convert api response to pandas - json

Question

I'd like to convert API response into a pandas dataframe to make it easier to manipulate.
Below it's what I've tried so far:
import requests
import pandas as pd
URL = 'https://api.gleif.org/api/v1/lei-records?page[size]=10&page[number]=1&filter[entity.names]=*'
r = requests.get(URL, proxies=proxyDict)
x = r.json()
x
out:
{'meta': {'goldenCopy': {'publishDate': '2020-07-14T00:00:00Z'},
'pagination': {'currentPage': 1,
'perPage': 10,
'from': 1,
'to': 10,
'total': 1675786,
'lastPage': 167579}},
'links': {'first': 'https://api.gleif.org/api/v1/lei-records?filter%5Bentity.names%5D=%2A&page%5Bnumber%5D=1&page%5Bsize%5D=10',
'next': 'https://api.gleif.org/api/v1/lei-records?filter%5Bentity.names%5D=%2A&page%5Bnumber%5D=2&page%5Bsize%5D=10',
'last': 'https://api.gleif.org/api/v1/lei-records?filter%5Bentity.names%5D=%2A&page%5Bnumber%5D=167579&page%5Bsize%5D=10'},
'data': [{'type': 'lei-records',
'id': '254900RR9EUYHB7PI211',
'attributes': {'lei': '254900RR9EUYHB7PI211',
'entity': {'legalName': {'name': 'MedicLights Research Inc.',
'language': None},
'otherNames': [],
'transliteratedOtherNames': [],
'legalAddress': {'language': None,
'addressLines': ['300 Ranee Avenue'],
'addressNumber': None,
'addressNumberWithinBuilding': None,
'mailRouting': None,
'city': 'Toronto',
'region': 'CA-ON',
'country': 'CA',
'postalCode': 'M6A 1N8'},
'headquartersAddress': {'language': None,
'addressLines': ['76 Marble Arch Crescent'],
'addressNumber': None,
'addressNumberWithinBuilding': None,
'mailRouting': None,
'city': 'Toronto',
'region': 'CA-ON',
'country': 'CA',
'postalCode': 'M1R 1W9'},
'registeredAt': {'id': 'RA000079', 'other': None},
'registeredAs': '002185472',
'jurisdiction': 'CA-ON',
'category': None,
'legalForm': {'id': 'O90R', 'other': None},
'associatedEntity': {'lei': None, 'name': None},
'status': 'ACTIVE',
'expiration': {'date': None, 'reason': None},
'successorEntity': {'lei': None, 'name': None},
'otherAddresses': []},
'registration': {'initialRegistrationDate': '2020-07-13T21:09:50Z',
'lastUpdateDate': '2020-07-13T21:09:50Z',
'status': 'ISSUED',
'nextRenewalDate': '2021-07-13T21:09:50Z',
'managingLou': '5493001KJTIIGC8Y1R12',
'corroborationLevel': 'PARTIALLY_CORROBORATED',
'validatedAt': {'id': 'RA000079', 'other': None},
'validatedAs': '002185472'},
'bic': None},
'relationships': {'managing-lou': {'links': {'related': 'https://api.gleif.org/api/v1/lei-records/254900RR9EUYHB7PI211/managing-lou'}},
'lei-issuer': {'links': {'related': 'https://api.gleif.org/api/v1/lei-records/254900RR9EUYHB7PI211/lei-issuer'}},
'direct-parent': {'links': {'reporting-exception': 'https://api.gleif.org/api/v1/lei-records/254900RR9EUYHB7PI211/direct-parent-reporting-exception'}},
'ultimate-parent': {'links': {'reporting-exception': 'https://api.gleif.org/api/v1/lei-records/254900RR9EUYHB7PI211/ultimate-parent-reporting-exception'}}},
'links': {'self': 'https://api.gleif.org/api/v1/lei-records/254900RR9EUYHB7PI211'}},
{'type': 'lei-records',
'id': '254900F9XV2K6IR5TO93',
Then I tried to put it into pandas and gives me the following results
f = pd.DataFrame(x['data'])
f
type id attributes relationships links
0 lei-records 254900RR9EUYHB7PI211 {'lei': '254900RR9EUYHB7PI211', 'entity': {'le... {'managing-lou': {'links': {'related': 'https:... {'self': 'https://api.gleif.org/api/v1/lei-rec...
1 lei-records 254900F9XV2K6IR5TO93 {'lei': '254900F9XV2K6IR5TO93', 'entity': {'le... {'managing-lou': {'links': {'related': 'https:... {'self': 'https://api.gleif.org/api/v1/lei-rec...
2 lei-records 254900DIC0729LEXNL12 {'lei': '254900DIC0729LEXNL12', 'entity': {'le... {'managing-lou': {'links': {'related': 'https:... {'self': 'https://api.gleif.org/api/v1/lei-rec...
Which isn't the result expected. I even tried to read_json with below codes:
g = pd.read_json(x.text)
g
which gives me the error
AttributeError: 'dict' object has no attribute 'text'
the expected output should look like this:
lei entity.legalName.name entity.legalAddress.addressLines entity.legalAddress.city entity.legalAddress.postalcode status registration.status
254900RR9EUYHB7PI211 MedicLights Research Inc. 300 Ranee Avenue Toronto M6A 1N8 ACTIVE ISSUED
Thanks for anyone helping

Answer 1

Use json_normalize like:
pd.json_normalize(x['data'])

Answer 2

Here is another method to use the pandas to normalize the json file using pandas.io.json.json_normalize from pandas.io.json library.
How to normalize json correctly by Python Pandas