[编辑]
我有以下方式的数据框
ID , EmailID , First Name, Last Name, Gender, DOB
1 , [email protected] , One First , One Last , M , 11-13-1920
2 , [email protected] , Two First , Two Last , M , 11-13-1920
3 , [email protected] , Thr First , Thr Last , M , 11-13-1920
4 , [email protected] , Fou First , Fou Last , M , 11-13-1920
5 , [email protected] , Fiv First , Fiv Last , M , 11-13-1920
6 , [email protected] , Six First , Six Last , M , 11-13-1920
我想要下面想要的
[
{"_id" : "[email protected]", "_souce" : {"ID": 1, "EmailID" : "[email protected]", "data" : "{'ID':'1', 'EmailID': '[email protected]', 'First Name' : 'One First', 'Last Name' : 'One First', 'Gender': 'M', 'DOB': '11-13-1920'}"}},
{"_id" : "[email protected]", "_souce" : {"ID": 2, "EmailID" : "[email protected]", "data" : "{'ID':'2', 'EmailID': '[email protected]', 'First Name' : 'Two First', 'Last Name' : 'Two First', 'Gender': 'M', 'DOB': '11-13-1920'}"}},
{"_id" : "[email protected]", "_souce" : {"ID": 3, "EmailID" : "[email protected]", "data" : "{'ID':'3', 'EmailID': '[email protected]', 'First Name' : 'The First', 'Last Name' : 'The First', 'Gender': 'M', 'DOB': '11-13-1920'}"}},
{"_id" : "[email protected]", "_souce" : {"ID": 4, "EmailID" : "[email protected]", "data" : "{'ID':'4', 'EmailID': '[email protected]', 'First Name' : 'Fou First', 'Last Name' : 'Fou First', 'Gender': 'M', 'DOB': '11-13-1920'}"}},
{"_id" : "[email protected]", "_souce" : {"ID": 5, "EmailID" : "[email protected]", "data" : "{'ID':'5', 'EmailID': '[email protected]', 'First Name' : 'Fiv First', 'Last Name' : 'Fiv First', 'Gender': 'M', 'DOB': '11-13-1920'}"}},
{"_id" : "[email protected]", "_souce" : {"ID": 6, "EmailID" : "[email protected]", "data" : "{'ID':'6', 'EmailID': '[email protected]', 'First Name' : 'Six First', 'Last Name' : 'Six First', 'Gender': 'M', 'DOB': '11-13-1920'}"}}
]
我怎样才能有效地做到这一点？我应该循环并通过它或通过熊猫制作另一个数组吗
转换后的字典应具有
将所有行转换为jsons转换为新列，然后添加_id列，按预期顺序将最后设置的列按字典顺序排序DataFrame.to_dict：
df['data'] = df.apply(lambda x: x.to_json(), axis=1)
df['_souce'] = df[['ID','EmailID','data']].apply(lambda x: x.to_dict(), axis=1)
df['_id'] = df['ID'].astype(str)+ '-' + df['EmailID'].astype(str)
d = df[['_id','_souce']].to_dict(orient='records')
print (d)
[{
'_id': '[email protected]',
'_souce': {
'ID': 1,
'EmailID': '[email protected]',
'data': '{"ID":1,"EmailID":"[email protected]","First Name":"One First","Last Name":"One Last","Gender":"M","DOB":"11-13-1920"}'
}
}, {
'_id': '[email protected]',
'_souce': {
'ID': 2,
'EmailID': '[email protected]',
'data': '{"ID":2,"EmailID":"[email protected]","First Name":"Two First","Last Name":"Two Last","Gender":"M","DOB":"11-13-1920"}'
}
}, {
'_id': '[email protected]',
'_souce': {
'ID': 3,
'EmailID': '[email protected]',
'data': '{"ID":3,"EmailID":"[email protected]","First Name":"Thr First","Last Name":"Thr Last","Gender":"M","DOB":"11-13-1920"}'
}
}, {
'_id': '[email protected]',
'_souce': {
'ID': 4,
'EmailID': '[email protected]',
'data': '{"ID":4,"EmailID":"[email protected]","First Name":"Fou First","Last Name":"Fou Last","Gender":"M","DOB":"11-13-1920"}'
}
}, {
'_id': '[email protected]',
'_souce': {
'ID': 5,
'EmailID': '[email protected]',
'data': '{"ID":5,"EmailID":"[email protected]","First Name":"Fiv First","Last Name":"Fiv Last","Gender":"M","DOB":"11-13-1920"}'
}
}, {
'_id': '[email protected]',
'_souce': {
'ID': 6,
'EmailID': '[email protected]',
'data': '{"ID":6,"EmailID":"[email protected]","First Name":"Six First","Last Name":"Six Last","Gender":"M","DOB":"11-13-1920"}'
}
}]
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