我正在努力將 JSON 響應(yīng)轉(zhuǎn)換為我想用于各種其他操作的熊貓數(shù)據(jù)框。我已經(jīng)嘗試過此處列出的方法。但問題是我無法json_normalize有效使用，因?yàn)槿绻覍⑺璧逆I作為record_path參數(shù)傳遞，則會(huì)拋出錯(cuò)誤，因?yàn)橹挥幸恍┳侄斡羞@個(gè)鍵，而不是全部。我不想遍歷整個(gè) JSON 并逐個(gè)比較鍵并重新創(chuàng)建我自己的字典對(duì)象。我想獲取帶有uuidand nice_to_have_skills, nice_to_have_skills_path,nice_to_have_experience作為列的數(shù)據(jù)框，在這些列中，可以在 json 對(duì)象中的和鍵nice_to_have下找到這些屬性。nice_to_haveoperands我想"nice_to_have_skill" -> ["user research", "Wireframing / Prototyping"]在我的數(shù)據(jù)框中像這樣nice_to_have_skill提取列名和["user research", "Wireframing / Prototyping"]該列中的值。編輯：如果 JSON 具有任意深度，如何處理它？例如{“nice_to_have”：[{“運(yùn)算符”：“AND”，“操作數(shù)”：[{“運(yùn)算符”：“OR”，“操作數(shù)”：[{“類別”：“語言”，“值”：[{“ value": "Korean", "clusters": []}]}]}]}], "company_name": "Framework", "company_role": ["Manufacturing", "Supply Chain/Procurement"]}是一部分的 JSON 并且可以有任何級(jí)別的嵌套。
1 回答
慕沐林林
TA貢獻(xiàn)2016條經(jīng)驗(yàn) 獲得超9個(gè)贊
傳遞d['hits']給json_normalize結(jié)果：
d = json.loads(json_text)
In [136]: %time pd.json_normalize(d['hits'])
CPU times: user 2.1 ms, sys: 41 μs, total: 2.14 ms
Wall time: 2.12 ms
Out[136]:
uuid text_about objectID search_space is_searchspace nice_to_have must_have some key some_key
0 00000000-0000-0000-0000-000000000000 some_text 00000000-0000-0000-0000-000000000000-text_about NaN NaN NaN NaN NaN NaN
1 00000000-0000-0000-0000-000000000000 NaN 00000000-0000-0000-0000-000000000000-search_space some json object True NaN NaN NaN NaN
2 00000000-0000-0000-0000-000000000000 NaN 00000000-0000-0000-0000-000000000000-nice_to_have NaN NaN [{'operator': 'AND', 'operands': [{'category':... NaN NaN NaN
3 00000000-0000-0000-0000-000000000000 NaN 00000000-0000-0000-0000-000000000000-must_have NaN NaN NaN [{'operator': 'AND', 'operands': [{'category':... NaN NaN
4 NaN NaN NaN NaN NaN NaN NaN some json object NaN
5 10000000-0000-0000-0000-000000000001 some text 10000000-0000-0000-0000-000000000001-text_about NaN NaN NaN NaN NaN NaN
6 10000000-0000-0000-0000-000000000001 NaN 10000000-0000-0000-0000-000000000001-search_space some json object True NaN NaN NaN NaN
7 10000000-0000-0000-0000-000000000001 NaN 10000000-0000-0000-0000-000000000001-nice_to_have NaN NaN [{'operator': 'AND', 'operands': [{'category':... NaN NaN NaN
8 10000000-0000-0000-0000-000000000001 NaN 10000000-0000-0000-0000-000000000001-must_have NaN NaN NaN [{'operator': 'AND', 'operands': [{'category':... NaN NaN
9 NaN NaN NaN NaN NaN NaN NaN NaN some json object
在那里你可以選擇nice_to_have：
df = pd.json_normalize(d, record_path=['hits'])
In [263]: %time df['nice_to_have'].dropna().sum()
CPU times: user 705 μs, sys: 11 μs, total: 716 μs
Wall time: 713 μs
Out[263]:
[{'operator': 'AND',
'operands': [{'category': 'Skill',
'values': [{'value': 'MySQL ', 'clusters': []}]}]},
{'operator': 'AND',
'operands': [{'category': 'Skill',
'values': [{'value': 'Frontend Programming Language ',
'clusters': [{'key': 'Programming Language~>Frontend Programming Language',
'name': 'Frontend Programming Language',
'path': ['Programming Language', 'Frontend Programming Language'],
'uuid': 'e8c5cc6c-d92b-4098-8965-41e6818fe337',
'category': 'skill',
'pretty_lineage': ['Programming Language']}]}]}]}]
希望這有用。
編輯：
回應(yīng)您的評(píng)論：此 json 的主要問題是級(jí)別不一致，因此無法執(zhí)行規(guī)范化并引發(fā) KeyError。
獲得以下解決方法nice_to_have：
f = list(filter(lambda x: 'nice_to_have' in x, d['hits']))
>> pd.json_normalize(f, ['nice_to_have', 'operands', 'values', 'clusters'])
key name path uuid category pretty_lineage
0 Programming Language~>Frontend Programming Lan... Frontend Programming Language [Programming Language, Frontend Programming La... e8c5cc6c-d92b-4098-8965-41e6818fe337 skill [Programming Language]
從那里你可以得到你想要得到的值?？梢詰?yīng)用類似的解決方法來獲取must_have.
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