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Storing list of list in sql database

I wanted to store list of list consisting of longitude & latitude in the database. Any instinct, on how can I do that.
For example I have to store data such as:[[77.714062, 12.967682], [77.714082, 12.967675], [77.714158, 12.967664], [77.71422, 12.967658], [77.714323, 12.967648], [77.715087, 12.967576], [77.715514, 12.967572], [77.716038, 12.96755], [77.716238, 12.967524], [77.716321, 12.967514], [77.716301, 12.967425], [77.716203, 12.967444], [77.716054, 12.967465], [77.715734, 12.967485], [77.715441, 12.967491], [77.715077, 12.967513], [77.714525, 12.967558], [77.714095, 12.967591], [77.714008, 12.967615], [77.713854, 12.967674], [77.713676, 12.967811], [77.713543, 12.967957], [77.713464, 12.968085], [77.713431, 12.968147], [77.713353, 12.968296], [77.713019, 12.968976], [77.712813, 12.969461], [77.712696, 12.969716], [77.712578, 12.970003], [77.712469, 12.970261], [77.712348, 12.970558], [77.712265, 12.970778], [77.712171, 12.971033], [77.712062, 12.971312], [77.711966, 12.971566], [77.711862, 12.971841], [77.711673, 12.972352], [77.711256, 12.973533], [77.710798, 12.974761], [77.710674, 12.975076], [77.710094, 12.97655], [77.709591, 12.977998], [77.709571, 12.978068], [77.709549, 12.978142], [77.709031, 12.979735], [77.708906, 12.980173], [77.708657, 12.980959], [77.708422, 12.981816], [77.708281, 12.982341], [77.708203, 12.982756], [77.708163, 12.98343], [77.708155, 12.984047], [77.708166, 12.984277], [77.708173, 12.984421], [77.708129, 12.984937], [77.708099, 12.985114], [77.708074, 12.985224], [77.707273, 12.988019], [77.706995, 12.988926], [77.706744, 12.989707], [77.706739, 12.989725], [77.706696, 12.989853], [77.706423, 12.990738], [77.706396, 12.990826], [77.706333, 12.99107], [77.706064, 12.991884], [77.706017, 12.992026], [77.705785, 12.992726], [77.705758, 12.992927], [77.705727, 12.992927], [77.705677, 12.992928], [77.705644, 12.992929], [77.705536, 12.992931], [77.705052, 12.992964], [77.704915, 12.992993], [77.704834, 12.99301], [77.704443, 12.993128], [77.704112, 12.993226], [77.703465, 12.993403], [77.703391, 12.993419], [77.703423, 12.993556], [77.704126, 12.993377], [77.704873, 12.993162], [77.705104, 12.99311], [77.705253, 12.993086], [77.705708, 12.993053], [77.705748, 12.993053], [77.705782, 12.993052], [77.705832, 12.993051], [77.70591, 12.993047], [77.707049, 12.992987], [77.707795, 12.992966], [77.708993, 12.992932], [77.709264, 12.992924], [77.710051, 12.992914], [77.710562, 12.992884], [77.710827, 12.992852], [77.710956, 12.992821], [77.71114, 12.992765], [77.711552, 12.992681], [77.712168, 12.992525], [77.713555, 12.992054], [77.713646, 12.992019], [77.714037, 12.991891], [77.714192, 12.99186], [77.714306, 12.991857], [77.714368, 12.991858], [77.714406, 12.991859], [77.714731, 12.991925], [77.71502, 12.991994], [77.715271, 12.992055], [77.715769, 12.992159], [77.716042, 12.992263], [77.716182, 12.992391], [77.716387, 12.992618], [77.716638, 12.99291], [77.716657, 12.992932], [77.717007, 12.993288], [77.717035, 12.993324], [77.717229, 12.993574], [77.717269, 12.993626], [77.717485, 12.993938], [77.7176, 12.994103], [77.717673, 12.99432], [77.717745, 12.994532], [77.717796, 12.994611], [77.717868, 12.994722], [77.717917, 12.994798], [77.717956, 12.995009], [77.717977, 12.995273], [77.717979, 12.995631], [77.717988, 12.99583], [77.717924, 12.99608], [77.717421, 12.996944], [77.717344, 12.997164], [77.717319, 12.997359], [77.717309, 12.99759], [77.717324, 12.997835], [77.717382, 12.998641], [77.717333, 12.998791], [77.717288, 12.998846], [77.717213, 12.998847], [77.717044, 12.998852], [77.716964, 12.998854], [77.716617, 12.998836], [77.716457, 12.998842], [77.716309, 12.998882], [77.716133, 12.998948], [77.71559, 12.999241], [77.715226, 12.999437], [77.714697, 12.999722], [77.714621, 12.999914], [77.714556, 13.000808], [77.714535, 13.001097], [77.714483, 13.001808], [77.714387, 13.001928], [77.713399, 13.002112], [77.712552, 13.002321], [77.712406, 13.002413], [77.712412, 13.002572], [77.712514, 13.002797], [77.712601, 13.00299], [77.71266, 13.00312], [77.712681, 13.003165], [77.712678, 13.00338], [77.712663, 13.003709], [77.712678, 13.004095], [77.712669, 13.004161], [77.712672, 13.004371], [77.712678, 13.004504], [77.712146, 13.004444], [77.711826, 13.004403], [77.711527, 13.004365], [77.711499, 13.004362], [77.711296, 13.004357], [77.711202, 13.004354], [77.710916, 13.004353], [77.710584, 13.004328], [77.710548, 13.004323], [77.710489, 13.00432], [77.710314, 13.00431], [77.709984, 13.004288], [77.710007, 13.00456], [77.710032, 13.00487], [77.710073, 13.005374], [77.710059, 13.005603], [77.709962, 13.005865], [77.709596, 13.006614], [77.709433, 13.007059], [77.709369, 13.007379], [77.709372, 13.007579], [77.709373, 13.007641], [77.709382, 13.007846], [77.709431, 13.008162], [77.709456, 13.008514], [77.709569, 13.00896], [77.709799, 13.009639], [77.709821, 13.009702], [77.709927, 13.010083], [77.709955, 13.010183], [77.709996, 13.010318], [77.710077, 13.010582], [77.710054, 13.010778], [77.710043, 13.010876], [77.710031, 13.010983], [77.71, 13.011126], [77.709928, 13.011508], [77.709926, 13.011522], [77.709877, 13.011984], [77.709635, 13.013524], [77.709597, 13.013883], [77.709562, 13.014052], [77.709512, 13.014471], [77.709453, 13.014936], [77.709403, 13.015329], [77.709376, 13.015537], [77.709359, 13.015692], [77.709308, 13.015886], [77.709273, 13.016166], [77.709168, 13.016768], [77.709047, 13.017502], [77.70891, 13.018152], [77.708829, 13.018535], [77.708689, 13.019078], [77.708598, 13.019295], [77.708521, 13.019382], [77.708462, 13.019483], [77.709847, 13.020261], [77.714061, 13.022766], [77.715638, 13.0236], [77.715776, 13.023677], [77.715685, 13.023773], [77.715992, 13.023914], [77.716709, 13.024325], [77.716771, 13.024363], [77.717482, 13.024797], [77.717799, 13.024968], [77.718089, 13.025125], [77.718356, 13.025268], [77.719608, 13.025943], [77.720323, 13.026334], [77.720377, 13.026363], [77.720346, 13.026559], [77.720298, 13.026869], [77.720277, 13.026974], [77.720256, 13.027077], [77.720256, 13.027336], [77.720335, 13.027333], [77.720334, 13.027407], [77.720348, 13.027611], [77.720381, 13.027753], [77.720404, 13.027766], [77.720574, 13.027758], [77.720629, 13.02783]] as a single entry.``` Any reply will be appreciated. Thanks in advance.

There is no direct and natural way to store a list of list in the general case. But here you have not a arbitral list of list but a list (arbitrary length) of lists of 2 elements, a latitude and a longitude. Then if you create a table containing one column for each sublist element if will be trivial.
Assuming con is a connection to your SQLite database, and data your list of (2 element) lists:
curs = con.cursor()
curs.execute("CREATE TABLE coord(long REAL, lat REAL)")
curs.executemany("INSERT INTO coord(long, lat) VALUES(?,?)", data)

Assuming you are using sqllite package in Python. just save the string to a variable and insert it like this:
my_list = [[77.717044, 12.998852], [77.716964, 12.998854]...] # not correct but you get the point
con.execute("insert into MY_TABLE (LIST_COLUMN) values (?)",
(my_list))
where con is your connection object.

One solution is to create a table with lat and long columns. Then insert each sublist as a row in the database.

I see two ways to achieve this (I prefer way 1):
Convert the list to a numpy array and store as a BLOB (data object).
con.execute("INSERT INTO MY_TABLE (List_Object) values (?)", (np.array(my_list).to_bytes()))
and load it back
np.frombuffer(np.array(a).tobytes(), dtype=np.float64).reshape(-1, 2).tolist()
Convert to string and store as text. (convert with str() or json)

Issues Querying and Downloading Sentinel-3 OLCI Data with Sentinelsat

I am working with Sentinel-3 OLCI Level-2 Data Products with the Sentinelsat API and am having issues querying and exceeding my data download quota. Overall, I would like to write a program that accepts a date range and a specific geographic location, then downloads a dataframe of all values in the "Oa04_radiance"-band within the specified dates for that location. This is what I have so far:
from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt
from datetime import date
from geojson import Feature, Point, Polygon
api = SentinelAPI('user', 'password', 'https://apihub.copernicus.eu/apihub')
lon = -123.312383
lat = 49.319269
my_point = Point((lon, lat))
footprint = geojson_to_wkt(my_point)
products = api.query(footprint,
date=(date(2021, 1, 1), date(2021, 6, 15)),
platformname='Sentinel-3',
producttype='OL_2_LRR___',
cloudcoverpercentage=(0, 80))
products_df = api.to_dataframe(products)
api.download_all(products_df.index)
Error Output:
Traceback (most recent call last):
File "C:/Users/t7dej/Desktop/Turbid Time Local/SentSat/SenSat_mdl.py", line 48, in <module>
api.download_all(products_df_sorted.index)
File "E:\Software\Anaconda\lib\site-packages\sentinelsat\sentinel.py", line 723, in download_all
is_online = not self.trigger_offline_retrieval(pid)
File "E:\Software\Anaconda\lib\site-packages\sentinelsat\sentinel.py", line 636, in trigger_offline_retrieval
raise LTAError(msg, r)
sentinelsat.exceptions.LTAError: HTTP status 403 Forbidden: User quota exceeded: MediaRegulationException : An exception occured while creating a stream: Maximum number of 4 concurrent flows achieved by the user
Even when I set my api.query(limit=1) I am receiving this error message. The products_df is 173 MB and has a geometry column with a value of :
MULTIPOLYGON (((-146.081 -49.2196, -145.768 -48.2668, -145.201 -46.4727, -144.658 -44.6765, -144.135 -42.8782, -143.63 -41.0787, -143.142 -39.2767, -142.667 -37.4733, -142.204 -35.6694, -141.753 -33.863, -141.312 -32.0559, -140.878 -30.2474, -140.453 -28.4377, -140.033 -26.6268, -139.62 -24.8151, -139.211 -23.0024, -138.806 -21.1887, -138.404 -19.3744, -138.006 -17.5593, -137.609 -15.7434, -137.213 -13.927, -136.819 -12.1101, -136.425 -10.2928, -136.031 -8.47512, -135.636 -6.65724, -135.24 -4.83889, -134.843 -3.02078, -134.443 -1.2025, -134.04 0.61575, -133.634 2.43352, -133.224 4.25148, -132.81 6.06894, -132.391 7.88578, -131.965 9.702120000000001, -131.534 11.5179, -131.095 13.3329, -130.649 15.1468, -130.194 16.9598, -129.729 18.7716, -129.253 20.582, -128.767 22.3916, -128.267 24.1992, -127.753 26.0043, -127.224 27.8086, -126.678 29.6103, -126.113 31.4098, -125.527 33.2067, -124.919 35.0007, -124.286 36.7919, -123.624 38.5795, -122.932 40.3637, -122.205 42.1436, -121.44 43.9188, -120.631 45.6892, -119.773 47.4541, -118.861 49.2124, -117.886 50.9639, -116.841 52.7073, -115.714 54.4414, -114.495 56.1652, -113.168 57.8769, -111.717 59.5747, -110.12 61.256, -108.352 62.9179, -106.382 64.5568, -104.173 66.16800000000001, -101.677 67.7456, -98.8378 69.2821, -95.5855 70.7672, -91.83369999999999 72.1889, -87.4884 73.5312, -82.44029999999999 74.7715, -76.5813 75.8849, -69.83199999999999 76.83880000000001, -62.1773 77.59399999999999, -53.7161 78.1165, -44.6976 78.3725, -35.5051 78.3451, -26.5677 78.03619999999999, -18.2457 77.46639999999999, -10.7612 76.6694, -4.18714 75.6829, 3.64587789990069e-15 74.8448206506109, 1.50843 74.5429, 6.41354 73.28100000000001, 10.6367 71.9226, 14.2845 70.4875, 17.4519 68.99160000000001, 20.2206 67.4469, 21.6077 67.77589999999999, 23.0264 68.09050000000001, 24.4837 68.3925, 25.9603 68.679, 27.4944 68.9546, 29.0662 69.2161, 30.6951 69.46680000000001, 32.3417 69.6985, 34.024 69.9145, 35.7316 70.10760000000001, 37.4774 70.2903, 39.2531 70.4558, 41.0752 70.6046, 42.9054 70.7343, 44.7579 70.8455, 46.6297 70.938, 48.5174 71.01139999999999, 50.4176 71.0655, 52.3019 71.10290000000001, 52.0832 72.8877, 51.8771 74.6721, 51.6876 76.4562, 51.5212 78.2398, 51.3914 80.0231, 51.3212 81.80629999999999, 51.3582 83.58880000000001, 51.57959163346614 85.05115000000001, 3.911836325497215e-15 85.05115000000001, -133.5599156744917 85.05115000000001, -133.35 83.92870000000001, -133.276 82.14530000000001, -133.33 80.3614, -133.453 78.57859999999999, -133.611 76.7949, -133.799 75.0104, -134.002 73.226, -134.218 71.4409, -134.445 69.6553, -134.678 67.8693, -134.918 66.0826, -135.163 64.2954, -135.413 62.5074, -135.666 60.719, -135.923 58.93, -136.183 57.1401, -136.447 55.3495, -136.712 53.5582, -136.982 51.7664, -137.253 49.9738, -137.528 48.1799, -137.805 46.3858, -138.085 44.5911, -138.368 42.7954, -138.653 40.9991, -138.942 39.2019, -139.234 37.4039, -139.528 35.6058, -139.826 33.8069, -140.128 32.0072, -140.433 30.207, -140.741 28.4055, -141.054 26.6057, -141.37 24.8042, -141.691 23.0012, -142.016 21.1992, -142.346 19.3968, -142.68 17.5942, -143.02 15.7915, -143.365 13.9887, -143.716 12.1859, -144.073 10.3832, -144.436 8.58104, -144.806 6.77937, -145.183 4.97715, -145.567 3.17639, -145.96 1.37623, -146.361 -0.423407, -146.77 -2.22189, -147.19 -4.01968, -147.62 -5.81594, -148.06 -7.61083, -148.512 -9.40438, -148.976 -11.1962, -149.454 -12.9862, -149.946 -14.7742, -150.453 -16.5599, -150.975 -18.343, -151.516 -20.1234, -152.076 -21.901, -152.655 -23.6751, -153.257 -25.4461, -153.883 -27.2129, -154.534 -28.9754, -155.214 -30.7335, -155.923 -32.487, -156.667 -34.2331, -157.447 -35.9749, -158.267 -37.7106, -159.131 -39.4374, -160.043 -41.1566, -161.008 -42.867, -162.033 -44.5672, -162.604 -45.4664, -161.805 -45.7119, -160.989 -45.9615, -160.165 -46.2051, -159.335 -46.4427, -158.497 -46.6742, -157.652 -46.8994, -156.79 -47.1211, -155.932 -47.3333, -155.068 -47.539, -154.207 -47.742, -153.329 -47.9343, -152.444 -48.1198, -151.538 -48.3011, -150.642 -48.4725, -149.738 -48.6368, -148.842 -48.7908, -147.928 -48.9408, -147.008 -49.0835, -146.081 -49.2196)))
I have specified a geojson point object in the products query and am wondering why it is returning such a large multipolygon object in the products_df. I am thinking this is why products_df is so large and my quota is exceeded. Does anyone have any recommendations for this? Also, is it possible to query only the specific band 'Oa04_radiance' before downloading since I do not need any of the other bands from the Sentinel-3 OLCI Level-2 data products.

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

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
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-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
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-6.12767649e+00 -6.96556044e+00 -4.83318233e+00 -7.69717407e+00
-6.56721640e+00]
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-8.38169765e+00 -1.54907408e+01 -1.07314129e+01 -8.34741211e+00
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9.12959814e-01 -1.42015231e+00 -1.60620236e+00 -1.05381451e+01
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-1.43742008e+01 -1.75251045e+01 -1.33377829e+01 -1.88240013e+01
-1.68392487e+01]
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-1.08697920e+01 -2.12837963e+01 -1.57861137e+01 -1.22496452e+01
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.
.
.
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-1.06069736e+01 -1.13605204e+01 -1.08889675e+01 -9.81694508e+00
-1.06967080e+00 -1.58631933e+00 -1.36870170e+00 -1.17178059e+00
-7.71994257e+00 -9.72991562e+00 -9.93029594e+00 -1.08334541e+01
-9.51464462e+00 -9.31674480e+00 -1.08087626e+01 -9.48193550e+00
-9.81091881e+00 -8.34595966e+00 -1.10064735e+01 -9.91674614e+00
-1.18921404e+01 -1.08112535e+01 -9.73410416e+00 -7.14041519e+00
-8.62492752e+00 -1.10488844e+01 -1.03341990e+01 -1.04487247e+01
-1.07167931e+01]]]
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)