如何使用重复组中的嵌套结构将数据框架转换为JSON文件。我的团队要求我将数据框架转换为具有特定嵌套结构的JSON文件。我的数据框是以下一个： df = pd.dataframe（{ “类型”：['客户'，“客户”，“客户”，'cu ...

Question

我想实现的是创建一个由列“类型”，“ customer_id”和包含字段“电子邮件”和“策略＃”的字典分组的JSON文件。这三个要素用于分组。属性字典必须包含另一个嵌套词典，称为policy_no，receipt_no，payment_code和klados。必须重复每种组合“类型”，“ customer_id”和“属性”的“策略详细信息”字典，如下：

        "type": "customer",
        "customer_id": "1-0000001",
        "attributes": {
            "email": "[email protected]",
            "# of policies": 4,
            "policies details": [
                {
                    "POLICY_NO": 000000001,
                    "RECEIPT_NO": 420000001,
                    "PAYMENT_CODE": "RF35000000000000000000001"
                    "KLADOS": "Αυτοκινήτου"
},
                {
                    "POLICY_NO": 000000002,
                    "RECEIPT_NO": 420000002,
                    "PAYMENT_CODE": "RF35000000000000000000002"
                    "KLADOS": "Αυτοκινήτου"
                                        ......

为了达到这种格式，我尝试了以下方法：

1st方法

# Create the new 'policy_details' field as dictionary
df['policy_details'] = df[['POLICY_NO', 'RECEIPT_NO', 'PAYMENT_CODE', 'KLADOS']].to_dict(orient='records')

# Create the new 'attributes' field as dictionary with the nested 'policy_details'
df['attributes'] = df[['email', '# of policies', 'policy_details']].to_dict(orient='records')


# JSON Section

# Create the JSON structure
j = df[['type', 'customer_id', 'attributes']].to_json(orient='records', force_ascii = False)

print(j)

But
'TYPE'，'customer_id'和属性未分组（每个策略_Details都重复）
1. “ policy_details”支架是卷曲的，不是正方形，如下所示：
  
  [{ "type": "customer", "customer_id": "1-0000001", "attributes": { "email": "[email protected]", "# of policies": 4, "policy_details": { "POLICY_NO": "000000001", "RECEIPT_NO": 420000001, "PAYMENT_CODE": "RF35000000000000000000001", "KLADOS": "Αυτοκινήτου" } } }, { "type": "customer", "customer_id": "1-0000001", "attributes": { "email": "[email protected]", "# of policies": 4, "policy_details": { "POLICY_NO": "000000002", "RECEIPT_NO": "420000002", "PAYMENT_CODE": "RF35000000000000000000002", "KLADOS": "Αυτοκινήτου" } } }, { "type": "customer", "customer_id": "1-0000001", "attributes": { "email": "[email protected]", "# of policies": 4, "policy_details": { "POLICY_NO": "000000003", "RECEIPT_NO": "420000003", "PAYMENT_CODE": "RF35000000000000000000003", "KLADOS": "Αυτοκινήτου" } } }, { "type": "customer", "customer_id": "1-0000001", "attributes": { "email": "[email protected]", "# of policies": 4, "policy_details": { "POLICY_NO": "000000004", "RECEIPT_NO": "420000004", "PAYMENT_CODE": "RF35000000000000000000004", "KLADOS": "Αυτοκινήτου" } } }, { "type": "customer", "customer_id": "1-0000002", "attributes": { "email": "[email protected]", "# of policies": 6, "policy_details": { "POLICY_NO": "000000005", "RECEIPT_NO": "420000005", "PAYMENT_CODE": "RF35000000000000000000005", "KLADOS": "Αυτοκινήτου" } } }, { "type": "customer", "customer_id": "1-0000002", "attributes": { "email": "[email protected]", "# of policies": 6, "policy_details": { "POLICY_NO": "000000006", "RECEIPT_NO": "420000006", "PAYMENT_CODE": "RF35000000000000000000006", "KLADOS": "Αυτοκινήτου" } } }, { "type": "customer", "customer_id": "1-0000002", "attributes": { "email": "[email protected]", "# of policies": 6, "policy_details": { "POLICY_NO": "000000007", "RECEIPT_NO": "420000007", "PAYMENT_CODE": "null", "KLADOS": "Αυτοκινήτου" } } }, { "type": "customer", "customer_id": "1-0000002", "attributes": { "email": "[email protected]", "# of policies": 6, "policy_details": { "POLICY_NO": "000000008", "RECEIPT_NO": "420000008", "PAYMENT_CODE": "RF35000000000000000000008", "KLADOS": "Αυτοκινήτου" } } }, { "type": "customer", "customer_id": "1-0000002", "attributes": { "email": "[email protected]", "# of policies": 6, "policy_details": { "POLICY_NO": "000000009", "RECEIPT_NO": "420000009", "PAYMENT_CODE": "RF35000000000000000000009", "KLADOS": "Αυτοκινήτου" } } }, { "type": "customer", "customer_id": "1-0000002", "attributes": { "email": "[email protected]", "# of policies": 6, "policy_details": { "POLICY_NO": "000000010", "RECEIPT_NO": "420000010", "PAYMENT_CODE": "null", "KLADOS": "Αυτοκινήτου" } } }, { "type": "customer", "customer_id": "1-0000003", "attributes": { "email": "[email protected]", "# of policies": 5, "policy_details": { "POLICY_NO": "000000011", "RECEIPT_NO": "420000011", "PAYMENT_CODE": "RF35000000000000000000011", "KLADOS": "Αυτοκινήτου" } } }, { "type": "customer", "customer_id": "1-0000003", "attributes": { "email": "[email protected]", "# of policies": 5, "policy_details": { "POLICY_NO": "000000012", "RECEIPT_NO": "420000012", "PAYMENT_CODE": "RF35000000000000000000012", "KLADOS": "Αυτοκινήτου" } } }, { "type": "customer", "customer_id": "1-0000003", "attributes": { "email": "[email protected]", "# of policies": 5, "policy_details": { "POLICY_NO": "000000013", "RECEIPT_NO": "420000013", "PAYMENT_CODE": "null", "KLADOS": "Αυτοκινήτου" } } }, { "type": "customer", "customer_id": "1-0000003", "attributes": { "email": "[email protected]", "# of policies": 5, "policy_details": { "POLICY_NO": "000000014", "RECEIPT_NO": "420000014", "PAYMENT_CODE": "RF35000000000000000000014", "KLADOS": "Αυτοκινήτου" } } }, { "type": "customer", "customer_id": "1-0000003", "attributes": { "email": "[email protected]", "# of policies": 5, "policy_details": { "POLICY_NO": "000000015", "RECEIPT_NO": "420000015", "PAYMENT_CODE": "RF35000000000000000000015", "KLADOS": "Αυτοκινήτου" } } } ]
2. 2ND方法
# Create the new 'attributes' field as dictionary df['attributes'] = df[['email', '# of policies']].to_dict(orient='records') # Convert the dictionary 'attributes' to a string df['attributes'] = df[['attributes']].astype(str) # JSON Section # Create the JSON structure j = (df.groupby(['type', 'customer_id', 'attributes']) .apply(lambda x: x[['POLICY_NO', 'RECEIPT_NO','PAYMENT_CODE', 'KLADOS']].to_dict('records'), include_groups=False) .reset_index() .rename(columns={0:'policies details'}) .to_dict(orient='records') ) # Convert list of dictionaries to JSON json_output = json.dumps(j, indent=4, ensure_ascii=False) print(json_output)
这个时间：
''TYPE'，'customer_id'和“属性”最终被分组，但“属性”表示为字符串，而不是字典（键：值对）
1. “ policy_details”具有方括号，但没有嵌套（如下所示，它位于相同的“属性”级别：
  
  [ { "type": "customer", "customer_id": "1-0000001", "attributes": "{'email': '[email protected]', '# of policies': 4}", "policies details": [ { "POLICY_NO": "000000001", "RECEIPT_NO": 420000001, "PAYMENT_CODE": "RF35000000000000000000001", "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": "000000002", "RECEIPT_NO": "420000002", "PAYMENT_CODE": "RF35000000000000000000002", "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": "000000003", "RECEIPT_NO": "420000003", "PAYMENT_CODE": "RF35000000000000000000003", "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": "000000004", "RECEIPT_NO": "420000004", "PAYMENT_CODE": "RF35000000000000000000004", "KLADOS": "Αυτοκινήτου" } ] }, { "type": "customer", "customer_id": "1-0000002", "attributes": "{'email': '[email protected]', '# of policies': 6}", "policies details": [ { "POLICY_NO": "000000005", "RECEIPT_NO": "420000005", "PAYMENT_CODE": "RF35000000000000000000005", "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": "000000006", "RECEIPT_NO": "420000006", "PAYMENT_CODE": "RF35000000000000000000006", "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": "000000007", "RECEIPT_NO": "420000007", "PAYMENT_CODE": "null", "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": "000000008", "RECEIPT_NO": "420000008", "PAYMENT_CODE": "RF35000000000000000000008", "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": "000000009", "RECEIPT_NO": "420000009", "PAYMENT_CODE": "RF35000000000000000000009", "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": "000000010", "RECEIPT_NO": "420000010", "PAYMENT_CODE": "null", "KLADOS": "Αυτοκινήτου" } ] }, { "type": "customer", "customer_id": "1-0000003", "attributes": "{'email': '[email protected]', '# of policies': 5}", "policies details": [ { "POLICY_NO": "000000011", "RECEIPT_NO": "420000011", "PAYMENT_CODE": "RF35000000000000000000011", "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": "000000012", "RECEIPT_NO": "420000012", "PAYMENT_CODE": "RF35000000000000000000012", "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": "000000013", "RECEIPT_NO": "420000013", "PAYMENT_CODE": "null", "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": "000000014", "RECEIPT_NO": "420000014", "PAYMENT_CODE": "RF35000000000000000000014", "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": "000000015", "RECEIPT_NO": "420000015", "PAYMENT_CODE": "RF35000000000000000000015", "KLADOS": "Αυτοκινήτου" } ] } ]
2. ```
我想实现的JSON结构。
```
  [ { "type": "customer", "customer_id": "1-0000001", "attributes": { "email": "[email protected]", "# of policies": 4, "policies details": [ { "POLICY_NO": 000000001, "RECEIPT_NO": 420000001, "PAYMENT_CODE": "RF35000000000000000000001" "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": 000000002, "RECEIPT_NO": 420000002, "PAYMENT_CODE": "RF35000000000000000000002" "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": 000000003, "RECEIPT_NO": 420000003, "PAYMENT_CODE": "RF35000000000000000000003" "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": 000000004, "RECEIPT_NO": 420000004, "PAYMENT_CODE": "RF35000000000000000000004" "KLADOS": "Αυτοκινήτου" } ] } }, { "type": "customer", "customer_id": "1-0000002", "attributes": { "email": "[email protected]", "# of policies": 6, "policies details": [ { "POLICY_NO": 000000005, "RECEIPT_NO": 420000005, "PAYMENT_CODE": "RF35000000000000000000005" "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": 000000006, "RECEIPT_NO": 420000006, "PAYMENT_CODE": "RF35000000000000000000006" "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": 000000007, "RECEIPT_NO": 420000007, "PAYMENT_CODE": "null" "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": 000000008, "RECEIPT_NO": 420000008, "PAYMENT_CODE": "RF35000000000000000000008" "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": 000000009, "RECEIPT_NO": 420000009, "PAYMENT_CODE": "RF35000000000000000000009" "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": 000000010, "RECEIPT_NO": 420000010, "PAYMENT_CODE": "null" "KLADOS": "Αυτοκινήτου" } ] } }, { "type": "customer", "customer_id": "1-0000003", "attributes": { "email": "[email protected]", "# of policies": 5, "policies details": [ { "POLICY_NO": 000000011, "RECEIPT_NO": 420000011, "PAYMENT_CODE": "RF35000000000000000000011" "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": 000000012, "RECEIPT_NO": 420000012, "PAYMENT_CODE": "RF35000000000000000000012" "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": 000000013, "RECEIPT_NO": 420000013, "PAYMENT_CODE": "null" "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": 000000014, "RECEIPT_NO": 420000014, "PAYMENT_CODE": "RF35000000000000000000014" "KLADOS": "Αυτοκινήτου" }, { "POLICY_NO": 000000015, "RECEIPT_NO": 420000015, "PAYMENT_CODE": "RF35000000000000000000015" "KLADOS": "Αυτοκινήτου" } ] } } ]

这里是您可以使用pandasgroupby

尝试使用所需格式的一种方法

希望这对您有帮助，让我知道它是否对您有用。


import json 
list_of_cust= []
// Group By on require columns 
for x, y in df.groupby(by=["type", "customer_id",'email', '# of policies']):
    // selecting required columns on group By
    policy = y[['POLICY_NO', 'RECEIPT_NO', 'PAYMENT_CODE','KLADOS']]
    obj = (x, policy)
    list_of_cust.append(obj)

jsonstr = "["
for x in list_of_cust:
    str = f''' {{ "type": "{x[0][0]}",
        "customer_id": "{x[0][1]}",
        "attributes": {{
            "email": "{x[0][2]}",
            "# of policies": {x[0][3]},
            "policies details": {json.dumps(x[1].to_dict(orient="records"), indent=4, ensure_ascii=False)}
            }}
        }},'''
    jsonstr= jsonstr + str
jsonstr= jsonstr[0:len(jsonstr)-1] + "]"

如何使用重复组中的嵌套结构将数据框架转换为JSON文件。我的团队要求我将数据框架转换为具有特定嵌套结构的JSON文件。我的数据框是以下一个： df = pd.dataframe（{ “类型”：['客户'，“客户”，“客户”，'cu ...

问题描述投票：0回答：1

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最新问题

如何使用重复组中的嵌套结构将数据框架转换为JSON文件。 我的团队要求我将数据框架转换为具有特定嵌套结构的JSON文件。我的数据框是以下一个： df = pd.dataframe（{ “类型”：['客户'，“客户”，“客户”，'cu ...

问题描述 投票：0回答：1

1个回答

最新问题

如何使用重复组中的嵌套结构将数据框架转换为JSON文件。我的团队要求我将数据框架转换为具有特定嵌套结构的JSON文件。我的数据框是以下一个： df = pd.dataframe（{ “类型”：['客户'，“客户”，“客户”，'cu ...

问题描述投票：0回答：1