我用來pd.json_normalize將"sections"這些數(shù)據(jù)中的字段展平為行。除了空列表的行之外，它工作正常"sections"。該 ID 被完全忽略，并且從最終的扁平化數(shù)據(jù)框中丟失。我需要確保數(shù)據(jù)中的每個(gè)唯一 ID 至少有一行（某些 ID 可能有很多行，每個(gè)唯一 ID、每個(gè)唯一 、 、以及當(dāng)我在數(shù)據(jù)中取消嵌套更多字段時(shí)最多可以有section_id一行question_id）answer_id： {'_id': '5f48f708fe22ca4d15fb3b55', 'created_at': '2020-08-28T12:22:32Z', 'sections': []}]樣本數(shù)據(jù)：sample = [{'_id': '5f48bee4c54cf6b5e8048274', 'created_at': '2020-08-28T08:23:00Z', 'sections': [{'comment': '', 'type_fail': None, 'answers': [{'comment': 'stuff', 'feedback': [], 'value': 10.0, 'answer_type': 'default', 'question_id': '5e59599c68369c24069630fd', 'answer_id': '5e595a7c3fbb70448b6ff935'}, {'comment': 'stuff', 'feedback': [], 'value': 10.0, 'answer_type': 'default', 'question_id': '5e598939cedcaf5b865ef99a', 'answer_id': '5e598939cedcaf5b865ef998'}], 'score': 20.0, 'passed': True, '_id': '5e59599c68369c24069630fe', 'custom_fields': []}, {'comment': '', 'type_fail': None, 'answers': [{'comment': '', 'feedback': [], 'value': None, 'answer_type': 'not_applicable', 'question_id': '5e59894f68369c2398eb68a8', 'answer_id': '5eaad4e5b513aed9a3c996a5'},測(cè)試：df = pd.json_normalize(sample)df2 = pd.json_normalize(df.to_dict(orient="records"), meta=["_id", "created_at"], record_path="sections", record_prefix="section_")此時(shí)，我現(xiàn)在缺少一行 ID“5f48f708fe22ca4d15fb3b55”，我仍然需要它。df3 = pd.json_normalize(df2.to_dict(orient="records"), meta=["_id", "created_at", "section__id", "section_score", "section_passed", "section_type_fail", "section_comment"], record_path="section_answers", record_prefix="")我可以以某種方式更改此設(shè)置以確保每個(gè) ID 至少獲得一行嗎？我正在處理數(shù)百萬條記錄，并且不想稍后意識(shí)到我的最終數(shù)據(jù)中缺少一些 ID。我能想到的唯一解決方案是標(biāo)準(zhǔn)化每個(gè)數(shù)據(jù)幀，然后再次將其連接到原始數(shù)據(jù)幀。
2 回答
吃雞游戲
TA貢獻(xiàn)1829條經(jīng)驗(yàn) 獲得超7個(gè)贊
解決問題的最好方法是修復(fù)dict
如果sections為空list，則填充[{'answers': [{}]}]
for i, d in enumerate(sample):
if not d['sections']:
sample[i]['sections'] = [{'answers': [{}]}]
df = pd.json_normalize(sample)
df2 = pd.json_normalize(df.to_dict(orient="records"), meta=["_id", "created_at"], record_path="sections", record_prefix="section_")
# display(df2)
section_comment section_type_fail section_answers section_score section_passed section__id section_custom_fields _id created_at
0 NaN [{'comment': 'stuff', 'feedback': [], 'value': 10.0, 'answer_type': 'default', 'question_id': '5e59599c68369c24069630fd', 'answer_id': '5e595a7c3fbb70448b6ff935'}, {'comment': 'stuff', 'feedback': [], 'value': 10.0, 'answer_type': 'default', 'question_id': '5e598939cedcaf5b865ef99a', 'answer_id': '5e598939cedcaf5b865ef998'}] 20.0 True 5e59599c68369c24069630fe [] 5f48bee4c54cf6b5e8048274 2020-08-28T08:23:00Z
1 NaN [{'comment': '', 'feedback': [], 'value': None, 'answer_type': 'not_applicable', 'question_id': '5e59894f68369c2398eb68a8', 'answer_id': '5eaad4e5b513aed9a3c996a5'}, {'comment': '', 'feedback': [], 'value': None, 'answer_type': 'not_applicable', 'question_id': '5e598967cedcaf5b865efe3e', 'answer_id': '5eaad4ece3f1e0794372f8b2'}, {'comment': 'stuff', 'feedback': [], 'value': 0.0, 'answer_type': 'default', 'question_id': '5e598976cedcaf5b865effd1', 'answer_id': '5e598976cedcaf5b865effd3'}] 0.0 True 5e59894f68369c2398eb68a9 [] 5f48bee4c54cf6b5e8048274 2020-08-28T08:23:00Z
2 NaN NaN [{}] NaN NaN NaN NaN 5f48f708fe22ca4d15fb3b55 2020-08-28T12:22:32Z
df3 = pd.json_normalize(df2.to_dict(orient="records"), meta=["_id", "created_at", "section__id", "section_score", "section_passed", "section_type_fail", "section_comment"], record_path="section_answers", record_prefix="")
# display(df3)
comment feedback value answer_type question_id answer_id _id created_at section__id section_score section_passed section_type_fail section_comment
0 stuff [] 10.0 default 5e59599c68369c24069630fd 5e595a7c3fbb70448b6ff935 5f48bee4c54cf6b5e8048274 2020-08-28T08:23:00Z 5e59599c68369c24069630fe 20 True NaN
1 stuff [] 10.0 default 5e598939cedcaf5b865ef99a 5e598939cedcaf5b865ef998 5f48bee4c54cf6b5e8048274 2020-08-28T08:23:00Z 5e59599c68369c24069630fe 20 True NaN
2 [] NaN not_applicable 5e59894f68369c2398eb68a8 5eaad4e5b513aed9a3c996a5 5f48bee4c54cf6b5e8048274 2020-08-28T08:23:00Z 5e59894f68369c2398eb68a9 0 True NaN
3 [] NaN not_applicable 5e598967cedcaf5b865efe3e 5eaad4ece3f1e0794372f8b2 5f48bee4c54cf6b5e8048274 2020-08-28T08:23:00Z 5e59894f68369c2398eb68a9 0 True NaN
4 stuff [] 0.0 default 5e598976cedcaf5b865effd1 5e598976cedcaf5b865effd3 5f48bee4c54cf6b5e8048274 2020-08-28T08:23:00Z 5e59894f68369c2398eb68a9 0 True NaN
5 NaN NaN NaN NaN NaN NaN 5f48f708fe22ca4d15fb3b55 2020-08-28T12:22:32Z NaN NaN NaN NaN NaN
繁花不似錦
TA貢獻(xiàn)1851條經(jīng)驗(yàn) 獲得超4個(gè)贊
這是 的一個(gè)已知問題json_normalize。我還沒有找到使用json_normalize.?您可以嘗試使用flatten_json，如下所示：
import flatten_json as fj
dic = (fj.flatten(d) for d in sample)
df = pd.DataFrame(dic)
print(df)
? ? ? ? ? ? ? ? ? ? ? ? _id? ? ? ? ? ? created_at sections_0_comment? ...? ? ? ? ? ? sections_1__id sections_1_custom_fields sections
0? 5f48bee4c54cf6b5e8048274? 2020-08-28T08:23:00Z? ? ? ? ? ? ? ? ? ? ?...? 5e59894f68369c2398eb68a9? ? ? ? ? ? ? ? ? ? ? ?[]? ? ? NaN
1? 5f48f708fe22ca4d15fb3b55? 2020-08-28T12:22:32Z? ? ? ? ? ? ? ? NaN? ...? ? ? ? ? ? ? ? ? ? ? ?NaN? ? ? ? ? ? ? ? ? ? ? NaN? ? ? ?[]
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