我正在嘗試從我們的 Hubspot CRM 數(shù)據(jù)庫中獲取數(shù)據(jù)并使用 pandas 將其轉(zhuǎn)換為數(shù)據(jù)框。我仍然是 python 的初學(xué)者，但我無法讓 json_normalize 工作。數(shù)據(jù)庫的輸出是這樣的 JSON 格式：{'archived': False, 'archived_at': None, 'associations': None, 'created_at': datetime.datetime(2019, 12, 21, 17, 56, 24, 739000, tzinfo=tzutc()), 'id': 'xxx', 'properties': {'createdate': '2019-12-21T17:56:24.739Z', 'email': 'xxxxx@xxxxx.com', 'firstname': 'John', 'hs_object_id': 'xxx', 'lastmodifieddate': '2020-04-22T04:37:40.274Z', 'lastname': 'Hansen'}, 'updated_at': datetime.datetime(2020, 4, 22, 4, 37, 40, 274000, tzinfo=tzutc())}, {'archived': False, 'archived_at': None, 'associations': None, 'created_at': datetime.datetime(2019, 12, 21, 17, 52, 38, 485000, tzinfo=tzutc()), 'id': 'bbb', 'properties': {'createdate': '2019-12-21T17:52:38.485Z', 'email': 'bbb@bbb.dk', 'firstname': 'John2', 'hs_object_id': 'bbb', 'lastmodifieddate': '2020-05-19T07:18:28.384Z', 'lastname': 'Hansen2'}, 'updated_at': datetime.datetime(2020, 5, 19, 7, 18, 28, 384000, tzinfo=tzutc())}, {'archived': False, 'archived_at': None, 'associations': None,等嘗試使用此代碼將其放入數(shù)據(jù)框中：import hubspotimport pandas as pdimport jsonfrom pandas.io.json import json_normalizeimport osclient = hubspot.Client.create(api_key='################')all_contacts = contacts_client = client.crm.contacts.get_all()df=pd.io.json.json_normalize(all_contacts,'properties')df.headdf.to_csv ('All contacts.csv')但是我不斷收到無法解決的錯誤。我也試過pd.dataframe(all_contacts)和pf.dataframe.from_dict(all_contacts)
1 回答
寶慕林4294392
TA貢獻2021條經(jīng)驗 獲得超8個贊
all_contacts 變量是一個類似字典的元素列表。因此，為了創(chuàng)建數(shù)據(jù)框，我使用列表理解來創(chuàng)建一個元組，該元組僅包含每個類似字典的元素的“屬性”。
import datetime
import pandas as pd
from dateutil.tz import tzutc
data = ({'archived': False,
'archived_at': None,
'associations': None,
'created_at': datetime.datetime(2019, 12, 21, 17, 56, 24, 739000, tzinfo=tzutc()),
'id': 'xxx',
'properties': {'createdate': '2019-12-21T17:56:24.739Z',
'email': 'xxxxx@xxxxx.com',
'firstname': 'John',
'hs_object_id': 'xxx',
'lastmodifieddate': '2020-04-22T04:37:40.274Z',
'lastname': 'Hansen'},
'updated_at': datetime.datetime(2020, 4, 22, 4, 37, 40, 274000, tzinfo=tzutc())},
{'archived': False,
'archived_at': None,
'associations': None,
'created_at': datetime.datetime(2019, 12, 21, 17, 52, 38, 485000, tzinfo=tzutc()),
'id': 'bbb',
'properties': {
'createdate': '2019-12-21T17:52:38.485Z',
'email': 'bbb@bbb.dk',
'firstname': 'John2',
'hs_object_id': 'bbb',
'lastmodifieddate': '2020-05-19T07:18:28.384Z',
'lastname': 'Hansen2'},
'updated_at': datetime.datetime(2020, 5, 19, 7, 18, 28, 384000, tzinfo=tzutc())})
df = pd.DataFrame([row['properties'] for row in data])
print(df)
輸出：
createdate email ... lastmodifieddate lastname
0 2019-12-21T17:56:24.739Z xxxxx@xxxxx.com ... 2020-04-22T04:37:40.274Z Hansen
1 2019-12-21T17:52:38.485Z bbb@bbb.dk ... 2020-05-19T07:18:28.384Z Hansen2
[2 rows x 6 columns]
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