zufanka zufanka - 2 years ago 138
JSON Question

Python: json_normalize a pandas series gives TypeError

I have tens of thousands rows of json snippets like this in a pandas series

df["json"]


[{
'IDs': [{
'lotId': '1',
'Id': '123456'
}],
'date': '2009-04-17',
'bidsCount': 2,
}, {
'IDs': [{
'lotId': '2',
'Id': '123456'
}],
'date': '2009-04-17',
'bidsCount': 4,
}, {
'IDs': [{
'lotId': '3',
'Id': '123456'
}],
'date': '2009-04-17',
'bidsCount': 8,
}]


Sample of the original file:

{"type": "OPEN","title": "rainbow","json": [{"IDs": [{"lotId": "1","Id": "123456"}],"date": "2009-04-17","bidsCount": 2,}, {"IDs": [{"lotId": "2","Id": "123456"}],"date": "2009-04-17","bidsCount": 4,}, {"IDs": [{"lotId": "3","Id": "123456"}],"date": "2009-04-17","bidsCount": 8,}]}
{"type": "CLOSED","title": "clouds","json": [{"IDs": [{"lotId": "1","Id": "23345"}],"date": "2009-05-17","bidsCount": 2,}, {"IDs": [{"lotId": "2","Id": "23345"}],"date": "2009-05-17","bidsCount": 4,}, {"IDs": [{"lotId": "3","Id": "23345"}],"date": "2009-05-17","bidsCount": 8,}]}


df = pd.read_json("file.json", lines=True)


I am trying to make them into a data frame, something like

Id lotId bidsCount date
123456 1 2 2009-04-17
123456 2 4 2009-04-17
123456 3 8 2009-04-17


by using

json_normalize(df["json"])


However I get

AttributeError: 'list' object has no attribute 'values'


I guess the json snippet is seen as a list, however I can not figure out how to make it work otherwise.
Help appreciated!

Answer Source

I think your df['json'] is a nested list. You can use a for loop and concatenate the dataframe to get the big dataframe i.e

Data:

{"type": "OPEN","title": "rainbow","json": [{"IDs": [{"lotId": "1","Id": "123456"}],"date": "2009-04-17","bidsCount": 2,}, {"IDs": [{"lotId": "2","Id": "123456"}],"date": "2009-04-17","bidsCount": 4,}, {"IDs": [{"lotId": "3","Id": "123456"}],"date": "2009-04-17","bidsCount": 8,}]}
{"type": "CLOSED","title": "clouds","json": [{"IDs": [{"lotId": "1","Id": "23345"}],"date": "2009-05-17","bidsCount": 2,}, {"IDs": [{"lotId": "2","Id": "23345"}],"date": "2009-05-17","bidsCount": 4,}, {"IDs": [{"lotId": "3","Id": "23345"}],"date": "2009-05-17","bidsCount": 8,}]}

df = pd.read_json("file.json", lines=True)

DataFrame:

new_df = pd.concat([pd.DataFrame(json_normalize(x)) for x in df['json']],ignore_index=True)

Output:

                                IDs  bidsCount        date
0  [{'Id': '123456', 'lotId': '1'}]          2  2009-04-17
1  [{'Id': '123456', 'lotId': '2'}]          4  2009-04-17
2  [{'Id': '123456', 'lotId': '3'}]          8  2009-04-17
3   [{'Id': '23345', 'lotId': '1'}]          2  2009-05-17
4   [{'Id': '23345', 'lotId': '2'}]          4  2009-05-17
5   [{'Id': '23345', 'lotId': '3'}]          8  2009-05-17

If you want the keys of IDs as columns then you use

new_df['lotId'] = [x[0]['lotId'] for x in new_df['IDs']]
new_df['IDs'] = [x[0]['Id'] for x in new_df['IDs']]
      IDs  bidsCount        date lotId
0  123456          2  2009-04-17     1
1  123456          4  2009-04-17     2
2  123456          8  2009-04-17     3
3   23345          2  2009-05-17     1
4   23345          4  2009-05-17     2
5   23345          8  2009-05-17     3
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