我有以下字符串格式的JSON数据。如何使用R或Python将data转换为表格格式？
我尝试过df = pd.DataFrame(data)，但这没用，因为data是一个字符串。
data = '{"Id":"048f7de7-81a4-464d-bd6d-df3be3b1e7e8","RecordType":20, "CreationTime":"2019-10-08T12:12:32","Operation":"SetScheduledRefresh", "OrganizationId":"39b03722-b836-496a-85ec-850f0957ca6b","UserType":0, "UserAgent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36", "ItemName":"ASO Daily Statistics","Schedules":{"RefreshFrequency":"Daily", "TimeZone":"E. South America Standard Time","Days":["All"], "Time":["07:30:00","10:30:00","13:30:00","16:30:00","19:30:00","22:30:00"]}, "IsSuccess":true,"ActivityId":"4e8b4514-24be-4ba5-a7d3-a69e8cb8229e"}'
所需的输出：
output =
------------------------------------------------------------------
ID | RecordType | CreationTime
048f7de7-81a4-464d-bd6d-df3be3b1e7e8 | 20 | 2019-10-08T12:12:32
错误：
ValueError Traceback (most recent call last)
<ipython-input-26-039b238b38ef> in <module>
----> 1 df = pd.DataFrame(data)
e:\Anaconda3\lib\site-packages\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
483 )
484 else:
--> 485 raise ValueError("DataFrame constructor not properly called!")
486
487 NDFrame.__init__(self, mgr, fastpath=True)
ValueError: DataFrame constructor not properly called!
答案 0 :(得分：0)
您将需要reticulate库：您将需要将所有true更改为True。看下面的代码
a <- 'string = {"Id":"048f7de7-81a4-464d-bd6d-df3be3b1e7e8","RecordType":20,
"CreationTime":"2019-10-08T12:12:32","Operation":"SetScheduledRefresh",
"OrganizationId":"39b03722-b836-496a-85ec-850f0957ca6b","UserType":0,
"UserAgent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36",
"ItemName":"ASO Daily Statistics","Schedules":{"RefreshFrequency":"Daily",
"TimeZone":"E. South America Standard Time","Days":["All"],
"Time":["07:30:00","10:30:00","13:30:00","16:30:00","19:30:00","22:30:00"]},
"IsSuccess":true,"ActivityId":"4e8b4514-24be-4ba5-a7d3-a69e8cb8229e"}'
data.frame(reticulate::py_eval(gsub('true','True',sub('.*=\\s+','',a))))
答案 1 :(得分：0)
data
str.replace true与True ast.literal_eval将data从str转换为dict pandas.io.json.json_normalize将json转换为熊猫数据框import pandas as pd
from ast import literal_eval
from pandas.io.json import json_normalize
data = '{"Id":"048f7de7-81a4-464d-bd6d-df3be3b1e7e8","RecordType":20, "CreationTime":"2019-10-08T12:12:32","Operation":"SetScheduledRefresh", "OrganizationId":"39b03722-b836-496a-85ec-850f0957ca6b","UserType":0, "UserAgent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36", "ItemName":"ASO Daily Statistics","Schedules":{"RefreshFrequency":"Daily", "TimeZone":"E. South America Standard Time","Days":["All"], "Time":["07:30:00","10:30:00","13:30:00","16:30:00","19:30:00","22:30:00"]}, "IsSuccess":true,"ActivityId":"4e8b4514-24be-4ba5-a7d3-a69e8cb8229e"}'
data = data.replace('true', 'True')
data = literal_eval(data)
{'ActivityId': '4e8b4514-24be-4ba5-a7d3-a69e8cb8229e',
'CreationTime': '2019-10-08T12:12:32',
'Id': '048f7de7-81a4-464d-bd6d-df3be3b1e7e8',
'IsSuccess': True,
'ItemName': 'ASO Daily Statistics',
'Operation': 'SetScheduledRefresh',
'OrganizationId': '39b03722-b836-496a-85ec-850f0957ca6b',
'RecordType': 20,
'Schedules': {'Days': ['All'],
'RefreshFrequency': 'Daily',
'Time': ['07:30:00',
'10:30:00',
'13:30:00',
'16:30:00',
'19:30:00',
'22:30:00'],
'TimeZone': 'E. South America Standard Time'},
'UserAgent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 '
'(KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36',
'UserType': 0}
df = json_normalize(data)
Id RecordType CreationTime Operation OrganizationId UserType UserAgent ItemName IsSuccess ActivityId Schedules.RefreshFrequency Schedules.TimeZone Schedules.Days Schedules.Time
048f7de7-81a4-464d-bd6d-df3be3b1e7e8 20 2019-10-08T12:12:32 SetScheduledRefresh 39b03722-b836-496a-85ec-850f0957ca6b 0 Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36 ASO Daily Statistics True 4e8b4514-24be-4ba5-a7d3-a69e8cb8229e Daily E. South America Standard Time [All] [07:30:00, 10:30:00, 13:30:00, 16:30:00, 19:30:00, 22:30:00]