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

Question

我在Python中有一個DataFrame，看起來像下面的一個。 有一列（在下文中稱為“ json”），其中包含一個大的嵌套JSON字符串。 我該如何解析它，這樣我就可以擁有一個包含許多列的干凈數據框。 只是特別需要在單獨的列中每個ID的費用和每月金額。 理想情況下，我有一張看起來像的桌子：

ID，名稱，費用，每月

10001，坦率，15.85，15.85

10002，瑪麗，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

妳去 JSON中有2種不同的成本（cost和agentSuggestion成本），因此都在此處添加：

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)

您可以遵循類似的邏輯來解析其他字段，例如每月。 有關更多參考，請參見此處。到處尋找JSON 前綴 （例如，帶有Notepad ++的JSTool ）以查看JSON的結構，這將有助於理解其結構。

如果您認為有用，請接受答案。

Answer 2

Pandas提供了一些用於處理json文件的實用程序。 對於您的情況有意義的是pd.read_json和pd.io.json_normalize 。 但是他們確實希望輸入的格式與您使用的json格式不同。

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'.