Question

我在Python中有一个DataFrame，如下所示。这是一个列（在下面称为＆＃39; json＆＃39;），其中包含一个大的嵌套JSON字符串。我怎样才能解析它，这样我就可以拥有一个包含许多列的干净数据框。特别需要单独列中每个ID的成本和每月金额。理想情况下，我有一张表格如下：

ID，名称，费用，每月

10001，frank，15.85,15.85

10002，mary，30.86,23.03

    d = {'id': ['10001', '10002'], 'json': ['{"costs":[{"cost":15.85}],"policies":[{"logo":"HLIF-transparent-inhouse.png","monthly":15.85,"rating":"A++","waiverOfPremium":1.74,"carrier":"companyabc","face":250000,"term":20,"newFace":null,"newMonthly":null,"isCompanyD":true,"carrierCode":"xyz","product":"XYZt"}],"agentSuggestion":{"costs":[{"cost":15.85}],"options":{"product":"XYZt","gender":"male","healthClass":"0","smoker":"false","age":32,"term":"20","faceAmount":250000,"waiverOfPremiumAmount":1.74,"includeWaiverOfPremium":false,"state":"CT"},"policies":[{"logo":"HLIF-transparent-inhouse.png","monthly":15.85,"rating":"A++","waiverOfPremium":1.74,"carrier":"companyabc","face":250000,"term":20,"newFace":null,"newMonthly":null,"isCompanyD":true,"carrierCode":"xyz","product":"XYZt"}]}}', '{"costs":[{"cost":30.86}],"policies":[{"logo":"HLIF-transparent-inhouse.png","monthly":23.03,"rating":"A++","waiverOfPremium":7.83,"carrier":"companyabc","face":1000000,"term":10,"newFace":null,"newMonthly":null,"isCompanyD":true,"carrierCode":"xyz","product":"XYZt"}],"agentSuggestion":{"costs":[{"cost":30.86}],"options":{"product":"XYZt","gender":"female","healthClass":"0","smoker":"false","age":35,"term":10,"faceAmount":1000000,"waiverOfPremiumAmount":7.83,"includeWaiverOfPremium":true,"state":"GA"},"policies":[{"logo":"HLIF-transparent-inhouse.png","monthly":23.03,"rating":"A++","waiverOfPremium":7.83,"carrier":"companyabc","face":1000000,"term":10,"newFace":null,"newMonthly":null,"isCompanyD":true,"carrierCode":"xyz","product":"XYZt"}]}}'], 'name':['frank','mary']}

   test = pd.DataFrame(data=d)

Answer 1

你去吧。您的JSON有两种不同的成本（成本和agentSuggestion成本），因此两者都添加在此处：

import json
test = pd.DataFrame(d, columns = ['id', 'json', 'name'])
test['cost'] = test['json'].transform(lambda x: json.loads(x)['costs'][0]['cost'])
test['agent_suggestion_cost'] = test['json']\
    .transform(lambda x: json.loads(x)['agentSuggestion']["costs"][0]['cost'])
print(test)

您可以按照类似的逻辑来解析其他字段，例如每月。有关更多参考，请参阅例如here查看JSON预处理器（例如JSTool和Notepad ++）以查看JSON的结构，这将有助于理解其结构。

如果你发现它有用，请接受答案。

Answer 2

Pandas提供了几个用于处理json文件的实用程序。对你的案件有意义的是 pd.read_json和 pd.io.json_normalize。但是，他们希望以不同的json格式输入。

orient : string,

Indication of expected JSON string format. Compatible JSON strings can be produced by to_json() with a corresponding orient value. The set of possible orients is:

'split' : dict like {index -> [index], columns -> [columns], data -> [values]}
'records' : list like [{column -> value}, ... , {column -> value}]
'index' : dict like {index -> {column -> value}}
'columns' : dict like {column -> {index -> value}}
'values' : just the values array
The allowed and default values depend on the value of the typ parameter.

when typ == 'series',
allowed orients are {'split','records','index'}
default is 'index'
The Series index must be unique for orient 'index'.
when typ == 'frame',
allowed orients are {'split','records','index', 'columns','values'}
default is 'columns'
The DataFrame index must be unique for orients 'index' and 'columns'.
The DataFrame columns must be unique for orients 'index', 'columns', and 'records'.

在Pandas DataFrame中解析列，其中一列包含嵌套的JSON字符串

2 个答案: