所以这可能是一个重复的问题，但我会试一试，因为我没有找到任何东西。
我正在尝试用熊猫展平 json，正常工作。查看此处的文档示例是我正在尝试做的最接近的示例：
data = [{'state': 'Florida',
'shortname': 'FL',
'info': {'governor': 'Rick Scott'},
'counties': [{'name': 'Dade', 'population': 12345},
{'name': 'Broward', 'population': 40000},
{'name': 'Palm Beach', 'population': 60000}]},
{'state': 'Ohio',
'shortname': 'OH',
'info': {'governor': 'John Kasich'},
'counties': [{'name': 'Summit', 'population': 1234},
{'name': 'Cuyahoga', 'population': 1337}]}]
result = pd.json_normalize(data, 'counties', ['state', 'shortname',
['info', 'governor']])
result
name population state shortname info.governor
0 Dade 12345 Florida FL Rick Scott
1 Broward 40000 Florida FL Rick Scott
2 Palm Beach 60000 Florida FL Rick Scott
3 Summit 1234 Ohio OH John Kasich
4 Cuyahoga 1337 Ohio OH John Kasich
但是，此示例向我们展示了一种将内部数据counties与列状态和短名称一起展平的方法。假设我n在root每个 json 对象中都有列数（上面示例中的列n数state或shortname列数）。我如何将它们全部包括在内，以便使县变平但保留相邻的所有其他东西？
首先我尝试了这样的事情：
#None to treat data as a list of records
#Result of counties is still nested, not working
result = pd.json_normalize(data, None, ['counties'])
要么
result = pd.json_normalize(data, None, ['counties', 'name'])
然后我想到了获取列dataframe.columns并重用它，因为 meta 参数json_normalize可以采用字符串数组。
但我被困住了。并且columns似乎返回了我不想返回的嵌套 json 属性。
#still nested
cols = pd.json_normalize(data).columns.to_list()
#Exclude it because we already have it
cols = [index for index in cols if index != 'counties']
#remove nested columns if any
cols = [index for index in cols if "." not in index]
result = pd.json_normalize(data, 'counties', cols, errors="ignore")
#still nested
name population state shortname ... other6 other7 counties info.governor
0 Dade 12345 Florida FL ... dumb_data dumb_data [{'name': 'Dade', 'population': 12345}, {'name... NaN
1 Broward 40000 Florida FL ... dumb_data dumb_data [{'name': 'Dade', 'population': 12345}, {'name... NaN
2 Palm Beach 60000 Florida FL ... dumb_data dumb_data [{'name': 'Dade', 'population': 12345}, {'name... NaN
3 Summit 1234 Ohio OH ... dumb_data dumb_data [{'name': 'Summit', 'population': 1234}, {'nam... NaN
4 Cuyahoga 1337 Ohio OH ... dumb_data dumb_data [{'name': 'Summit', 'population': 1234}, {'nam... NaN
我不希望只对列名进行硬编码，因为它们会发生变化，而且在这种情况下，我有 64 个......
为了更好地理解，这是我正在从Woo Rest API处理的真实数据。我没有在这里使用它，因为它真的很长，但基本上我试图在line_items其中仅保留 product_id ，当然还有与line_items.
好吧，伙计们，如果您想展平 json 并保留其他所有内容，您应该使用pd.Dataframe.explode()
这是我的逻辑：
import pandas as pd
data = [
{'state': 'Florida',
'shortname': 'FL',
'info': {'governor': 'Rick Scott'},
'counties': [
{'name': 'Dade', 'population': 12345},
{'name': 'Broward', 'population': 40000},
{'name': 'Palm Beach', 'population': 60000}
]
},
{'state': 'Ohio',
'shortname': 'OH',
'info': {'governor': 'John Kasich'},
'counties': [{'name': 'Summit', 'population': 1234},
{'name': 'Cuyahoga', 'population': 1337}]}
]
#No Formating only converting to a Df
result = pd.json_normalize(data)
#Exploding the wanted nested column
exploded = result.explode('counties')
#Keeping the name only - this can be custom
exploded['countie_name'] = exploded['counties'].apply(lambda x: x['name'])
#Drop the used column since we took what interested us inside it.
exploded = exploded.drop(['counties'], axis=1)
print(exploded)
#Duplicate for Florida, as wanted with diferent countie names
state shortname info.governor countie_name
0 Florida FL Rick Scott Dade
0 Florida FL Rick Scott Broward
0 Florida FL Rick Scott Palm Beach
1 Ohio OH John Kasich Summit
1 Ohio OH John Kasich Cuyahoga
想象一下，你有一个篮子产品的内容作为一个嵌套的 json，对explode篮子的内容同时保持一般的篮子属性，你可以这样做。
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