我对编码非常陌生，我想将嵌套的JSON文件转换为CSV。我知道可以使用pandas模块轻松实现。
我尝试使用pandas软件包并将标准化的部分输出到cvs中。
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"2019-02-07T14:00:00.000Z", "v_alc": 0.3852}, {"t": "2019-02-07T15:00:00.000Z", "v_alc": 0.3438297872}, {"t": "2019-02-07T16:00:00.000Z", "v_alc": 0.4885714286}, {"t": "2019-02-07T17:00:00.000Z", "v_alc": 0.5139583333}, {"t": "2019-02-07T18:00:00.000Z", "v_alc": 0.4481818182}, {"t": "2019-02-07T19:00:00.000Z", "v_alc": 0.3231111111}, {"t": "2019-02-07T20:00:00.000Z", "v_alc": 0.305}, {"t": "2019-02-07T21:00:00.000Z", "v_alc": 0.3018367347}, {"t": "2019-02-07T22:00:00.000Z", "v_alc": 0.3054}, {"t": "2019-02-07T23:00:00.000Z", "v_alc": 0.326}, {"t": "2019-02-08T00:00:00.000Z", "v_alc": 0.3595833333}, {"t": "2019-02-08T01:00:00.000Z", "v_alc": 0.4104255319}, {"t": "2019-02-08T02:00:00.000Z", "v_alc": 0.3588}, {"t": "2019-02-08T03:00:00.000Z", "v_alc": 0.3382}, {"t": "2019-02-08T04:00:00.000Z", "v_alc": 0.305625}, {"t": "2019-02-08T05:00:00.000Z", "v_alc": 0.34325}, {"t": "2019-02-08T06:00:00.000Z", "v_alc": 0.3891666667}, {"t": "2019-02-08T07:00:00.000Z", "v_alc": 0.3381081081}, {"t": "2019-02-08T08:00:00.000Z", "v_alc": 0.5335897436}, {"t": "2019-02-08T09:00:00.000Z", "v_alc": 0.4008163265}, {"t": "2019-02-08T10:00:00.000Z", "v_alc": 0.3123404255}, {"t": "2019-02-08T11:00:00.000Z", "v_alc": 0.3280851064}, {"t": "2019-02-08T12:00:00.000Z", "v_alc": 0.2973333333}, {"t": "2019-02-08T13:00:00.000Z", "v_alc": 0.2947916667}, {"t": "2019-02-08T14:00:00.000Z", "v_alc": 0.3066}, {"t": "2019-02-08T15:00:00.000Z", "v_alc": 0.3938636364}, {"t": "2019-02-08T16:00:00.000Z", "v_alc": 0.5452380952}, {"t": "2019-02-08T17:00:00.000Z", "v_alc": 0.3494871795}, {"t": "2019-02-08T18:00:00.000Z", "v_alc": 0.325106383}, {"t": "2019-02-08T19:00:00.000Z", "v_alc": 0.3283333333}, {"t": "2019-02-08T20:00:00.000Z", "v_alc": 0.31375}, {"t": "2019-02-08T21:00:00.000Z", "v_alc": 0.3391836735}, {"t": "2019-02-08T22:00:00.000Z", "v_alc": 0.3617391304}, {"t": "2019-02-08T23:00:00.000Z", "v_alc": 0.3286}, {"t": "2019-02-09T00:00:00.000Z", "v_alc": 0.3425}, {"t": "2019-02-09T01:00:00.000Z", "v_alc": 0.3659090909}, {"t": "2019-02-09T02:00:00.000Z", "v_alc": 0.3580769231}, {"t": "2019-02-09T03:00:00.000Z", "v_alc": 0.3397826087}, {"t": "2019-02-09T04:00:00.000Z", "v_alc": 0.3319512195}, {"t": "2019-02-09T05:00:00.000Z", "v_alc": 0.3107843137}, {"t": "2019-02-09T06:00:00.000Z", "v_alc": 0.3089361702}, {"t": "2019-02-09T07:00:00.000Z", "v_alc": 0.3552173913}, {"t": "2019-02-09T08:00:00.000Z", "v_alc": 0.5072}, {"t": "2019-02-09T09:00:00.000Z", "v_alc": 0.4972}, {"t": "2019-02-09T10:00:00.000Z", "v_alc": 0.64175}, {"t": "2019-02-09T11:00:00.000Z", "v_alc": 0.4969565217}, {"t": "2019-02-09T12:00:00.000Z", "v_alc": 0.3991666667}, {"t": "2019-02-09T13:00:00.000Z", "v_alc": 0.4159183673}, {"t": "2019-02-09T14:00:00.000Z", "v_alc": 0.4604255319}, {"t": "2019-02-09T15:00:00.000Z", "v_alc": 0.6008333333}, {"t": "2019-02-09T16:00:00.000Z", "v_alc": 0.5222727273}, {"t": "2019-02-09T17:00:00.000Z", "v_alc": 0.3736956522}, {"t": "2019-02-09T18:00:00.000Z", "v_alc": 0.3779591837}, {"t": "2019-02-09T19:00:00.000Z", "v_alc": 0.3551020408}, {"t": "2019-02-09T20:00:00.000Z", "v_alc": 0.3625}, {"t": "2019-02-09T21:00:00.000Z", "v_alc": 0.3319047619}, {"t": "2019-02-09T22:00:00.000Z", "v_alc": 0.3476470588}, {"t": "2019-02-09T23:00:00.000Z", "v_alc": 0.3747916667}, {"t": "2019-02-10T00:00:00.000Z", "v_alc": 0.3697916667}, {"t": "2019-02-10T01:00:00.000Z", "v_alc": 0.3229545455}, {"t": "2019-02-10T02:00:00.000Z", "v_alc": 0.3221276596}, {"t": "2019-02-10T03:00:00.000Z", "v_alc": 0.3158}, {"t": "2019-02-10T04:00:00.000Z", "v_alc": 0.32125}, {"t": "2019-02-10T05:00:00.000Z", "v_alc": 0.3241860465}, {"t": "2019-02-10T06:00:00.000Z", "v_alc": 0.303125}, {"t": "2019-02-10T07:00:00.000Z", "v_alc": 0.3140425532}, {"t": "2019-02-10T08:00:00.000Z", "v_alc": 0.5367346939}, {"t": "2019-02-10T09:00:00.000Z", "v_alc": 0.3625581395}, {"t": "2019-02-10T10:00:00.000Z", "v_alc": 0.4341176471}, {"t": "2019-02-10T11:00:00.000Z", "v_alc": 0.411627907}, {"t": "2019-02-10T12:00:00.000Z", "v_alc": 0.466875}, {"t": "2019-02-10T13:00:00.000Z", "v_alc": 0.4318181818}, {"t": "2019-02-10T14:00:00.000Z", "v_alc": 0.3642222222}, {"t": "2019-02-10T15:00:00.000Z", "v_alc": 0.3268085106}, {"t": "2019-02-10T16:00:00.000Z", "v_alc": 0.393877551}, {"t": "2019-02-10T17:00:00.000Z", "v_alc": 0.4158695652}, {"t": "2019-02-10T18:00:00.000Z", "v_alc": 0.5480851064}, {"t": "2019-02-10T19:00:00.000Z", "v_alc": 0.5466}, {"t": "2019-02-10T20:00:00.000Z", "v_alc": 0.4887234043}, {"t": "2019-02-10T21:00:00.000Z", "v_alc": 0.4388461538}, {"t": "2019-02-10T22:00:00.000Z", "v_alc": 0.4172}, {"t": "2019-02-10T23:00:00.000Z", "v_alc": 0.3665306122}, {"t": "2019-02-11T00:00:00.000Z", "v_alc": 0.3511111111}, {"t": "2019-02-11T01:00:00.000Z", "v_alc": 0.3243589744}, {"t": "2019-02-11T02:00:00.000Z", "v_alc": null}
这是我尝试过的。我不确定如何更深入地了解嵌套标头。
import pandas as pd
from pandas.io.json import json_normalize
import json
with open('AQ_T1_Feb19.json') as file:
data = json.load(file)
df = json_normalize(data, ['status'],['data'])
df.to_csv('AQ_T1_Feb19.csv', encoding='utf-8', index=False)
csv文件应将t和v_amm作为标题，并在列中包含相应的值。我无法上传图片，但希望您能理解我的意思。
答案 0 :(得分：1)
json_normalize：record_path keys {'data': {'metrics': {'mid1': {'did1': {'data': [{'t': <class 'str'>,
'v_amm': <class 'float'>}]},
'did2': {'data': [{'t': <class 'str'>,
'v_alc': <class 'float'>}]}}}}}
null值，必须替换为None
from pandas.io.json import json_normalize
import pandas as pd
data = {your json}
df_v_alc = json_normalize(data, ['data', 'metrics', 'mid1', 'did2', 'data'])
t v_alc
2019-02-07T08:00:00.000Z 0.344000
2019-02-07T09:00:00.000Z 0.391778
2019-02-07T10:00:00.000Z 0.325208
2019-02-07T11:00:00.000Z 0.293800
2019-02-07T12:00:00.000Z 0.309302
df_v_amm = json_normalize(data, ['data', 'metrics', 'mid1', 'did1', 'data'])
t v_amm
2019-02-07T08:00:00.000Z 0.320000
2019-02-07T09:00:00.000Z 0.322889
2019-02-07T10:00:00.000Z 0.209375
2019-02-07T11:00:00.000Z 0.167200
2019-02-07T12:00:00.000Z 0.196279
答案 1 :(得分：0)
nested-csv https://github.com/kawasin73/nested_csv
$ pip3 install nested-csv
正在转换python脚本。
import json
import nested_csv
with open("sample.json") as f:
data = json.loads(f.read())
fieldnames = nested_csv.generate_fieldnames(data)
with open("sample.csv", "w+") as f:
w = nested_csv.NestedDictWriter(f, fieldnames)
w.writeheader()
w.writerow(data)
with open("did1.csv", "w+") as f:
fieldnames = ["data.metrics.mid1.did1.data[id]", "data.metrics.mid1.did1.data[].t",
"data.metrics.mid1.did1.data[].v_amm", "data.metrics.mid1.did1.id", "status"]
w = nested_csv.NestedDictWriter(f, fieldnames)
w.writeheader()
w.writerow(data)
with open("did2.csv", "w+") as f:
fieldnames = ["data.metrics.mid1.did2.data[id]", "data.metrics.mid1.did2.data[].t",
"data.metrics.mid1.did2.data[].v_alc", "data.metrics.mid1.did2.id", "status"]
w = nested_csv.NestedDictWriter(f, fieldnames)
w.writeheader()
w.writerow(data)
您可以获取以下csv文件。
data.metrics.mid1.did1.data[id],data.metrics.mid1.did1.data[].t,data.metrics.mid1.did1.data[].v_amm,data.metrics.mid1.did1.id,data.metrics.mid1.did2.data[id],data.metrics.mid1.did2.data[].t,data.metrics.mid1.did2.data[].v_alc,data.metrics.mid1.did2.id,status
0,2019-02-07T08:00:00.000Z,0.32,did1,0,2019-02-07T08:00:00.000Z,0.344,did2,success
1,2019-02-07T09:00:00.000Z,0.3228888889,did1,1,2019-02-07T09:00:00.000Z,0.3917777778,did2,success
2,2019-02-07T10:00:00.000Z,0.209375,did1,2,2019-02-07T10:00:00.000Z,0.3252083333,did2,success
3,2019-02-07T11:00:00.000Z,0.1672,did1,3,2019-02-07T11:00:00.000Z,0.2938,did2,success
4,2019-02-07T12:00:00.000Z,0.1962790698,did1,4,2019-02-07T12:00:00.000Z,0.3093023256,did2,success
5,2019-02-07T13:00:00.000Z,0.3023529412,did1,5,2019-02-07T13:00:00.000Z,0.472745098,did2,success
6,2019-02-07T14:00:00.000Z,0.3298,did1,6,2019-02-07T14:00:00.000Z,0.3852,did2,success
data.metrics.mid1.did1.data[id],data.metrics.mid1.did1.data[].t,data.metrics.mid1.did1.data[].v_amm,data.metrics.mid1.did1.id,status
0,2019-02-07T08:00:00.000Z,0.32,did1,success
1,2019-02-07T09:00:00.000Z,0.3228888889,did1,success
2,2019-02-07T10:00:00.000Z,0.209375,did1,success
3,2019-02-07T11:00:00.000Z,0.1672,did1,success
4,2019-02-07T12:00:00.000Z,0.1962790698,did1,success
5,2019-02-07T13:00:00.000Z,0.3023529412,did1,success
6,2019-02-07T14:00:00.000Z,0.3298,did1,success
data.metrics.mid1.did2.data[id],data.metrics.mid1.did2.data[].t,data.metrics.mid1.did2.data[].v_alc,data.metrics.mid1.did2.id,status
0,2019-02-07T08:00:00.000Z,0.344,did2,success
1,2019-02-07T09:00:00.000Z,0.3917777778,did2,success
2,2019-02-07T10:00:00.000Z,0.3252083333,did2,success
3,2019-02-07T11:00:00.000Z,0.2938,did2,success
4,2019-02-07T12:00:00.000Z,0.3093023256,did2,success
5,2019-02-07T13:00:00.000Z,0.472745098,did2,success
6,2019-02-07T14:00:00.000Z,0.3852,did2,success