Python 如何为熊猫数据帧和其他输出数据列表个性化Json输出

我有下面的代码，它提供了信息

每月收到的工资和工资贷记前的余额

检查是否在每月5日前将工资记入贷方，如果是，则列出带日期的工资清单

过去3个月的平均工资

代码

    if Salary.empty:
        total_Salary = 0
        Salary_b4_5th = "No"
        #sum_Salary = {'Salary': "-",'date': "-"}
        avg_Salary_3months = 0
        print("--------------")
        print("Is salary received before 5th :", Salary_b4_5th)
        print("--------------")
        print("No of times salary received :", total_Salary)

    else:

        Salary.date = pd.to_datetime(Salary.date, format="%d-%m-%Y")
        check_Salary_date = Salary[Salary['date'].dt.day <= 5].groupby('date').sum()
        check_Salary_date.index = check_Salary_date.index.strftime('%d-%b-%y')
        check_Salary_date.index.name = 'Date'
        sum_Salary = Salary.groupby('date').sum()
        sum_Salary = Salary.groupby(pd.Grouper(key='date', freq='1M')).sum()
        sum_Salary_3months = Salary.groupby(pd.Grouper(key='date', freq='1M')).mean().dropna(subset=['Salary']).tail(3)
        avg_Salary_3months = sum_Salary_3months['Salary'].mean()
        sum_Salary.index = sum_Salary.index.strftime('%b-%y')
        sum_Salary.index.name = 'Month'
        total_Salary = len(sum_Salary.axes[0])
        print("--------------")
        print("\nSalary received per month \n", sum_Salary)
        print("--------------")
        print("Total Count of salary received :", total_Salary)
        print("--------------")
        if not check_Salary_date.empty:
            Salary_b4_5th = "Yes"
            print("\nIs salary credited before 5th of every month:", Salary_b4_5th)
            print("--------------")
            print("List of salary credited before 5th : \n", check_Salary_date)
        else:
            Salary_b4_5th = "No"
            print("\nIs salary credited before 5th of every month:", Salary_b4_5th)
        print("--------------")
        print("Avg Salary of last 3 months :", avg_Salary_3months)
        print("--------------")

预期Json输出：

[{"Is salary credited before 5th": "True"},

{
    "List of salary credited before 5th": {
        "Balance before Salary": {
          "03-Aug-18":176.48,
          "04-Sep-18":48.48,
          "05-Oct-18":241.48

        },
        "Salary": {
        "03-Aug-18":14783.0,
        "04-Sep-18":16249.0,
        "05-Oct-18":14448.0

        }
    }
},
{
    "Salary received per month": {
        "Balance before Salary": {
          "Jun-18":27.2,
          "Jul-18":88.2,
          "Aug-18":176.48,
          "Sep-18":48.48,
          "Oct-18":241.48,
          "Nov-18":49.48

        },
        "Salary": {
          "Jun-18":15300.0,
          "Jul-18":15300.0,
          "Aug-18":14783.0,
          "Sep-18":16249.0,
          "Oct-18":14448.0,
          "Nov-18":15663.0}
    }
},

{"Avg Salary of last 3 months" : 15453.333333333334} 
]

问题：

如何将多个数据帧保存为Json文件

如何将多个非数据帧与数据帧一起保存为json文件

简而言之，我希望得到一个与预期Json输出中所给出的完全相同的输出。如何获得此输出？

使用

来记录（）

将用户答案存储到变量
预期值\u ans\u 1
和
预期值\u ans\u 2
中。
这里，
df1
和
df2
是您的两个数据帧
 在预期输出的第一个元素中，
True
如何转换为
yes
！对不起，这是
真的
。我已经改变了我将如何将其放入这个模型：
df.to_json（“/path/test.json”）
我将在哪里序列化这些数据？我们如何创建这种格式
[{“5号之前的工资是否已计入”：str（预期值）}，{“5号之前计入的工资列表”：df1.to_dict（）}，{“每月收到的工资”：df2.to_dict（）}，{”过去3个月的平均工资：预期2}]
您能告诉我如何创建该格式吗？以及如何将其保存为本地系统中的json文件我已根据您的需要更新了答案以保存到json文件中。创建该格式需要所述的嵌套字典列表。我需要您的帮助，当数据帧
df1.to_dict（）
和
df2.to_dict（）时，我遇到了一个错误
为空。有什么办法可以解决这个问题吗
[{"Is salary credited before 5th": "True"},

{
    "List of salary credited before 5th": {
        "Balance before Salary": {
          "03-Aug-18":176.48,
          "04-Sep-18":48.48,
          "05-Oct-18":241.48

        },
        "Salary": {
        "03-Aug-18":14783.0,
        "04-Sep-18":16249.0,
        "05-Oct-18":14448.0

        }
    }
},
{
    "Salary received per month": {
        "Balance before Salary": {
          "Jun-18":27.2,
          "Jul-18":88.2,
          "Aug-18":176.48,
          "Sep-18":48.48,
          "Oct-18":241.48,
          "Nov-18":49.48

        },
        "Salary": {
          "Jun-18":15300.0,
          "Jul-18":15300.0,
          "Aug-18":14783.0,
          "Sep-18":16249.0,
          "Oct-18":14448.0,
          "Nov-18":15663.0}
    }
},

{"Avg Salary of last 3 months" : 15453.333333333334} 
]

import json
expected_ans_1 = True
expected_ans_2 = 15453.333333333334

js = [{"Is salary credited before 5th": str(expected_ans_1)},
 {"List of salary credited before 5th": df1.to_dict()},
 {"Salary received per month": df2.to_dict()},
 {"Avg Salary of last 3 months": expected_ans_2}]

with open('test.json', 'w') as outfile:
    json.dump(js, outfile)