将Pandas数据帧转换为嵌套的json

Question

我需要将pandas数据帧转换成嵌套的json。.to_json的输出给出了以下内容

{"Annual Expenditure":{"0":250,"1":250},"Annual Frequency":{"0":1,"1":1},"Avg days":{"0":null,"1":null},"First visit":{"0":1449100800000,"1":1490054400000},"Frequency":{"0":1,"1":1},"Guest":{"0":25642,"1":55521},"Last visit":{"0":1449100800000,"1":1490054400000},"Monetory":{"0":250,"1":250},"Recency":{"0":701,"1":227},"Visit_Ids":{"0":[80611],"1":[342104]},"RFMClass":{"0":"444","1":"144"},"AvgLTV":{"0":13305.7692307692,"1":13305.7692307692}}

但是我需要在一个嵌套的json中使用key作为guest_id并有相应的值。像这样：

^{pr2}$

.to_json（）函数不能按我想要的方式工作，我已经用尽了所有的选项。你知道怎么做吗？在

编辑：

谢谢你的回答！我将获取json输出作为循环的一部分，并且我得到的每个输出都是唯一的json。那么，有没有一种方法可以创建以下内容？公司名称：

{45: {"50492":{"Annual Expenditure":1000,"Annual Frequency":1,"Avg days":null,"First visit":1486339200000,"Frequency":1,"Last visit":1486339200000,"Merchants":45,"Monetory":1000,"Recency":270,"Visit_Ids":[300758],"RFMClass":"144","AvgLTV":113800.0},"1041":{"Annual Expenditure":1000,"Annual Frequency":1,"Avg days":null,"First visit":1478649600000,"Frequency":1,"Last visit":1478649600000,"Merchants":45,"Monetory":1000,"Recency":359,"Visit_Ids":[257022],"RFMClass":"244","AvgLTV":113800.0},"783":{"Annual Expenditure":1000,"Annual Frequency":1,"Avg days":null,"First visit":1457049600000,"Frequency":1,"Last visit":1457049600000,"Merchants":45,"Monetory":1000,"Recency":609,"Visit_Ids":[123464],"RFMClass":"444","AvgLTV":113800.0}}}

这里的45是一个唯一的标识符，我将在循环结束时传入它。在

Answer 1

将^{}+^{}与参数orient一起使用：

df.set_index('Guest').to_json('file.json', orient='index')

---

^{pr2}$

输入DataFrame：

d = {"Annual Expenditure":{"0":250,"1":250},"Annual Frequency":{"0":1,"1":1},"Avg days":{"0":np.nan,"1":np.nan},"First visit":{"0":1449100800000,"1":1490054400000},"Frequency":{"0":1,"1":1},"Guest":{"0":25642,"1":55521},"Last visit":{"0":1449100800000,"1":1490054400000},"Monetory":{"0":250,"1":250},"Recency":{"0":701,"1":227},"Visit_Ids":{"0":[80611],"1":[342104]},"RFMClass":{"0":"444","1":"144"},"AvgLTV":{"0":13305.7692307692,"1":13305.7692307692}}

---

df = pd.DataFrame(d)
print (df)
   Annual Expenditure  Annual Frequency  Avg days        AvgLTV  \
0                 250                 1       NaN  13305.769231   
1                 250                 1       NaN  13305.769231   

     First visit  Frequency  Guest     Last visit  Monetory RFMClass  Recency  \
0  1449100800000          1  25642  1449100800000       250      444      701   
1  1490054400000          1  55521  1490054400000       250      144      227   

  Visit_Ids  
0   [80611]  
1  [342104] 

编辑：

j = df.set_index('Guest').to_json(orient='index')
j_final = {45: j}