Develop Reference

List of coordinates to WKB hex

I am having some trouble when trying to load geojson data to Athena. I am trying to load world data boundaries (from this link)
The thing is, for some reason AWS Athena does not let me load MultiPolygon countries data (loading done based on this), only Polygons. As a result of this, only the first Polygon of a MultiPolygon is loaded.
Because of this, I am trying to find another solution to load the data. I have thought of the following:
Try to build a DataFrame with the name of the country, the list of coordinates and the corresponding type (Polygon or MultiPolygon). Depending on the type, generate a new column in wkb hex format accepted by Athena. Generate a partitioned parquet file and try to load it into Athena.
Any thoughts on how to do this?
Coordinates are coming in this format:
Polygon:
[[[-17.102499999999907, 32.823330000000055], [-17.05305999999996, 32.80944000000005], [-17.029999999999916, 32.810560000000066], [-17.013199999999927, 32.81346000000008], [-16.959999999999923, 32.830820000000074], [-16.913329999999917, 32.83916000000005], [-16.902219999999943, 32.83791000000008], [-16.715559999999925, 32.758890000000065], [-16.720839999999953, 32.74528000000004], [-16.819579999999917, 32.64611000000008], [-16.82188999999994, 32.64435000000009], [-16.83916999999991, 32.638610000000085], [-16.94360999999992, 32.637500000000045], [-16.988329999999905, 32.65527000000009], [-17.067779999999914, 32.67694000000006], [-17.102499999999907, 32.68333000000007], [-17.15805999999992, 32.709160000000054], [-17.19596999999993, 32.72888000000006], [-17.206389999999942, 32.73750000000007], [-17.232779999999934, 32.77000000000004], [-17.239169999999945, 32.77833000000004], [-17.254519999999957, 32.81284000000005], [-17.190699999999936, 32.86861000000005], [-17.169719999999927, 32.87028000000004], [-17.15888999999993, 32.865270000000066], [-17.151949999999943, 32.85750000000007], [-17.13333999999992, 32.838880000000074], [-17.12527999999992, 32.83194000000009], [-17.102499999999907, 32.823330000000055]]]
MultiPolygon:
[[[[111.41152000000005, 2.376390000000071], [111.36804000000006, 2.359580000000051], [111.35081000000008, 2.371940000000052], [111.30393000000004, 2.450000000000045], [111.30554000000006, 2.466390000000046], [111.31184000000007, 2.497220000000084], [111.30497000000003, 2.594440000000077], [111.29525000000007, 2.6808300000000713], [111.29332000000005, 2.73333000000008], [111.29540000000003, 2.748470000000054], [111.30359000000004, 2.767640000000085], [111.31248000000005, 2.775830000000041], [111.32895000000008, 2.780350000000055], [111.34539000000007, 2.771810000000073], [111.35443000000004, 2.760000000000047], [111.37776000000008, 2.708050000000071], [111.38165000000004, 2.698330000000055], [111.38388000000003, 2.680000000000062], [111.38388000000003, 2.652500000000088], [111.38026000000008, 2.615000000000066], [111.37693000000007, 2.536110000000064], [111.37692000000004, 2.495280000000093], [111.37831000000006, 2.482780000000048], [111.38109000000003, 2.471940000000074], [111.39943000000005, 2.408050000000059], [111.40583000000004, 2.393890000000056], [111.41152000000005, 2.376390000000071]]], [[[104.21191000000005, 2.7113900000000513], [104.16775000000007, 2.705280000000073], [104.15554000000003, 2.710550000000069], [104.14165000000003, 2.730830000000082], [104.12941000000006, 2.753890000000069], [104.12497000000008, 2.770000000000038], [104.12329000000005, 2.781670000000076], [104.16948000000008, 2.892500000000041], [104.18385000000006, 2.879440000000045], [104.18794000000003, 2.86779000000007], [104.18941000000007, 2.866390000000081], [104.19912000000005, 2.841940000000079], [104.20415000000003, 2.826670000000092], [104.21719000000007, 2.785560000000089], [104.21970000000005, 2.774440000000083], [104.22073000000006, 2.721870000000081], [104.21191000000005, 2.7113900000000513]]], [[[117.6869200000001, 4.168340000000057], [117.64373, 4.212990000000047], [117.63890000000004, 4.22861000000006], [117.64972, 4.238050000000044], [117.68581000000006, 4.259880000000066], [117.72388000000001, 4.260000000000048], [117.7460900000001, 4.258330000000058], [117.7602700000001, 4.255280000000084], [117.76971000000003, 4.25111000000004], [117.78554000000008, 4.239440000000059], [117.90356000000008, 4.174040000000048], [117.87944000000005, 4.173050000000046], [117.83971000000008, 4.171940000000063], [117.72664000000009, 4.169720000000041], [117.6869200000001, 4.168340000000057]]], [[[118.59604000000002, 4.638330000000053], [118.5681800000001, 4.599440000000072], [118.52970000000005, 4.600280000000055], [118.34987000000001, 4.67205000000007], [118.39462000000003, 4.676050000000089], [118.47887000000003, 4.689170000000047], [118.57640000000004, 4.650830000000042], [118.59604000000002, 4.638330000000053]]], [[[100.31762000000003, 5.335420000000056], [100.28704000000005, 5.254440000000045], [100.20386000000008, 5.271110000000078], [100.19609000000003, 5.293330000000083], [100.17968000000008, 5.427500000000066], [100.18580000000003, 5.462290000000053], [100.24774000000008, 5.466670000000079], [100.26110000000006, 5.466940000000079], [100.27275000000003, 5.464720000000057], [100.29413000000005, 5.458050000000071], [100.30525000000006, 5.451940000000093], [100.30827000000005, 5.4461200000000645], [100.32025000000004, 5.427780000000041], [100.32469000000003, 5.38083000000006], [100.31762000000003, 5.335420000000056]]], [[[99.86954000000003, 6.419300000000078], [99.89386000000007, 6.4019400000000815], [99.91545000000008, 6.387080000000083], [99.92302000000007, 6.333610000000078], [99.87162000000006, 6.288190000000043], [99.73956000000004, 6.248890000000074], [99.65694000000008, 6.360830000000078], [99.65221000000008, 6.369720000000086], [99.64695000000006, 6.385000000000048], [99.64193000000006, 6.407220000000052], [99.64222000000007, 6.422080000000051], [99.70332000000008, 6.426110000000051], [99.71666000000005, 6.425000000000068], [99.72747000000004, 6.4222200000000385], [99.73220000000003, 6.413050000000055], [99.74942000000004, 6.407780000000059], [99.79413000000005, 6.411670000000072], [99.85295000000008, 6.464150000000075], [99.85913000000005, 6.436390000000074], [99.86954000000003, 6.419300000000078]]], [[[102.09523000000007, 6.236140000000091], [102.12302000000005, 6.218050000000062], [102.16666000000004, 6.193610000000092], [102.18588000000005, 6.205730000000074], [102.22165000000007, 6.217500000000086], [102.31303000000003, 6.189440000000047], [102.33388000000008, 6.175550000000044], [102.35860000000008, 6.151670000000081], [102.38540000000006, 6.116530000000068], [102.41193000000004, 6.070830000000058], [102.43274000000008, 6.020000000000039], [102.48803000000004, 5.902640000000076], [102.50166000000007, 5.882080000000087], [102.53972000000005, 5.853170000000091], [102.58136000000007, 5.828610000000083], [102.60524000000004, 5.812220000000082], [102.62345000000005, 5.795690000000093], [102.64609000000007, 5.763470000000041], [102.66573000000005, 5.729860000000087], [102.84220000000005, 5.589300000000037], [102.87690000000003, 5.56833000000006], [102.92135000000007, 5.547430000000077], [102.96087000000006, 5.537010000000066], [103.03775000000007, 5.47694000000007], [103.06442000000004, 5.44805000000008], [103.09607000000005, 5.410280000000057], [103.12191000000007, 5.377360000000067], [103.18192000000005, 5.282780000000059], [103.20832000000007, 5.240550000000042], [103.22803000000005, 5.205550000000073], [103.24025000000006, 5.176940000000059], [103.24802000000005, 5.157500000000084], [103.26554000000004, 5.113330000000076], [103.27913000000007, 5.086110000000076], [103.35608000000008, 4.949170000000038], [103.40998000000008, 4.858050000000048], [103.44107000000008, 4.765280000000075], [103.45497000000006, 4.626940000000047], [103.45380000000006, 4.484790000000089], [103.46498000000003, 4.411670000000072], [103.47635000000008, 4.374720000000082], [103.48553000000004, 4.349860000000092], [103.49371000000008, 4.308750000000089], [103.48650000000004, 4.280550000000062], [103.46637000000004, 4.234440000000063], [103.44656000000003, 4.16644000000008], [103.41414000000003, 4.150760000000048], [103.39595000000003, 4.111390000000085], [103.39413000000008, 4.085000000000093], [103.40107000000006, 4.057780000000093], [103.40912000000003, 4.032220000000052], [103.41385000000008, 3.958890000000053], [103.37636000000003, 3.863060000000075], [103.33636000000007, 3.74410000000006], [103.37108000000006, 3.642780000000073], [103.38498000000004, 3.622220000000084], [103.42497000000003, 3.57278000000008], [103.44830000000007, 3.548060000000077], [103.46303000000006, 3.531810000000064], [103.47643000000005, 3.498680000000092], [103.45775000000003, 3.472780000000057], [103.44163000000003, 3.440140000000042], [103.42636000000005, 3.392500000000041], [103.42580000000004, 3.371670000000051], [103.42719000000005, 3.329720000000065], [103.43065000000007, 3.309720000000084], [103.43941000000007, 3.280560000000093], [103.44442000000004, 3.258330000000057], [103.45137000000005, 3.218330000000037], [103.45442000000003, 3.180970000000059], [103.45137000000005, 3.148610000000076], [103.44551000000007, 3.126530000000059], [103.43469000000005, 3.052780000000041], [103.43247000000008, 2.962220000000059], [103.43802000000005, 2.925830000000076], [103.45135000000005, 2.888190000000065], [103.46942000000007, 2.857780000000048], [103.48246000000006, 2.8366700000000833], [103.50165000000004, 2.808050000000094], [103.52246000000008, 2.780830000000094], [103.55469000000005, 2.7443100000000413], [103.59220000000005, 2.706110000000079], [103.61885000000007, 2.680560000000071], [103.63702000000006, 2.6640900000000443], [103.65331000000003, 2.660830000000089], [103.72775000000007, 2.640000000000043], [103.76526000000007, 2.625830000000064], [103.82025000000004, 2.575900000000047], [103.83386000000007, 2.517360000000053], [103.82706000000007, 2.476390000000037], [103.83637000000004, 2.455140000000085], [103.89832000000007, 2.385830000000055], [103.97746000000006, 2.243050000000039], [104.01969000000003, 2.136670000000038], [104.05830000000003, 2.059170000000051], [104.11580000000004, 1.966110000000071], [104.18830000000003, 1.8050000000000632], [104.22331000000003, 1.7179200000000492], [104.25359000000003, 1.633330000000057], [104.29329000000007, 1.437780000000088], ...]], [[[117.4333200000001, 6.628330000000062], [117.41235000000006, 6.6258300000000645], [117.35068000000001, 6.6409700000000385], [117.33859000000007, 6.649440000000084], [117.33527000000004, 6.658890000000042], [117.34274000000005, 6.672530000000052], [117.3986000000001, 6.676940000000059], [117.41278, 6.680000000000064], [117.42720000000008, 6.686110000000042], [117.43776000000003, 6.69264000000004], [117.44456000000002, 6.706110000000081], [117.43859000000009, 6.718330000000037], [117.42302000000007, 6.721670000000074], [117.40332000000001, 6.723120000000051], [117.40012000000002, 6.736800000000073], [117.4104000000001, 6.7470800000000395], [117.4679000000001, 6.761390000000063], [117.49192000000005, 6.744300000000067], [117.51486, 6.705830000000049], [117.51555000000008, 6.689440000000047], [117.51277000000005, 6.678610000000049], [117.50074000000006, 6.663330000000087], [117.46693000000005, 6.642500000000041], [117.45388000000003, 6.635000000000048], [117.44414000000006, 6.631110000000092], [117.4333200000001, 6.628330000000062]]], [[[117.59206000000006, 4.1698200000000725], [117.5288700000001, 4.175280000000043], [117.49775, 4.178890000000081], [117.47554000000002, 4.183610000000044], [117.45276000000001, 4.18861000000004], [117.43943000000002, 4.195830000000058], [117.42442000000005, 4.214720000000057], [117.4202600000001, 4.224720000000047], [117.41081000000008, 4.242500000000064], [117.40179, 4.254580000000089], [117.25179000000003, 4.353890000000092], [117.23944000000006, 4.35833000000008], [117.22360000000003, 4.358750000000043], [117.21111000000008, 4.354440000000068], [117.2027700000001, 4.345970000000079], [117.19179000000008, 4.336250000000064], [117.17665000000011, 4.335550000000069], [117.04387000000008, 4.336940000000084], [116.9369200000001, 4.350830000000087], [116.92442000000005, 4.352220000000045], [116.91053000000011, 4.352220000000045], [116.88916000000006, 4.349300000000085], [116.87693000000002, 4.345280000000059], [116.83985000000007, 4.33042000000006], [116.68877000000009, 4.33075000000008], [116.64194000000009, 4.334720000000061], [116.53360000000009, 4.330830000000049], [116.52762000000007, 4.319300000000055], [116.46582000000001, 4.294030000000077], [116.45416, 4.294580000000053], [116.4416500000001, 4.299440000000061], [116.42581000000007, 4.310000000000059], [116.31470000000002, 4.357780000000048], [116.15332000000001, 4.355000000000075], [116.1402700000001, 4.337220000000059], [116.07416, 4.283610000000067], [116.06110000000001, 4.2779200000000515], [116.04762000000005, 4.281110000000069], [116.03088000000002, 4.300760000000082], [116.0041500000001, 4.330000000000041], [115.98665000000005, 4.339720000000057], [115.97609, 4.343050000000062], [115.88804000000005, 4.36812000000009], [115.87248, 4.361110000000053], [115.85755000000006, 4.344100000000083], [115.85054000000002, 4.32167000000004], [115.7702700000001, 4.244720000000086], [115.76139, 4.239720000000091], [115.69081000000006, 4.180280000000039], [115.68331, 4.167360000000087], [115.6597200000001, 4.108600000000081], [115.66110000000003, 4.097780000000057], [115.66249000000005, 4.078330000000051], [115.61638000000005, 3.854170000000067], [115.58528000000001, 3.741670000000056], [115.57693000000006, 3.708610000000078], [115.57139000000006, 3.66611000000006], [115.57138000000009, 3.612780000000043], [115.57416, 3.594720000000052], [115.57887000000005, 3.585550000000069], [115.60193000000004, 3.539170000000069], [115.62608, 3.45778000000007], [115.62769000000003, 3.434300000000064], [115.61387000000002, 3.420420000000092], [115.59693000000004, 3.424720000000093], [115.58297000000005, 3.427990000000079], [115.57083, 3.4177800000000502], [115.55887000000007, 3.389170000000092], [115.55331000000001, 3.373890000000074], [115.53888000000006, 3.333610000000078], [115.53415000000007, 3.31833000000006], [115.52998000000002, 3.301670000000058], [115.51193, 3.2100000000000932], [115.50998000000004, 3.198330000000055], [115.51305000000002, 3.184030000000064], [115.52859000000001, 3.176110000000051], [115.4988800000001, 3.050280000000043], [115.49553000000003, 3.040000000000077], [115.48720000000003, 3.027500000000088], [115.37804000000006, 2.991800000000069], [115.31832000000009, 2.987780000000043], [115.31026000000008, 2.997780000000091], [115.30554000000006, 3.006670000000042], [115.30138000000011, 3.016390000000058], [115.24971000000005, 3.010830000000055], [115.15208000000007, 2.922080000000051], [115.13971000000004, 2.9061100000000692], [115.11832000000004, 2.851110000000062], [115.08236000000011, 2.613610000000051], [115.09206000000006, 2.600550000000055], [115.10762, 2.59722000000005], [115.12318000000005, 2.605550000000049], [115.1336, 2.611530000000073], [115.16944000000001, 2.605140000000062], [115.18525, 2.596940000000074], [115.19914000000006, 2.583050000000071], [115.2360900000001, 2.52965000000006], [115.23082000000011, 2.5080600000000572], [115.21568000000002, 2.492780000000039], [115.20387000000005, 2.487220000000093], ...]], [[[117.28298000000007, 7.319440000000043], [117.27832000000001, 7.250000000000057], [117.2763900000001, 7.238610000000051], [117.27304000000004, 7.228330000000085], [117.25054, 7.179170000000056], [117.19274000000007, 7.173330000000078], [117.17221000000006, 7.173330000000078], [117.1599900000001, 7.171940000000063], [117.14943000000005, 7.168610000000058], [117.1208200000001, 7.155550000000062], [117.09818000000007, 7.13722000000007], [117.0894300000001, 7.118890000000078], [117.07929000000001, 7.105000000000075], [117.06645000000003, 7.105070000000069], [117.05193000000008, 7.17083000000008], [117.06248000000005, 7.271670000000086], [117.07068000000004, 7.284030000000087], [117.08805000000007, 7.294170000000065], [117.0932600000001, 7.293450000000064], [117.14512000000002, 7.334030000000041], [117.22221000000002, 7.3527800000000525], [117.26818000000003, 7.343890000000044], [117.27832000000001, 7.333750000000066], [117.28298000000007, 7.319440000000043]]], [[[116.87471000000005, 7.221800000000087], [116.85694000000001, 7.183890000000076], [116.85234000000003, 7.188330000000064], [116.8787400000001, 7.274170000000083], [116.88666, 7.280280000000062], [117.00088000000005, 7.35292000000004], [117.00943000000007, 7.342220000000054], [117.01749000000007, 7.308330000000069], [117.01804000000004, 7.266740000000084], [116.9580400000001, 7.241670000000056], [116.90818000000002, 7.233470000000068], [116.87471000000005, 7.221800000000087]]]]
Thank you in advance,
Best regards.

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: 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: 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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"}]
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: 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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

print word vectors simply instead of obtaining them as array

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
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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
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-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.)

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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1.38295190e-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])