Python 如何将json文件规范化为数据帧？

我有一个json文件，如下所示。我已经试过这个解决方案了

]

当我尝试使用pandas读取

json

文件时，出现以下错误

ValueError:预期的对象或值

我已经尝试了以下方法

df = pd.read_json(open(r"test_data.json", "r",encoding="utf8"))
df = pd.read_json(r'test_data.json', encoding='utf-8-sig')
basepath = 'C:\\Users\\test\\Downloads' 
pd.read_json(basePath + '\\test_data.json') 

我希望我的输出如下所示

---

错误表明它不理解代码的一部分

您应该检查代码的格式。要做到这一点的页面是：

---

错误表明它不理解代码的一部分

您应该检查代码的格式。要做到这一点的页面是：

用于展平嵌套数据

代码

# Load json data from file
with open('test.json') as f:
    adict = json.load(f)

    # Use json normalize for nested dictionaries
    df = pd.json_normalize(adict)

df

    answersData.employeeId  answersData.answers.Address_2   answersData.answers.Address_2_CC    answersData.answers.CellphoneNumberConsent  answersData.answers.Consent_Given   answersData.answers.DoB answersData.answers.EmailaddressConsent answersData.answers.First_Name  answersData.answers.Gender  answersData.answers.Last_Name   ... answersData.answers.countryName answersData.answers.householdResponsibility answersData.answers.poorCardHas answersData.answers.poorCardReason  answersData.answers.postalCode  answersData.answers.profilePicture  answersData.answers.provinceCity    answersData.createdBy   answersData.dateCreated answersData.type
0   0923a   Address_Line_2_1    Address_2_CC_1  YES Y   1971-07-10T16:00:00.000Z    NotApplicable   First_Name_1    M   Last_Name_1 ... IND husband N   OTH 123456  None    Province_1  MAM_1@123.com   2021-02-23T17:20:33.134Z    profile
1   27l23t  Address_Line_2_2    Address_2_CC_2  YES Y   1980-07-10T16:00:00.000Z    NotApplicable   First_Name_2    M   Last_Name_2 ... IND wife    N   OTH 621 None    Province_2  MAM_2@123.com   2021-02-23T17:20:33.134Z    profile
2   290p    Address_Line_2_3    Address_2_CC_3  NO  N   1991-10-10T16:00:00.000Z    NotApplicable   First_Name_3    M   Last_Name_3 ... IND Father  N   OTH 123456  None    Province_3  MAM_3@123.com   2021-01-11T19:11:20.134Z    profile
3   17mk9i  Address_Line_2_4    Address_2_CC_4  YES Y   1947-07-10T16:00:00.000Z    NotApplicable   First_Name_4    M   Last_Name_4 ... IND mother  N   OTH 123456  None    Province_4  MAM_4@123.com   2021-05-23T17:20:33.134Z    profile
4   17lo8i  Address_Line_2_5    Address_2_CC_5  YES Y   1993-07-10T16:00:00.000Z    NotApplicable   First_Name_5    M   Last_Name_5 ... IND child   N   OTH 123456  None    Province_5  MAM_5@123.com   2021-01-01T17:20:33.134Z    profile
5   17k9i   Address_Line_2_6    Address_2_CC_6  YES Y   1983-07-10T16:00:00.000Z    NotApplicable   First_Name_6    M   Last_Name_6 ... IND husband N   OTH 123456  None    Province_6  MAM_6@123.com   2021-01-16T17:20:33.134Z    profile
6   87p TEST123 Test    YES Y   1801-07-10T16:00:00.000Z    NotApplicable   Test123 M   Test123 ... IND wife    N   OTH 654321  None    Province_2  jo@test.com 2021-02-23T17:20:33.134Z    profile
7   09l07ytw    TEST123 Test    YES Y   1801-07-10T16:00:00.000Z    NotApplicable   Test123 M   Test123 ... IND wife    N   OTH 654321  None    Province_2  jo@test.com 2021-02-23T17:20:33.134Z    profile

用于展平嵌套数据

代码

# Load json data from file
with open('test.json') as f:
    adict = json.load(f)

    # Use json normalize for nested dictionaries
    df = pd.json_normalize(adict)

df

    answersData.employeeId  answersData.answers.Address_2   answersData.answers.Address_2_CC    answersData.answers.CellphoneNumberConsent  answersData.answers.Consent_Given   answersData.answers.DoB answersData.answers.EmailaddressConsent answersData.answers.First_Name  answersData.answers.Gender  answersData.answers.Last_Name   ... answersData.answers.countryName answersData.answers.householdResponsibility answersData.answers.poorCardHas answersData.answers.poorCardReason  answersData.answers.postalCode  answersData.answers.profilePicture  answersData.answers.provinceCity    answersData.createdBy   answersData.dateCreated answersData.type
0   0923a   Address_Line_2_1    Address_2_CC_1  YES Y   1971-07-10T16:00:00.000Z    NotApplicable   First_Name_1    M   Last_Name_1 ... IND husband N   OTH 123456  None    Province_1  MAM_1@123.com   2021-02-23T17:20:33.134Z    profile
1   27l23t  Address_Line_2_2    Address_2_CC_2  YES Y   1980-07-10T16:00:00.000Z    NotApplicable   First_Name_2    M   Last_Name_2 ... IND wife    N   OTH 621 None    Province_2  MAM_2@123.com   2021-02-23T17:20:33.134Z    profile
2   290p    Address_Line_2_3    Address_2_CC_3  NO  N   1991-10-10T16:00:00.000Z    NotApplicable   First_Name_3    M   Last_Name_3 ... IND Father  N   OTH 123456  None    Province_3  MAM_3@123.com   2021-01-11T19:11:20.134Z    profile
3   17mk9i  Address_Line_2_4    Address_2_CC_4  YES Y   1947-07-10T16:00:00.000Z    NotApplicable   First_Name_4    M   Last_Name_4 ... IND mother  N   OTH 123456  None    Province_4  MAM_4@123.com   2021-05-23T17:20:33.134Z    profile
4   17lo8i  Address_Line_2_5    Address_2_CC_5  YES Y   1993-07-10T16:00:00.000Z    NotApplicable   First_Name_5    M   Last_Name_5 ... IND child   N   OTH 123456  None    Province_5  MAM_5@123.com   2021-01-01T17:20:33.134Z    profile
5   17k9i   Address_Line_2_6    Address_2_CC_6  YES Y   1983-07-10T16:00:00.000Z    NotApplicable   First_Name_6    M   Last_Name_6 ... IND husband N   OTH 123456  None    Province_6  MAM_6@123.com   2021-01-16T17:20:33.134Z    profile
6   87p TEST123 Test    YES Y   1801-07-10T16:00:00.000Z    NotApplicable   Test123 M   Test123 ... IND wife    N   OTH 654321  None    Province_2  jo@test.com 2021-02-23T17:20:33.134Z    profile
7   09l07ytw    TEST123 Test    YES Y   1801-07-10T16:00:00.000Z    NotApplicable   Test123 M   Test123 ... IND wife    N   OTH 654321  None    Province_2  jo@test.com 2021-02-23T17:20:33.134Z    profile

---

这不是有效的JSON，在

}{

搜索字符串文字

}{

之间缺少一个

，

-只有一个。它在列表中最后一个对象之前。@TheGreat You can's use:即使在我修复

之后，

之间的问题{，我仍然会得到错误

ValueError:Expected object或value

@DavidMeu-在修复

，

问题后，检查格式化程序说它是有效的

valid（RFC 8259）

这不是有效的JSON，在

}之间缺少
，
搜索字符串文字
}{
-只有一个。它在列表中最后一个对象之前。@TheGreat您可以使用：即使我修复了
，
之间的问题{

，我仍然会得到错误

值错误：预期的对象或值

@DavidMeu-在修复

，

问题后，并且签入格式化程序后说它是有效的

有效的（RFC 8259）

谢谢，向上投票。但我已经修复了格式问题

缺少逗号

，但它仍然会抛出错误，

lines=True

表示“将文件作为每行的json对象读取”。因此您的

值错误：预期对象或值

“数据帧格式”-然后我建议你展示一下你希望数据帧的样子。当然，你没想到我们会猜到吗？我在帖子中更新了当前的输出。谢谢，投了赞成票。但是我已经修复了格式问题

缺少逗号

，但它仍然抛出错误，

lines=True

意味着“每行将文件作为json对象读取”因此，您的

ValueError:Expected object或value

“数据帧格式”-然后我建议你展示一下你想要的数据帧的样子。当然，你没想到我们会猜？我在帖子中更新了当前的输出。哇，太棒了。谢谢，upvoted@TheGreat--很高兴我能帮上忙。这篇文章展示了如何获得不同程度的扁平化。哇，太棒了。谢谢，upvoted@TheGreat--很高兴我能帮忙。文章显示如何获得不同程度的扁平化。