru+","serviseImg":"bg-yb","number":"Numbers: 0","value":"yb"},{"cost":"0.00","id":130,"servise":"GG","serviseImg":"bg-qe","number":"Numbers: 0","value":"qe"},{"cost":"0.03","id":131,"servise":"Grindr","serviseImg":"bg-yw","number":"Numbers: 4841","value":"yw"},{"cost":"0.00","id":132,"servise":"OffGamers","serviseImg":"bg-uz","number":"Numbers: 0","value":"uz"},{"cost":"0.00","id":133,"servise":"Hepsiburadacom","serviseImg":"bg-gx","number":"Numbers: 0","value":"gx"},{"cost":"0.00","id":134,"servise":"Coinbase","serviseImg":"bg-re","number":"Numbers: 0","value":"re"},{"cost":"0.00","id":135,"servise":"RADA NABU","serviseImg":"bg-tj","number":"Numbers: 0","value":"tj"},{"cost":"0.09","id":136,"servise":"PayPal","serviseImg":"bg-ts","number":"Numbers: 27144","value":"ts"},{"cost":"0.00","id":137,"servise":"hily","serviseImg":"bg-rt","number":"Numbers: 0","value":"rt"},{"cost":"0.00","id":138,"servise":"SneakersnStuff","serviseImg":"bg-sf","number":"Numbers: 0","value":"sf"},{"cost":"0.00","id":139,"servise":"Dostavista","serviseImg":"bg-sv","number":"Numbers: 0","value":"sv"},{"cost":"0.00","id":140,"servise":"32red","serviseImg":"bg-qi","number":"Numbers: 0","value":"qi"},{"cost":"0.02","id":141,"servise":"Blizzard","serviseImg":"bg-bz","number":"Numbers: 27797","value":"bz"},{"cost":"0.00","id":142,"servise":"ezbuy","serviseImg":"bg-db","number":"Numbers: 0","value":"db"},{"cost":"0.00","id":143,"servise":"CoinField","serviseImg":"bg-vw","number":"Numbers: 0","value":"vw"},{"cost":"0.00","id":144,"servise":"Airtel","serviseImg":"bg-zl","number":"Numbers: 0","value":"zl"},{"cost":"0.00","id":145,"servise":"Airtel","serviseImg":"bg-wf","number":"Numbers: 0","value":"wf"},{"cost":"0.00","id":146,"servise":"MrGreen","serviseImg":"bg-lw","number":"Numbers: 0","value":"lw"},{"cost":"0.00","id":147,"servise":"Rediffmail","serviseImg":"bg-co","number":"Numbers: 0","value":"co"},{"cost":"0.00","id":148,"servise":"miloan","serviseImg":"bg-ey","number":"Numbers: 0","value":"ey"},{"cost":"0.00","id":149,"servise":"Paytm","serviseImg":"bg-ge","number":"Numbers: 0","value":"ge"},{"cost":"0.00","id":150,"servise":"Dhani","serviseImg":"bg-os","number":"Numbers: 0","value":"os"},{"cost":"0.00","id":151,"servise":"CMTcuzdan","serviseImg":"bg-ql","number":"Numbers: 0","value":"ql"},{"cost":"0.00","id":152,"servise":"Mercado","serviseImg":"bg-cq","number":"Numbers: 0","value":"cq"},{"cost":"0.05","id":153,"servise":"DiDi","serviseImg":"bg-xk","number":"Numbers: 26447","value":"xk"},{"cost":"0.00","id":154,"servise":"Monese","serviseImg":"bg-py","number":"Numbers: 0","value":"py"},{"cost":"0.00","id":155,"servise":"Kotak811","serviseImg":"bg-rv","number":"Numbers: 0","value":"rv"},{"cost":"0.00","id":156,"servise":"Hopi","serviseImg":"bg-jl","number":"Numbers: 0","value":"jl"},{"cost":"0.00","id":157,"servise":"Trendyol","serviseImg":"bg-pr","number":"Numbers: 0","value":"pr"},{"cost":"0.00","id":158,"servise":"Justdating","serviseImg":"bg-pu","number":"Numbers: 0","value":"pu"},{"cost":"0.00","id":159,"servise":"Pairs","serviseImg":"bg-dk","number":"Numbers: 0","value":"dk"},{"cost":"0.00","id":160,"servise":"Touchance","serviseImg":"bg-fm","number":"Numbers: 0","value":"fm"},{"cost":"0.00","id":161,"servise":"SnappFood","serviseImg":"bg-ph","number":"Numbers: 0","value":"ph"},{"cost":"0.00","id":162,"servise":"NCsoft","serviseImg":"bg-sw","number":"Numbers: 0","value":"sw"},{"cost":"0.00","id":163,"servise":"Tosla","serviseImg":"bg-nr","number":"Numbers: 0","value":"nr"},{"cost":"0.00","id":164,"servise":"Ininal","serviseImg":"bg-hy","number":"Numbers: 0","value":"hy"},{"cost":"0.00","id":165,"servise":"Paysend","serviseImg":"bg-tr","number":"Numbers: 0","value":"tr"},{"cost":"0.00","id":166,"servise":"CDkeys","serviseImg":"bg-pq","number":"Numbers: 0","value":"pq"},{"cost":"0.00","id":167,"servise":"AVON","serviseImg":"bg-ff","number":"Numbers: 0","value":"ff"},{"cost":"0.03","id":168,"servise":"dodopizza","serviseImg":"bg-sd","number":"Numbers: 27739","value":"sd"},{"cost":"0.24","id":169,"servise":"McDonalds","serviseImg":"bg-ry","number":"Numbers: 7729","value":"ry"},{"cost":"0.00","id":170,"servise":"E bike Gewinnspiel","serviseImg":"bg-le","number":"Numbers: 0","value":"le"},{"cost":"0.00","id":171,"servise":"JKF","serviseImg":"bg-hr","number":"Numbers: 0","value":"hr"},{"cost":"0.00","id":172,"servise":"MyFishka","serviseImg":"bg-qa","number":"Numbers: 0","value":"qa"},{"cost":"0.00","id":173,"servise":"Craigslist","serviseImg":"bg-wc","number":"Numbers: 0","value":"wc"},{"cost":"0.00","id":174,"servise":"Foody","serviseImg":"bg-kw","number":"Numbers: 0","value":"kw"},{"cost":"0.00","id":175,"servise":"Grab","serviseImg":"bg-jg","number":"Numbers: 0","value":"jg"},{"cost":"0.05","id":176,"servise":"Zalo","serviseImg":"bg-mj","number":"Numbers: 28606","value":"mj"},{"cost":"0.00","id":177,"servise":"LiveScore","serviseImg":"bg-eu","number":"Numbers: 0","value":"eu"},{"cost":"0.00","id":178,"servise":"888casino","serviseImg":"bg-ll","number":"Numbers: 0","value":"ll"},{"cost":"0.00","id":179,"servise":"Gamer","serviseImg":"bg-ed","number":"Numbers: 0","value":"ed"},{"cost":"0.00","id":180,"servise":"Huya","serviseImg":"bg-pp","number":"Numbers: 0","value":"pp"},{"cost":"0.00","id":181,"servise":"WestStein","serviseImg":"bg-th","number":"Numbers: 0","value":"th"},{"cost":"0.04","id":182,"servise":"Tango","serviseImg":"bg-xr","number":"Numbers: 27765","value":"xr"},{"cost":"0.00","id":183,"servise":"Global24","serviseImg":"bg-iz","number":"Numbers: 0","value":"iz"},{"cost":"0.03","id":184,"servise":"\u041c\u0412\u0438\u0434\u0435\u043e","serviseImg":"bg-tk","number":"Numbers: 27660","value":"tk"},{"cost":"0.00","id":185,"servise":"Sheerid","serviseImg":"bg-rx","number":"Numbers: 0","value":"rx"},{"cost":"0.00","id":186,"servise":"99app","serviseImg":"bg-ki","number":"Numbers: 0","value":"ki"},{"cost":"0.00","id":187,"servise":"CAIXA","serviseImg":"bg-my","number":"Numbers: 0","value":"my"},{"cost":"0.00","id":188,"servise":"OfferUp","serviseImg":"bg-zm","number":"Numbers: 0","value":"zm"},{"cost":"0.00","id":189,"servise":"Swvl","serviseImg":"bg-tq","number":"Numbers: 0","value":"tq"},{"cost":"0.00","id":190,"servise":"Haraj","serviseImg":"bg-au","number":"Numbers: 0","value":"au"},{"cost":"0.00","id":191,"servise":"Taksheel","serviseImg":"bg-ei","number":"Numbers: 0","value":"ei"},{"cost":"0.00","id":192,"servise":"hamrahaval","serviseImg":"bg-rp","number":"Numbers: 0","value":"rp"},{"cost":"0.00","id":193,"servise":"Gamekit","serviseImg":"bg-pa","number":"Numbers: 0","value":"pa"},{"cost":"0.00","id":194,"servise":" \u015eikayet var","serviseImg":"bg-fs","number":"Numbers: 0","value":"fs"},{"cost":"0.00","id":195,"servise":"Getir","serviseImg":"bg-ul","number":"Numbers: 0","value":"ul"},{"cost":"0.00","id":196,"servise":"irancell","serviseImg":"bg-cf","number":"Numbers: 0","value":"cf"},{"cost":"0.00","id":197,"servise":"Alfa","serviseImg":"bg-bt","number":"Numbers: 0","value":"bt"},{"cost":"0.00","id":198,"servise":"Disney Hotstar","serviseImg":"bg-ud","number":"Numbers: 0","value":"ud"},{"cost":"0.00","id":199,"servise":"Agroinform","serviseImg":"bg-qu","number":"Numbers: 0","value":"qu"},{"cost":"0.00","id":200,"servise":"humblebundle","serviseImg":"bg-un","number":"Numbers: 0","value":"un"},{"cost":"0.00","id":201,"servise":"Faberlic","serviseImg":"bg-rm","number":"Numbers: 0","value":"rm"},{"cost":"0.00","id":202,"servise":"CafeBazaar","serviseImg":"bg-uo","number":"Numbers: 0","value":"uo"},{"cost":"0.00","id":203,"servise":"cryptocom","serviseImg":"bg-ti","number":"Numbers: 0","value":"ti"},{"cost":"0.00","id":204,"servise":"Gittigidiyor","serviseImg":"bg-nk","number":"Numbers: 0","value":"nk"},{"cost":"0.00","id":205,"servise":"mzadqatar","serviseImg":"bg-jm","number":"Numbers: 0","value":"jm"},{"cost":"0.00","id":206,"servise":"Algida","serviseImg":"bg-lp","number":"Numbers: 0","value":"lp"},{"cost":"0.00","id":207,"servise":"Cita Previa","serviseImg":"bg-si","number":"Numbers: 0","value":"si"},{"cost":"0.00","id":208,"servise":"Potato Chat","serviseImg":"bg-fj","number":"Numbers: 0","value":"fj"},{"cost":"0.00","id":209,"servise":"Bitaqaty","serviseImg":"bg-pt","number":"Numbers: 0","value":"pt"},{"cost":"0.00","id":210,"servise":"Primaries 2020","serviseImg":"bg-qc","number":"Numbers: 0","value":"qc"},{"cost":"0.00","id":211,"servise":"Amasia","serviseImg":"bg-yo","number":"Numbers: 0","value":"yo"},{"cost":"0.00","id":212,"servise":"Dream11","serviseImg":"bg-ve","number":"Numbers: 0","value":"ve"},{"cost":"0.00","id":213,"servise":"Oriflame","serviseImg":"bg-qh","number":"Numbers: 0","value":"qh"},{"cost":"0.00","id":214,"servise":"Bykea","serviseImg":"bg-iu","number":"Numbers: 0","value":"iu"},{"cost":"0.00","id":215,"servise":"Immowelt","serviseImg":"bg-ib","number":"Numbers: 0","value":"ib"},{"cost":"0.00","id":216,"servise":"Digikala","serviseImg":"bg-zv","number":"Numbers: 0","value":"zv"},{"cost":"0.00","id":217,"servise":"Wing Money","serviseImg":"bg-jb","number":"Numbers: 0","value":"jb"},{"cost":"0.00","id":218,"servise":"Yaay","serviseImg":"bg-vn","number":"Numbers: 0","value":"vn"},{"cost":"0.00","id":219,"servise":"GameArena","serviseImg":"bg-wn","number":"Numbers: 0","value":"wn"},{"cost":"0.04","id":220,"servise":"Vita express","serviseImg":"bg-bj","number":"Numbers: 27861","value":"bj"},{"cost":"0.00","id":221,"servise":"Auchan","serviseImg":"bg-st","number":"Numbers: 0","value":"st"},{"cost":"0.00","id":222,"servise":"Picpay","serviseImg":"bg-ev","number":"Numbers: 0","value":"ev"},{"cost":"0.00","id":223,"servise":"Blued","serviseImg":"bg-qn","number":"Numbers: 0","value":"qn"},{"cost":"0.05","id":224,"servise":"Not on the list","serviseImg":"bg-ot","number":"Numbers: 19352","value":"ot"}]
for j in json_list:
n = i['number'].split()[-1]
print(n)
# do something interesting with n

Output not displaying full list of elements appended

from csv import reader
def func(sku_list):
values = []
with open(sku_list, 'r', encoding = 'utf-8') as pr:
rows = reader(pr)
for sku in rows:
values.append(sku[1])
return(values)
if __name__ == '__main__':
dir_path = "C:/Users/XXXX/Downloads/"
vendors = dir_path + 'file.csv'
new_prices = func(vendors)
print(new_prices)
sku_list is a csv file filled with pairs of brand names and their skus that I have downloaded from my db, for some reason as it iters through the rows and grabs just the sku value, hence sku[1], it stops well short of the actual length I expect the list to be
sku_list is 85,892 tuples long but when I print out the values appended to the list values it simply returns this:
['SKU', 'MWGB4896', 'MWGB4872', 'MWGB4848', 'MWGB3648', 'WGB4896', 'WGB4872', 'WGB4848', 'WGB3648', 'WGB2436', 'WGB1824', 'BKGB4896NT', 'BKGB4872NT', 'BKGB4848NT', 'BKGB3648NT', 'BKGB2436NT', 'BKGB1824NT', 'WFC2418G', 'WFC2418', 'WFC3624', 'WFC2418LB', 'WFC3648LB', 'WFC3624LB', 'WFC3624G', 'WFC3648G', 'WFC3648', 'LOWFC3624LB', 'LOWFC3624G', 'LOWFC3624', 'LOWFC2418LB', 'LOWFC2418G', 'LOWFC2418', 'LOWFC3648LB', 'LOWFC3648', 'LOWFC3648G', 'WM-7-B', 'WM-7-G', 'WM-7-BK', 'WMC-7', 'WM-7-R', 'APS-50', 'APS-70', 'APS-60', 'APS-84', 'SS15W', 'SC15W', 'SB15W', 'MFL-2W', 'WP-48', 'WP-40', 'WP-36', 'MP-48', 'MP-40', 'MP-36', 'OP-40', 'OP-36', 'OP-48', 'FFVSU96-2', 'FFVSU144-2', 'FFVSU192-2', '1-WA-1B', '1-WA-1BP', 'WCS-12', 'WCS-144', 'OPLD3416LSPP-2', 'OPLD3416LSPP-4', 'OPLD3416LSPP-5', 'OPLD3416LSPP-7', 'OPLD3416LSPP-8', 'OPLD1818LSPP-2', 'OPLD1818LSPP-4', 'OPLD1818LSPP-5', 'OPLD1818LSPP-7', 'OPLD1818LSPP-8', 'OPLD1818L-2', 'OPLD1818L-5', 'OPLD1818L-4', 'OPLD3416L-2', 'OPLD3416L-4', 'OPLD3416L-5', 'OPLD3416L-7', 'OPLD3416L-8', 'OPLD1818L-7', 'OPLD1818L-8', 'OPLD3416SPP-8-892', 'OPLD3416SPP-8-897', 'OPLD3416SPP-8-878', 'OPLD3416SPP-8-885', 'OPLD3416SPP-8-887', 'OPLD3416SPP-8-890', 'OPLD3416SPP-8-845', 'OPLD3416SPP-8-854', 'OPLD3416SPP-8-856', 'OPLD3416SPP-8-876', 'OPLD3416SPP-8-802', 'OPLD3416SPP-8-706', 'OPLD3416SPP-8-705', 'OPLD3416SPP-8-704', 'OPLD3416SPP-8-837', 'OPLD3416SPP-8-831', 'OPLD3416SPP-8-819', 'OPLD3416SPP-8-812', 'OPLD3416SPP-8-685', 'OPLD3416SPP-8-683', 'OPLD3416SPP-8-679', 'OPLD3416SPP-8-531', 'OPLD3416SPP-8-703', 'OPLD3416SPP-8-702', 'OPLD3416SPP-8-701', 'OPLD3416SPP-8-700', 'OPLD3416SPP-7-892', 'OPLD3416SPP-7-897', 'OPLD3416SPP-7-887', 'OPLD3416SPP-7-890', 'OPLD3416SPP-8-530', 'OPLD3416SPP-7-845', 'OPLD3416SPP-7-854', 'OPLD3416SPP-7-831', 'OPLD3416SPP-7-837', 'OPLD3416SPP-7-878', 'OPLD3416SPP-7-885', 'OPLD3416SPP-7-856', 'OPLD3416SPP-7-876', 'OPLD3416SPP-7-703', 'OPLD3416SPP-7-702', 'OPLD3416SPP-7-705', 'OPLD3416SPP-7-704', 'OPLD3416SPP-7-802', 'OPLD3416SPP-7-706', 'OPLD3416SPP-7-819', 'OPLD3416SPP-7-812', 'OPLD3416SPP-7-530', 'OPLD3416SPP-7-679', 'OPLD3416SPP-7-531', 'OPLD3416SPP-7-685', 'OPLD3416SPP-7-683', 'OPLD3416SPP-7-701', 'OPLD3416SPP-7-700', 'OPLD3416SPP-5-878', 'OPLD3416SPP-5-885', 'OPLD3416SPP-5-887', 'OPLD3416SPP-5-890', 'OPLD3416SPP-5-892', 'OPLD3416SPP-5-897', 'OPLD3416SPP-5-812', 'OPLD3416SPP-5-819', 'OPLD3416SPP-5-831', 'OPLD3416SPP-5-837', 'OPLD3416SPP-5-845', 'OPLD3416SPP-5-854', 'OPLD3416SPP-5-856', 'OPLD3416SPP-5-876', 'OPLD1818SPP-8-819', 'OPLD1818SPP-8-831', 'OPLD1818SPP-8-802', 'OPLD1818SPP-8-812', 'OPLD1818SPP-8-854', 'OPLD1818SPP-8-856', 'OPLD1818SPP-8-837', 'OPLD1818SPP-8-845', 'OPLD1818SPP-8-701', 'OPLD1818SPP-8-702', 'OPLD1818SPP-8-685', 'OPLD1818SPP-8-700', 'OPLD1818SPP-8-705', 'OPLD1818SPP-8-706', 'OPLD1818SPP-8-703', 'OPLD1818SPP-8-704', 'OPLD1818SPP-8-887', 'OPLD1818SPP-8-885', 'OPLD1818SPP-8-878', 'OPLD1818SPP-8-876', 'OPLD1818SPP-8-897', 'OPLD1818SPP-8-892', 'OPLD1818SPP-8-890', 'OPLD3416SPP-4-837', 'OPLD3416SPP-4-831', 'OPLD3416SPP-4-854', 'OPLD3416SPP-4-845', 'OPLD3416SPP-4-802', 'OPLD3416SPP-4-706',
'OPLD3416SPP-4-819', 'OPLD3416SPP-4-812', 'OPLD3416SPP-4-890', 'OPLD3416SPP-4-887', 'OPLD3416SPP-4-897', 'OPLD3416SPP-4-892', 'OPLD3416SPP-4-876', 'OPLD3416SPP-4-856', 'OPLD3416SPP-4-885', 'OPLD3416SPP-4-878', 'OPLD3416SPP-5-531', 'OPLD3416SPP-5-679', 'OPLD3416SPP-5-683', 'OPLD3416SPP-5-685', 'OPLD3416SPP-5-530', 'OPLD3416SPP-5-704', 'OPLD3416SPP-5-705', 'OPLD3416SPP-5-706', 'OPLD3416SPP-5-802', 'OPLD3416SPP-5-700', 'OPLD3416SPP-5-701', 'OPLD3416SPP-5-702', 'OPLD3416SPP-5-703', 'OPLD3416SPP-2-837', 'OPLD3416SPP-2-831', 'OPLD3416SPP-2-819', 'OPLD3416SPP-2-812', 'OPLD3416SPP-2-802', 'OPLD3416SPP-2-706', 'OPLD3416SPP-2-705', 'OPLD3416SPP-2-704', 'OPLD3416SPP-2-890', 'OPLD3416SPP-2-887', 'OPLD3416SPP-2-885', 'OPLD3416SPP-2-878', 'OPLD3416SPP-2-876', 'OPLD3416SPP-2-856', 'OPLD3416SPP-2-854', 'OPLD3416SPP-2-845', 'OPLD3416SPP-4-531', 'OPLD3416SPP-4-679', 'OPLD3416SPP-4-530', 'OPLD3416SPP-2-892', 'OPLD3416SPP-2-897', 'OPLD3416SPP-4-704', 'OPLD3416SPP-4-705', 'OPLD3416SPP-4-702', 'OPLD3416SPP-4-703', 'OPLD3416SPP-4-700', 'OPLD3416SPP-4-701', 'OPLD3416SPP-4-683', 'OPLD3416SPP-4-685', 'OPLD3416SPP-2-530', 'OPLD3416SPP-2-531', 'OPLD3416SPP-2-679', 'OPLD3416SPP-2-683', 'OPLD3416SPP-2-685', 'OPLD3416SPP-2-700', 'OPLD3416SPP-2-701', 'OPLD3416SPP-2-702', 'OPLD3416SPP-2-703', 'OPLD1818SPP-7-819', 'OPLD1818SPP-7-831', 'OPLD1818SPP-7-837', 'OPLD1818SPP-7-845', 'OPLD1818SPP-7-705', 'OPLD1818SPP-7-706', 'OPLD1818SPP-7-802', 'OPLD1818SPP-7-812', 'OPLD1818SPP-7-701', 'OPLD1818SPP-7-702', 'OPLD1818SPP-7-703', 'OPLD1818SPP-7-704', 'OPLD1818SPP-7-679', 'OPLD1818SPP-7-683', 'OPLD1818SPP-7-685', 'OPLD1818SPP-7-700', 'OPLD1818SPP-8-531', 'OPLD1818SPP-8-530', 'OPLD1818SPP-8-683', 'OPLD1818SPP-8-679', 'OPLD1818SPP-7-897', 'OPLD1818SPP-7-887', 'OPLD1818SPP-7-885', 'OPLD1818SPP-7-892', 'OPLD1818SPP-7-890', 'OPLD1818SPP-7-856', 'OPLD1818SPP-7-854', 'OPLD1818SPP-7-878', 'OPLD1818SPP-7-876', 'OPLD1818SPP-5-819', 'OPLD1818SPP-5-831', 'OPLD1818SPP-5-802', 'OPLD1818SPP-5-812', 'OPLD1818SPP-5-705', 'OPLD1818SPP-5-706', 'OPLD1818SPP-5-703', 'OPLD1818SPP-5-704', 'OPLD1818SPP-5-701', 'OPLD1818SPP-5-702', 'OPLD1818SPP-5-685', 'OPLD1818SPP-5-700', 'OPLD1818SPP-5-679', 'OPLD1818SPP-5-683', 'OPLD1818SPP-5-530', 'OPLD1818SPP-5-531', 'OPLD1818SPP-7-531', 'OPLD1818SPP-7-530', 'OPLD1818SPP-5-897', 'OPLD1818SPP-5-892', 'OPLD1818SPP-5-890', 'OPLD1818SPP-5-887', 'OPLD1818SPP-5-885', 'OPLD1818SPP-5-878', 'OPLD1818SPP-5-876', 'OPLD1818SPP-5-856', 'OPLD1818SPP-5-854', 'OPLD1818SPP-5-845', 'OPLD1818SPP-5-837', 'OPLD1818SPP-4-701', 'OPLD1818SPP-4-702', 'OPLD1818SPP-4-703', 'OPLD1818SPP-4-704', 'OPLD1818SPP-4-705', 'OPLD1818SPP-4-706', 'OPLD1818SPP-4-802', 'OPLD1818SPP-4-812', 'OPLD1818SPP-4-530', 'OPLD1818SPP-4-531', 'OPLD1818SPP-4-679', 'OPLD1818SPP-4-683', 'OPLD1818SPP-4-685', 'OPLD1818SPP-4-700', 'OPLD1818SPP-4-887', 'OPLD1818SPP-2-837', 'OPLD1818SPP-2-845', 'OPLD1818SPP-2-854', 'OPLD1818SPP-2-856', 'OPLD1818SPP-2-802', 'OPLD1818SPP-2-812', 'OPLD1818SPP-2-819', 'OPLD1818SPP-2-831', 'OPLD1818SPP-2-890', 'OPLD1818SPP-2-892', 'OPLD1818SPP-2-897', 'OPLD1818SPP-2-876', 'OPLD1818SPP-2-878', 'OPLD1818SPP-2-885', 'OPLD1818SPP-2-887', 'OPLD1818SPP-2-531', 'OPLD1818SPP-2-530', 'OPLD1818SPP-2-683', 'OPLD1818SPP-2-679', 'OPLD1818SPP-2-704', 'OPLD1818SPP-2-703',
'OPLD1818SPP-2-706', 'OPLD1818SPP-2-705', 'OPLD1818SPP-2-700', 'OPLD1818SPP-2-685', 'OPLD1818SPP-2-702', 'OPLD1818SPP-2-701', 'OPLD1818SPP-4-876', 'OPLD1818SPP-4-878', 'OPLD1818SPP-4-854', 'OPLD1818SPP-4-856', 'OPLD1818SPP-4-837', 'OPLD1818SPP-4-845', 'OPLD1818SPP-4-819', 'OPLD1818SPP-4-831', 'OPLD1818SPP-4-897', 'OPLD1818SPP-4-890', 'OPLD1818SPP-4-892', 'OPLD1818SPP-4-885', 'PLD4832DPP-2-845', 'PLD4832DPP-2-837', 'PLD4832DPP-2-856', 'PLD4832DPP-2-854', 'PLD4832DPP-2-878', 'PLD4832DPP-2-876', 'PLD4832DPP-2-887', 'PLD4832DPP-2-885', 'PLD4832DPP-2-892', 'PLD4832DPP-2-890', 'PLD4832DPP-2-897', 'PLD4832DPP-4-531', 'PLD4832DPP-4-530', 'PLD4832DPP-4-679', 'PLD4832DPP-4-683', 'PLD4832DPP-4-685', 'PLD4832DPP-4-700', 'PLD4832DPP-4-701', 'PLD4832DPP-4-702', 'PLD4832DPP-4-703', 'PLD4832DPP-4-704', 'PLD4832DPP-4-705', 'PLD4832DPP-4-706', 'PLD4832DPP-4-802', 'PLD4832DPP-4-812', 'PLD4832DPP-4-819', 'PLD4832DPP-4-831', 'PLD4832DPP-4-837', 'PLD4832DPP-4-845', 'PLD4832DPP-4-878', 'PLD4832DPP-4-876', 'PLD4832DPP-4-856', 'PLD4832DPP-4-854', 'PLD4832DPP-4-892', 'PLD4832DPP-4-890', 'PLD4832DPP-4-887', 'PLD4832DPP-4-885', 'PLD4832DPP-4-897', 'PLD4832DPP-5-683', 'PLD4832DPP-5-679', 'PLD4832DPP-5-531', 'PLD4832DPP-5-530', 'PLD4832DPP-5-701', 'PLD4832DPP-5-702', 'PLD4832DPP-5-685', 'PLD4832DPP-5-700', 'PLD4832DPP-5-705', 'PLD4832DPP-5-706', 'PLD4832DPP-5-703', 'PLD4832DPP-5-704', 'PLD4832DPP-5-819', 'PLD4832DPP-5-831', 'PLD4832DPP-5-802', 'PLD4832DPP-5-812', 'PLD4832DPP-5-854', 'PLD4832DPP-5-856', 'PLD4832DPP-5-837', 'PLD4832DPP-5-845', 'PLD4832DPP-2-701', 'PLD4832DPP-2-702', 'PLD4832DPP-2-685', 'PLD4832DPP-2-700', 'PLD4832DPP-2-679', 'PLD4832DPP-2-683', 'PLD4832DPP-2-530', 'PLD4832DPP-2-531', 'PLD4832DPP-2-819', 'PLD4832DPP-2-831', 'PLD4832DPP-2-802', 'PLD4832DPP-2-812', 'PLD4832DPP-2-705', 'PLD4832DPP-2-706', 'PLD4832DPP-2-703', 'PLD4832DPP-2-704', 'PLD4226DPP-8-887', 'PLD4226DPP-8-890', 'PLD4226DPP-8-892', 'PLD4226DPP-8-897', 'PLD4226DPP-8-856', 'PLD4226DPP-8-876', 'PLD4226DPP-8-878', 'PLD4226DPP-8-885', 'PLD4226DPP-8-831', 'PLD4226DPP-8-837', 'PLD4226DPP-8-845', 'PLD4226DPP-8-854', 'PLD4226DPP-8-706', 'PLD4226DPP-8-802', 'PLD4226DPP-8-812', 'PLD4226DPP-8-819', 'PLD4226DPP-5-892', 'PLD4226DPP-5-897', 'PLD4226DPP-5-887', 'PLD4226DPP-5-890', 'PLD4226DPP-7-530', 'PLD4226DPP-7-683', 'PLD4226DPP-7-685', 'PLD4226DPP-7-531', 'PLD4226DPP-7-679', 'PLD4226DPP-7-702', 'PLD4226DPP-7-703', 'PLD4226DPP-7-700', 'PLD4226DPP-7-701', 'PLD4226DPP-5-705', 'PLD4226DPP-5-704', 'PLD4226DPP-5-703', 'PLD4226DPP-5-702', 'PLD4226DPP-5-819', 'PLD4226DPP-5-812', 'PLD4226DPP-5-802', 'PLD4226DPP-5-706', 'PLD4226DPP-5-854', 'PLD4226DPP-5-845', 'PLD4226DPP-5-837', 'PLD4226DPP-5-831', 'PLD4226DPP-5-885', 'PLD4226DPP-5-878', 'PLD4226DPP-5-876', 'PLD4226DPP-5-856', 'PLD4226DPP-7-892', 'PLD4226DPP-7-897', 'PLD4226DPP-8-530', 'PLD4226DPP-8-531', 'PLD4226DPP-8-679', 'PLD4226DPP-8-683', 'PLD4226DPP-8-685', 'PLD4226DPP-8-700', 'PLD4226DPP-8-701', 'PLD4226DPP-8-702', 'PLD4226DPP-8-703', 'PLD4226DPP-8-704', 'PLD4226DPP-8-705', 'PLD4226DPP-7-705', 'PLD4226DPP-7-704', 'PLD4226DPP-7-802', 'PLD4226DPP-7-706', 'PLD4226DPP-7-819', 'PLD4226DPP-7-812', 'PLD4226DPP-7-837', 'PLD4226DPP-7-831', 'PLD4226DPP-7-854', 'PLD4226DPP-7-845', 'PLD4226DPP-7-876', 'PLD4226DPP-7-856', 'PLD4226DPP-7-885', 'PLD4226DPP-7-878', 'PLD4226DPP-7-890', 'PLD4226DPP-7-887', 'PLD4226DPP-2-892', 'PLD4226DPP-2-897', 'PLD4226DPP-2-887', 'PLD4226DPP-2-890', 'PLD4226DPP-2-878', 'PLD4226DPP-2-885', 'PLD4226DPP-2-856', 'PLD4226DPP-2-876', 'PLD4226DPP-4-683', 'PLD4226DPP-4-685', 'PLD4226DPP-4-531', 'PLD4226DPP-4-679', 'PLD4226DPP-4-530', 'PLD4226DPP-2-705', 'PLD4226DPP-2-704', 'PLD4226DPP-2-703', 'PLD4226DPP-2-702', 'PLD4226DPP-2-701', 'PLD4226DPP-2-700', 'PLD4226DPP-2-685', 'PLD4226DPP-2-683', 'PLD4226DPP-2-854', 'PLD4226DPP-2-845', 'PLD4226DPP-2-837', 'PLD4226DPP-2-831', 'PLD4226DPP-2-819', 'PLD4226DPP-2-812', 'PLD4226DPP-2-802', 'PLD4226DPP-2-706', 'PLD4226DPP-4-892', 'PLD4226DPP-4-897', 'PLD4226DPP-4-878', 'PLD4226DPP-4-885', 'PLD4226DPP-4-887', 'PLD4226DPP-4-890', 'PLD4226DPP-5-683', 'PLD4226DPP-5-685', 'PLD4226DPP-5-700', 'PLD4226DPP-5-701', 'PLD4226DPP-5-530', 'PLD4226DPP-5-531', 'PLD4226DPP-5-679', 'PLD4226DPP-4-705', 'PLD4226DPP-4-704', 'PLD4226DPP-4-802', 'PLD4226DPP-4-706', 'PLD4226DPP-4-701', 'PLD4226DPP-4-700', 'PLD4226DPP-4-703', 'PLD4226DPP-4-702', 'PLD4226DPP-4-854', 'PLD4226DPP-4-845', 'PLD4226DPP-4-876', 'PLD4226DPP-4-856', 'PLD4226DPP-4-819', 'PLD4226DPP-4-812', 'PLD4226DPP-4-837', 'PLD4226DPP-4-831', 'PLD4226DPP-2-530', 'PLD4226DPP-2-679', 'PLD4226DPP-2-531', 'PLD5438DPP-5-683', 'PLD5438DPP-5-685', 'PLD5438DPP-5-531', 'PLD5438DPP-5-679', 'PLD5438DPP-5-702', 'PLD5438DPP-5-703', 'PLD5438DPP-5-700', 'PLD5438DPP-5-701', 'PLD5438DPP-5-706', 'PLD5438DPP-5-802', 'PLD5438DPP-5-704', 'PLD5438DPP-5-705', 'PLD5438DPP-5-831', 'PLD5438DPP-5-837', 'PLD5438DPP-5-812', 'PLD5438DPP-5-819', 'PLD5438DPP-5-876', 'PLD5438DPP-5-856', 'PLD5438DPP-5-854', 'PLD5438DPP-5-845', 'PLD5438DPP-5-890', 'PLD5438DPP-5-887', 'PLD5438DPP-5-885', 'PLD5438DPP-5-878', 'PLD5438DPP-5-897', 'PLD5438DPP-5-892', 'PLD5438DPP-7-679', 'PLD5438DPP-7-531', 'PLD5438DPP-7-530', 'PLD5438DPP-4-530', 'PLD5438DPP-4-531', 'PLD5438DPP-4-679', 'PLD5438DPP-4-683', 'PLD5438DPP-4-685', 'PLD5438DPP-4-700', 'PLD5438DPP-4-701', 'PLD5438DPP-4-702', 'PLD5438DPP-4-703', 'PLD5438DPP-4-704', 'PLD5438DPP-4-705', 'PLD5438DPP-4-706', 'PLD5438DPP-4-802', 'PLD5438DPP-4-812', 'PLD5438DPP-4-819', 'PLD5438DPP-4-837', 'PLD5438DPP-4-831', 'PLD5438DPP-4-854', 'PLD5438DPP-4-845', 'PLD5438DPP-4-876', 'PLD5438DPP-4-856', 'PLD5438DPP-4-885', 'PLD5438DPP-4-878', 'PLD5438DPP-4-890', 'PLD5438DPP-4-887', 'PLD5438DPP-4-897', 'PLD5438DPP-4-892', 'PLD5438DPP-5-530',
'PLD5438DPP
None
the final sku in there PLD5438DPP should be PLD5438DPP-5-683 and for some reason the list cuts off there, which is only element 564/85,892, and the program terminates without an error code
I cannot attach the file of skus, this is for my job, just hoping someone can shed light on what I am doing to cause the list to cut short like that
this may or may not also be relevant but when I call .append(sku) as opposed to sku[1] and grab the whole tuple the same issue occurs but at element 292, exactly half of the amount of element appended when only doing half the tuple

The issue seems to be one on my local machine, where it was unable to print such a long list, this one was of size 85,892
for those with similar issues see if any of our specs are overlapping and that may determine the cause of this issue:
VSCode:
Version: 1.52.0 (user setup)
Commit: 940b5f4bb5fa47866a54529ed759d95d09ee80be
Date: 2020-12-10T22:45:11.850Z
Electron: 9.3.5
Chrome: 83.0.4103.122
Node.js: 12.14.1
V8: 8.3.110.13-electron.0
OS: Windows_NT x64 10.0.18363
Python:
3.9.0
see discussion in comments for other details

Glove6b50d parsing: could not convert string to float: '-'

I am trying to parse the Glove6b50d data from Kaggle in via Google Colab, then run it through the word2vec process (apologies for the huge URL - it's the fastest link I've found). However, I'm hitting a bug where '-' tokens are not parsed correctly, resulting in the above error.
I have attempted to handle this in a few ways. I've also looked into the load_word2vec_format method itself and tried to ignore errors, however it doesn't seem to make a difference. I've tried a map method on line two, following combinations of advice from these links: [a] and [b]. This hasn't fixed or changed the error message received (i.e. removing it changes nothing in the text).
gloveFile = pd.read_fwf("https://storage.googleapis.com/kagglesdsdata/datasets/652874/1154868/glove.6B.50d.txt?GoogleAccessId=web-data#kaggle-161607.iam.gserviceaccount.com&Expires=1589683535&Signature=kaS%2FTkSmvp7lhqwLJ%2B1lyuvP76PcDpwK1dnsCZEO0AiVXqQm7jsBc1r5g9af%2BuVkOSvMgqUDXYL4O%2BN43pnL5RLs7ns%2B3w%2BEtCYDTfJz6q1O0zfPz4%2BTcD3GV7UAGgVjVNIvncC9fHWcd2YuKwiZaTvKL%2BGRnMkf9b%2BYnOweYeXEeA1sX005krj%2FLMBbVTXmDTwOtN4HwVNb3%2BrbezkWkoEC6sxLPnGcsEKaBe%2Biv%2FuVSQG5FsQlwvRgsSU%2FMgk0c4bi%2FHxF04lrQW0E0s767TIXwHeodRHYpk5KQeKmyd91uKD2Zb8v8xQcf2%2BkmSNGQHbX0mDz8HBwYEmOdV7aMQ%3D%3D&response-content-disposition=attachment%3B+filename%3Dglove.6B.50d.txt",
delimiter="\n\t\s+", header=None)
map(lambda gloveFile: gloveFile.replace(r'[^\x00-\x7F]+' , '-'), gloveFile[0])
numpy.savetxt(r'/usr/local/lib/python3.6/dist-packages/gensim/test/test_data/glove6b50d.txt', gloveFile.values, fmt="%s")
from gensim.models import KeyedVectors
from gensim.test.utils import datapath, get_tmpfile
from gensim.scripts.glove2word2vec import glove2word2vec
glove_file = datapath('glove6b50d.txt')
glove2word2vec(glove_file, "glove6b50d_word2vec.txt")
model = KeyedVectors.load_word2vec_format("glove6b50d_word2vec.txt", binary=False)
Per the comment below, the exact error I'm getting is as follows:
/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:253: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function
'See the migration notes for details: %s' % _MIGRATION_NOTES_URL
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-132-6ad5a51f4fb3> in <module>()
9 glove2word2vec(glove_file, "glove6b50d_word2vec.txt")
10
---> 11 model = KeyedVectors.load_word2vec_format("glove6b50d_word2vec.txt", binary=False)
12
2 frames
/usr/local/lib/python3.6/dist-packages/gensim/models/utils_any2vec.py in <listcomp>(.0)
220 if len(parts) != vector_size + 1:
221 raise ValueError("invalid vector on line %s (is this really the text format?)" % line_no)
--> 222 word, weights = parts[0], [datatype(x) for x in parts[1:]]
223 add_word(word, weights)
224 if result.vectors.shape[0] != len(result.vocab):
ValueError: could not convert string to float: '-'
The system works fine using a text file containing only: "test -1.0 1.526 -2.55" or "- -1.0 1.526 -2.55". Additionally, searching the source text file (glove.6B.50d.txt) for occurrences of " - " comes up with no results. I'm on Windows, so I have done so by executing:
findstr /C:" - " glove.6B.50d.txt
Calling print(gloveFile) both pre- and post-map call provide the following output. Note that I've kept the mapping call in for completeness of my efforts, not for its effect.
0 the 0.418 0.24968 -0.41242 0.1217 0.34527 -0.0...
1 , 0.013441 0.23682 -0.16899 0.40951 0.63812 0....
2 . 0.15164 0.30177 -0.16763 0.17684 0.31719 0.3...
3 of 0.70853 0.57088 -0.4716 0.18048 0.54449 0.7...
4 to 0.68047 -0.039263 0.30186 -0.17792 0.42962 ...
... ...
399995 chanty 0.23204 0.025672 -0.70699 -0.045465 0.1...
399996 kronik -0.60921 -0.67218 0.23521 -0.11195 -0.4...
399997 rolonda -0.51181 0.058706 1.0913 -0.55163 -0.1...
399998 zsombor -0.75898 -0.47426 0.4737 0.7725 -0.780...
399999 andberger 0.072617 -0.51393 0.4728 -0.52202 -0...
If I print the first ten lines of the glove6b50d_word2vec.txt file, I get the following text, which matches the word2vec format. Additionally, if I count the occurrences of the string " - " in the document, I find none.
['400000 50\n', 'the 0.418 0.24968 -0.41242 0.1217 0.34527 -0.044457 -0.49688 -0.17862 -0.00066023 -0.6566 0.27843 -0.14767 -0.55677 0.14658 -0.0095095 0.011658 0.10204 -0.12792 -0.8443 -0.12181 -0.016801 -0.33279 -0.1552 -0.23131 -0.19181 -1.8823 -0.76746 0.099051 -0.42125 -0.19526 4.0071 -0.18594 -0.52287 -0.31681 0.00059213 0.0074449 0.17778 -0.15897 0.012041 -0.054223 -0.29871 -0.15749 -0.34758 -0.045637 -0.44251 0.18785 0.0027849 -0.18411 -0.11514 -0.78581\n', ', 0.013441 0.23682 -0.16899 0.40951 0.63812 0.47709 -0.42852 -0.55641 -0.364 -0.23938 0.13001 -0.063734 -0.39575 -0.48162 0.23291 0.090201 -0.13324 0.078639 -0.41634 -0.15428 0.10068 0.48891 0.31226 -0.1252 -0.037512 -1.5179 0.12612 -0.02442 -0.042961 -0.28351 3.5416 -0.11956 -0.014533 -0.1499 0.21864 -0.33412 -0.13872 0.31806 0.70358 0.44858 -0.080262 0.63003 0.32111 -0.46765 0.22786 0.36034 -0.37818 -0.56657 0.044691 0.30392\n', '. 0.15164 0.30177 -0.16763 0.17684 0.31719 0.33973 -0.43478 -0.31086 -0.44999 -0.29486 0.16608 0.11963 -0.41328 -0.42353 0.59868 0.28825 -0.11547 -0.041848 -0.67989 -0.25063 0.18472 0.086876 0.46582 0.015035 0.043474 -1.4671 -0.30384 -0.023441 0.30589 -0.21785 3.746 0.0042284 -0.18436 -0.46209 0.098329 -0.11907 0.23919 0.1161 0.41705 0.056763 -6.3681e-05 0.068987 0.087939 -0.10285 -0.13931 0.22314 -0.080803 -0.35652 0.016413 0.10216\n', 'of 0.70853 0.57088 -0.4716 0.18048 0.54449 0.72603 0.18157 -0.52393 0.10381 -0.17566 0.078852 -0.36216 -0.11829 -0.83336 0.11917 -0.16605 0.061555 -0.012719 -0.56623 0.013616 0.22851 -0.14396 -0.067549 -0.38157 -0.23698 -1.7037 -0.86692 -0.26704 -0.2589 0.1767 3.8676 -0.1613 -0.13273 -0.68881 0.18444 0.0052464 -0.33874 -0.078956 0.24185 0.36576 -0.34727 0.28483 0.075693 -0.062178 -0.38988 0.22902 -0.21617 -0.22562 -0.093918 -0.80375\n', 'to 0.68047 -0.039263 0.30186 -0.17792 0.42962 0.032246 -0.41376 0.13228 -0.29847 -0.085253 0.17118 0.22419 -0.10046 -0.43653 0.33418 0.67846 0.057204 -0.34448 -0.42785 -0.43275 0.55963 0.10032 0.18677 -0.26854 0.037334 -2.0932 0.22171 -0.39868 0.20912 -0.55725 3.8826 0.47466 -0.95658 -0.37788 0.20869 -0.32752 0.12751 0.088359 0.16351 -0.21634 -0.094375 0.018324 0.21048 -0.03088 -0.19722 0.082279 -0.09434 -0.073297 -0.064699 -0.26044\n', 'and 0.26818 0.14346 -0.27877 0.016257 0.11384 0.69923 -0.51332 -0.47368 -0.33075 -0.13834 0.2702 0.30938 -0.45012 -0.4127 -0.09932 0.038085 0.029749 0.10076 -0.25058 -0.51818 0.34558 0.44922 0.48791 -0.080866 -0.10121 -1.3777 -0.10866 -0.23201 0.012839 -0.46508 3.8463 0.31362 0.13643 -0.52244 0.3302 0.33707 -0.35601 0.32431 0.12041 0.3512 -0.069043 0.36885 0.25168 -0.24517 0.25381 0.1367 -0.31178 -0.6321 -0.25028 -0.38097\n', 'in 0.33042 0.24995 -0.60874 0.10923 0.036372 0.151 -0.55083 -0.074239 -0.092307 -0.32821 0.09598 -0.82269 -0.36717 -0.67009 0.42909 0.016496 -0.23573 0.12864 -1.0953 0.43334 0.57067 -0.1036 0.20422 0.078308 -0.42795 -1.7984 -0.27865 0.11954 -0.12689 0.031744 3.8631 -0.17786 -0.082434 -0.62698 0.26497 -0.057185 -0.073521 0.46103 0.30862 0.12498 -0.48609 -0.0080272 0.031184 -0.36576 -0.42699 0.42164 -0.11666 -0.50703 -0.027273 -0.53285\n', 'a 0.21705 0.46515 -0.46757 0.10082 1.0135 0.74845 -0.53104 -0.26256 0.16812 0.13182 -0.24909 -0.44185 -0.21739 0.51004 0.13448 -0.43141 -0.03123 0.20674 -0.78138 -0.20148 -0.097401 0.16088 -0.61836 -0.18504 -0.12461 -2.2526 -0.22321 0.5043 0.32257 0.15313 3.9636 -0.71365 -0.67012 0.28388 0.21738 0.14433 0.25926 0.23434 0.4274 -0.44451 0.13813 0.36973 -0.64289 0.024142 -0.039315 -0.26037 0.12017 -0.043782 0.41013 0.1796\n', '" 0.25769 0.45629 -0.76974 -0.37679 0.59272 -0.063527 0.20545 -0.57385 -0.29009 -0.13662 0.32728 1.4719 -0.73681 -0.12036 0.71354 -0.46098 0.65248 0.48887 -0.51558 0.039951 -0.34307 -0.014087 0.86488 0.3546 0.7999 -1.4995 -1.8153 0.41128 0.23921 -0.43139 3.6623 -0.79834 -0.54538 0.16943 -0.82017 -0.3461 0.69495 -1.2256 -0.17992 -0.057474 0.030498 -0.39543 -0.38515 -1.0002 0.087599 -0.31009 -0.34677 -0.31438 0.75004 0.97065\n']
My search methods are evidently thusfar ineffective. Would really appreciate some help.

In can't reproduce the problem running the following code (on a linux machine, Python 3.6):
In [1]: from gensim.models import KeyedVectors
In [2]: from gensim.scripts.glove2word2vec import glove2word2vec
In [3]: glove2word2vec('glove.6B.50d.txt', 'glove.68.50d.w2v.txt')
Out[3]: (400000, 50)
In [4]: model = KeyedVectors.load_word2vec_format('glove.68.50d.w2v.txt')
In [5]: len(model)
Out[5]: 400000
In [6]: model['the']
Out[7]:
array([ 4.1800e-01, 2.4968e-01, -4.1242e-01, 1.2170e-01, 3.4527e-01,
-4.4457e-02, -4.9688e-01, -1.7862e-01, -6.6023e-04, -6.5660e-01,
2.7843e-01, -1.4767e-01, -5.5677e-01, 1.4658e-01, -9.5095e-03,
1.1658e-02, 1.0204e-01, -1.2792e-01, -8.4430e-01, -1.2181e-01,
-1.6801e-02, -3.3279e-01, -1.5520e-01, -2.3131e-01, -1.9181e-01,
-1.8823e+00, -7.6746e-01, 9.9051e-02, -4.2125e-01, -1.9526e-01,
4.0071e+00, -1.8594e-01, -5.2287e-01, -3.1681e-01, 5.9213e-04,
7.4449e-03, 1.7778e-01, -1.5897e-01, 1.2041e-02, -5.4223e-02,
-2.9871e-01, -1.5749e-01, -3.4758e-01, -4.5637e-02, -4.4251e-01,
1.8785e-01, 2.7849e-03, -1.8411e-01, -1.1514e-01, -7.8581e-01],
dtype=float32)
Do these exact lines trigger the exact same error as originally reported for you? (If you still get an error, but the error is even the slightest bit different, can you add the updated error to your question?)
My best guess if you're still having a problem is some Windows-specific default-encoding mangling during one of the steps, or if the file was opened/saved in some other editor.

Tensorflow synapse output and input as text file

I have a object-detection output result with tensorflow.
which is a 13*13*125 float32 array. (tiny-yolo-v2)
which I can output with a text file.
Now I want to write another program, to parse the text file.
How can I use numpy or something else, to load the text file back to array?
I just want to load the text file, and place with the right position,
just like it's original (13*13*125) array, so i can do something with it.
Below is the sample of text output, full file is too long so i truncate some of it.
[[[ 1.02357492e-02 -4.58745286e-02 -3.53223026e-01 1.05358012e-01
-4.35989094e+00 -6.99408484e+00 -4.73687983e+00 -4.24568987e+00
-5.61564398e+00 -2.89398456e+00 -6.23602247e+00 -1.50982809e+00
-5.22811985e+00 -2.62615561e+00 -5.22997189e+00 -7.89167452e+00
-5.44317961e+00 -6.20110512e+00 -5.04192924e+00 -2.02722573e+00
-4.10300493e+00 -6.34364843e+00 -5.39359808e+00 -8.28967571e+00
-5.09567547e+00 7.58925438e-01 3.76614660e-01 -1.04403806e+00
-6.47649229e-01 -3.87396884e+00 -6.38732052e+00 -4.58577824e+00
-4.16016054e+00 -5.30572081e+00 -3.85717177e+00 -6.05014038e+00
-1.81165671e+00 -3.75763679e+00 -2.88092995e+00 -4.67342377e+00
-4.71987104e+00 -4.39510489e+00 -5.11723280e+00 -5.39322329e+00
-2.66565347e+00 -3.81883574e+00 -5.66908169e+00 -4.71839476e+00
-6.30157852e+00 -4.82082224e+00 2.92960078e-01 5.97341239e-01
-1.40992713e+00 -1.11044288e+00 -4.18370819e+00 -7.12151051e+00
-5.35866928e+00 -4.90489340e+00 -5.99866199e+00 -5.80428791e+00
-6.52985144e+00 -3.30560398e+00 -5.25323820e+00 -3.49718428e+00
-5.35445309e+00 -4.74132204e+00 -5.51197958e+00 -5.49749136e+00
-5.92978096e+00 -3.28844571e+00 -5.49212742e+00 -6.66666460e+00
-4.93477535e+00 -7.05240107e+00 -5.52950144e+00 3.19003403e-01
4.28509444e-01 -1.37157345e+00 -7.52444148e-01 -5.07417107e+00
-6.86192179e+00 -5.41829157e+00 -4.65392542e+00 -6.23983860e+00
-5.52950287e+00 -5.39464998e+00 -3.28175688e+00 -4.32805681e+00
-3.33854008e+00 -5.02955580e+00 -5.26897430e+00 -4.58855247e+00
-5.45577812e+00 -7.06963253e+00 -3.20757794e+00 -4.94750690e+00
-6.26993036e+00 -5.85515499e+00 -7.07721949e+00 -5.31591606e+00
3.89474362e-01 3.98911506e-01 -1.40775967e+00 -1.07488203e+00
-3.43887043e+00 -7.79423666e+00 -6.68394804e+00 -5.61368179e+00
-6.34769297e+00 -6.83281803e+00 -6.70960665e+00 -3.65747070e+00
-5.44360542e+00 -4.12546444e+00 -5.75262260e+00 -5.18836164e+00
-5.15945435e+00 -6.07800007e+00 -6.70447493e+00 -4.12499619e+00
-6.12767649e+00 -6.96556044e+00 -4.83318233e+00 -7.69717407e+00
-6.56721640e+00]
[ 1.61234528e-01 -4.70345289e-01 2.98500329e-01 8.74740630e-03
-8.38169765e+00 -1.54907408e+01 -1.07314129e+01 -8.34741211e+00
-1.13781738e+01 -7.54940701e+00 -1.22155275e+01 -4.80996466e+00
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.
.
.
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I tried to use following format, it can read out something but the sequence is totally wrong
net_out = np.fromfile(filename, np.float32, count=13*13*125).reshape(13,13,125)

How to build Numpy array from a String written in file in Python

I had to write an array of 1024 dimensional vectors in files, where it is a string.
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-1.89860701e-03 1.70966927e-02 -2.60377172e-02 4.39567082e-02
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1.38295190e-02 2.13973895e-02 2.09906921e-02 6.11540861e-03]]]
I am trying to extract it using regex (or any other method if possible).
So far I tried solutions like Extract values between square brackets ignoring single quotes but they are not working.
How could I extract these arrays and use them as numpy vectors?

You can use the numpy.fromstring() method to accomplish this task once you have read-in the file's contents. Below is a quick and dirty implementation of this (no error detection), which works with your example array.
import numpy as np
with open("test.txt", 'r') as f:
arr = np.fromstring(f.read(), np.dtype(np.float), 1024)

This would also get you there, using regular expressions:
import numpy as np
import re
with open("somefile.txt") as f:
content = f.read()
content = re.sub('[\[\n\]]', '', content)
arr = re.split(r'\s+', content)
np_arr = np.array([float(i) for i in arr])

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