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Nested Json Array not handled by pandas dataframe / pd.json_normalize

Any help is really appreciated,
I have the below JSON, provided by API call. I've omitted sensitive data, but the key names are as presented ("value", "value_raw").
[{
"Position": "1234",
"StartDate": "2020-11-21",
"ID": "1234",
"CloseDate": "2020-12-07",
"Title": "This is a title",
"data": [{
"value": 1234
},
{
"value": "some text"
},
{
"value": "some text",
"value_raw": 11111
},
{
"value_raw": 11111,
"value": "some text"
},
{
"value": "null"
},
{
"value": "some text",
"value_raw": 22222
},
{
"value_raw": 2222222,
"value": "some text"
},
{
"value_raw": "null",
"value": "null"
},
{
"value_raw": "null",
"value": "null"
},
{
"value_raw": 2222222,
"value": "some text"
},
{
"value": "null"
},
{
"value": "some text",
"value_raw": 2222
},
{
"value": 1
},
{
"value": "some text",
"value_raw": 22222
}
]
}, {
"Position": "1235",
"StartDate": "2020-12-21",
"ID": "1235",
"CloseDate": "2021-01-12",
"Title": "some text",
"data": [{
"value": 1235
},
{
"value": "some text"
},
{
"value": "some text",
"value_raw": 1111
},
{
"value": "some text",
"value_raw": 1111
},
{
"value": "null"
},
{
"value_raw": 1111,
"value": "some text"
},
{
"value_raw": 11111,
"value": "some text"
},
{
"value_raw": "null",
"value": "null"
},
{
"value": "some text",
"value_raw": 1111
},
{
"value_raw": "null",
"value": "null"
},
{
"value": "null"
},
{
"value": "some text",
"value_raw": 22222
},
{
"value": 1
},
{
"value_raw": 22222,
"value": "some text"
}
]
}, {
"ID": "1236",
"Position": "1236",
"StartDate": "2021-07-12",
"data": [{
"value": 1236
},
{
"value": "some text"
},
{
"value_raw": 1111,
"value": "some text"
},
{
"value": "some text",
"value_raw": 1111
},
{
"value": "null"
},
{
"value_raw": 1111,
"value": "some text"
},
{
"value_raw": 1111,
"value": "some text"
},
{
"value_raw": "null",
"value": "null"
},
{
"value": "null",
"value_raw": "null"
},
{
"value": "some text",
"value_raw": 111
},
{
"value": "null"
},
{
"value": "some text",
"value_raw": 12223
},
{
"value": 1
},
{
"value": "some text",
"value_raw": 2222
}
],
"Title": "some text",
"CloseDate": "2021-07-23"
}
]
When I normalize "data" using;
df = pd.json_normalize(mydata, record_path=['data'])
I end up with an output of 2 columns x 42 rows (excl. headings), illustration:
value
value_raw
1234
This is a title
some text
11111
Corporation
11111
null
some text
22222
some text
2222222
null
null
null
null
The only data im interested in is the key "value", I'd also like to know how to lay this data out as 3 rows x 14 columns (one row for each ID = '1234', '1235' & '1236' and no column headings needed as they provide zero benefit with the naming convention "value")
Any starting point would be great, I have spent hours looking at previous questions. What I have noticed is that the JSON I receive is very different to all of the examples out there.
Thanks everyone

Nice question. I think it's not possible with json_normalize.
Therefore did it with a loop and list comprehension:
values_list_all_rows = []
for json_element in json_list:
values_list_per_row = [value_dict["value"] for value_dict in json_element["data"] if "value" in value_dict]
values_list_all_rows.append(values_list_per_row)
pd.DataFrame(values_list_all_rows)
Gives (it will set None as value where rows has less values than other column):
0 1 2 3 4 5 6 7 8 9 10 11 12 13
1234 some text some text some text null some text some text null null some text null some text 1 some text
1235 some text some text some text null some text some text null some text null null some text 1 some text
1236 some text some text some text null some text some text null null some text null some text 1 None

Python - Get Nested Data from Multiple Levels

Wasn't sure how to title this question but I am working with the Quickbooks Online API and when querying a report like BalanceSheet or GeneralLedger the API returns data rows in multiple nested levels which is quite frustrating to parse through.
Example of the BalanceSheet return included below. I am only interested in the data from "Row" objects but as you can see that can be returned in 1, 2, 3 or more different levels of data. I am thinking of going through each level to check for Rows and then get each Row but that seems overly complex as I would need multiple for loops for each level.
I'm wondering if there is a better way to get each "Row" in that data without regard to which level it is on? Any ideas would be appreciated!
Here's an example of a return from their sandbox data:
{
"Header": {
"Time": "2021-04-28T14:12:17-07:00",
"ReportName": "BalanceSheet",
"DateMacro": "this calendar year-to-date",
"ReportBasis": "Accrual",
"StartPeriod": "2021-01-01",
"EndPeriod": "2021-04-28",
"SummarizeColumnsBy": "Month",
"Currency": "USD",
"Option": [
{
"Name": "AccountingStandard",
"Value": "GAAP"
},
{
"Name": "NoReportData",
"Value": "false"
}
]
},
"Columns": {
"Column": [
{
"ColTitle": "",
"ColType": "Account",
"MetaData": [
{
"Name": "ColKey",
"Value": "account"
}
]
},
{
"ColTitle": "Jan 2021",
"ColType": "Money",
"MetaData": [
{
"Name": "StartDate",
"Value": "2021-01-01"
},
{
"Name": "EndDate",
"Value": "2021-01-31"
},
{
"Name": "ColKey",
"Value": "Jan 2021"
}
]
},
{
"ColTitle": "Feb 2021",
"ColType": "Money",
"MetaData": [
{
"Name": "StartDate",
"Value": "2021-02-01"
},
{
"Name": "EndDate",
"Value": "2021-02-28"
},
{
"Name": "ColKey",
"Value": "Feb 2021"
}
]
},
{
"ColTitle": "Mar 2021",
"ColType": "Money",
"MetaData": [
{
"Name": "StartDate",
"Value": "2021-03-01"
},
{
"Name": "EndDate",
"Value": "2021-03-31"
},
{
"Name": "ColKey",
"Value": "Mar 2021"
}
]
},
{
"ColTitle": "Apr 1-28, 2021",
"ColType": "Money",
"MetaData": [
{
"Name": "StartDate",
"Value": "2021-04-01"
},
{
"Name": "EndDate",
"Value": "2021-04-28"
},
{
"Name": "ColKey",
"Value": "Apr 1-28, 2021"
}
]
}
]
},
"Rows": {
"Row": [
{
"Header": {
"ColData": [
{
"value": "ASSETS"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"Header": {
"ColData": [
{
"value": "Current Assets"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"Header": {
"ColData": [
{
"value": "Bank Accounts"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"ColData": [
{
"value": "Checking",
"id": "35"
},
{
"value": "1201.00"
},
{
"value": "1201.00"
},
{
"value": "1201.00"
},
{
"value": "1201.00"
}
],
"type": "Data"
},
{
"ColData": [
{
"value": "Savings",
"id": "36"
},
{
"value": "800.00"
},
{
"value": "800.00"
},
{
"value": "800.00"
},
{
"value": "800.00"
}
],
"type": "Data"
}
]
},
"Summary": {
"ColData": [
{
"value": "Total Bank Accounts"
},
{
"value": "2001.00"
},
{
"value": "2001.00"
},
{
"value": "2001.00"
},
{
"value": "2001.00"
}
]
},
"type": "Section",
"group": "BankAccounts"
},
{
"Header": {
"ColData": [
{
"value": "Accounts Receivable"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"ColData": [
{
"value": "Accounts Receivable (A/R)",
"id": "84"
},
{
"value": "5281.52"
},
{
"value": "5281.52"
},
{
"value": "5281.52"
},
{
"value": "5281.52"
}
],
"type": "Data"
}
]
},
"Summary": {
"ColData": [
{
"value": "Total Accounts Receivable"
},
{
"value": "5281.52"
},
{
"value": "5281.52"
},
{
"value": "5281.52"
},
{
"value": "5281.52"
}
]
},
"type": "Section",
"group": "AR"
},
{
"Header": {
"ColData": [
{
"value": "Other Current Assets"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"ColData": [
{
"value": "Inventory Asset",
"id": "81"
},
{
"value": "596.25"
},
{
"value": "596.25"
},
{
"value": "596.25"
},
{
"value": "596.25"
}
],
"type": "Data"
},
{
"ColData": [
{
"value": "Undeposited Funds",
"id": "4"
},
{
"value": "2062.52"
},
{
"value": "2062.52"
},
{
"value": "2062.52"
},
{
"value": "2062.52"
}
],
"type": "Data"
}
]
},
"Summary": {
"ColData": [
{
"value": "Total Other Current Assets"
},
{
"value": "2658.77"
},
{
"value": "2658.77"
},
{
"value": "2658.77"
},
{
"value": "2658.77"
}
]
},
"type": "Section",
"group": "OtherCurrentAssets"
}
]
},
"Summary": {
"ColData": [
{
"value": "Total Current Assets"
},
{
"value": "9941.29"
},
{
"value": "9941.29"
},
{
"value": "9941.29"
},
{
"value": "9941.29"
}
]
},
"type": "Section",
"group": "CurrentAssets"
},
{
"Header": {
"ColData": [
{
"value": "Fixed Assets"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"Header": {
"ColData": [
{
"value": "Truck",
"id": "37"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"ColData": [
{
"value": "Original Cost",
"id": "38"
},
{
"value": "13495.00"
},
{
"value": "13495.00"
},
{
"value": "13495.00"
},
{
"value": "13495.00"
}
],
"type": "Data"
}
]
},
"Summary": {
"ColData": [
{
"value": "Total Truck"
},
{
"value": "13495.00"
},
{
"value": "13495.00"
},
{
"value": "13495.00"
},
{
"value": "13495.00"
}
]
},
"type": "Section"
}
]
},
"Summary": {
"ColData": [
{
"value": "Total Fixed Assets"
},
{
"value": "13495.00"
},
{
"value": "13495.00"
},
{
"value": "13495.00"
},
{
"value": "13495.00"
}
]
},
"type": "Section",
"group": "FixedAssets"
}
]
},
"Summary": {
"ColData": [
{
"value": "TOTAL ASSETS"
},
{
"value": "23436.29"
},
{
"value": "23436.29"
},
{
"value": "23436.29"
},
{
"value": "23436.29"
}
]
},
"type": "Section",
"group": "TotalAssets"
},
{
"Header": {
"ColData": [
{
"value": "LIABILITIES AND EQUITY"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"Header": {
"ColData": [
{
"value": "Liabilities"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"Header": {
"ColData": [
{
"value": "Current Liabilities"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"Header": {
"ColData": [
{
"value": "Accounts Payable"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"ColData": [
{
"value": "Accounts Payable (A/P)",
"id": "33"
},
{
"value": "1602.67"
},
{
"value": "1602.67"
},
{
"value": "1602.67"
},
{
"value": "1602.67"
}
],
"type": "Data"
}
]
},
"Summary": {
"ColData": [
{
"value": "Total Accounts Payable"
},
{
"value": "1602.67"
},
{
"value": "1602.67"
},
{
"value": "1602.67"
},
{
"value": "1602.67"
}
]
},
"type": "Section",
"group": "AP"
},
{
"Header": {
"ColData": [
{
"value": "Credit Cards"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"ColData": [
{
"value": "Mastercard",
"id": "41"
},
{
"value": "157.72"
},
{
"value": "157.72"
},
{
"value": "157.72"
},
{
"value": "157.72"
}
],
"type": "Data"
}
]
},
"Summary": {
"ColData": [
{
"value": "Total Credit Cards"
},
{
"value": "157.72"
},
{
"value": "157.72"
},
{
"value": "157.72"
},
{
"value": "157.72"
}
]
},
"type": "Section",
"group": "CreditCards"
},
{
"Header": {
"ColData": [
{
"value": "Other Current Liabilities"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"ColData": [
{
"value": "Arizona Dept. of Revenue Payable",
"id": "89"
},
{
"value": "0.00"
},
{
"value": "0.00"
},
{
"value": "0.00"
},
{
"value": "0.00"
}
],
"type": "Data"
},
{
"ColData": [
{
"value": "Board of Equalization Payable",
"id": "90"
},
{
"value": "370.94"
},
{
"value": "370.94"
},
{
"value": "370.94"
},
{
"value": "370.94"
}
],
"type": "Data"
},
{
"ColData": [
{
"value": "Loan Payable",
"id": "43"
},
{
"value": "4000.00"
},
{
"value": "4000.00"
},
{
"value": "4000.00"
},
{
"value": "4000.00"
}
],
"type": "Data"
}
]
},
"Summary": {
"ColData": [
{
"value": "Total Other Current Liabilities"
},
{
"value": "4370.94"
},
{
"value": "4370.94"
},
{
"value": "4370.94"
},
{
"value": "4370.94"
}
]
},
"type": "Section",
"group": "OtherCurrentLiabilities"
}
]
},
"Summary": {
"ColData": [
{
"value": "Total Current Liabilities"
},
{
"value": "6131.33"
},
{
"value": "6131.33"
},
{
"value": "6131.33"
},
{
"value": "6131.33"
}
]
},
"type": "Section",
"group": "CurrentLiabilities"
},
{
"Header": {
"ColData": [
{
"value": "Long-Term Liabilities"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"ColData": [
{
"value": "Notes Payable",
"id": "44"
},
{
"value": "25000.00"
},
{
"value": "25000.00"
},
{
"value": "25000.00"
},
{
"value": "25000.00"
}
],
"type": "Data"
}
]
},
"Summary": {
"ColData": [
{
"value": "Total Long-Term Liabilities"
},
{
"value": "25000.00"
},
{
"value": "25000.00"
},
{
"value": "25000.00"
},
{
"value": "25000.00"
}
]
},
"type": "Section",
"group": "LongTermLiabilities"
}
]
},
"Summary": {
"ColData": [
{
"value": "Total Liabilities"
},
{
"value": "31131.33"
},
{
"value": "31131.33"
},
{
"value": "31131.33"
},
{
"value": "31131.33"
}
]
},
"type": "Section",
"group": "Liabilities"
},
{
"Header": {
"ColData": [
{
"value": "Equity"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
]
},
"Rows": {
"Row": [
{
"ColData": [
{
"value": "Opening Balance Equity",
"id": "34"
},
{
"value": "-9337.50"
},
{
"value": "-9337.50"
},
{
"value": "-9337.50"
},
{
"value": "-9337.50"
}
],
"type": "Data"
},
{
"ColData": [
{
"value": "Retained Earnings",
"id": "2"
},
{
"value": "1642.46"
},
{
"value": "1642.46"
},
{
"value": "1642.46"
},
{
"value": "1642.46"
}
],
"type": "Data"
},
{
"ColData": [
{
"value": "Net Income"
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
},
{
"value": ""
}
],
"type": "Data",
"group": "NetIncome"
}
]
},
"Summary": {
"ColData": [
{
"value": "Total Equity"
},
{
"value": "-7695.04"
},
{
"value": "-7695.04"
},
{
"value": "-7695.04"
},
{
"value": "-7695.04"
}
]
},
"type": "Section",
"group": "Equity"
}
]
},
"Summary": {
"ColData": [
{
"value": "TOTAL LIABILITIES AND EQUITY"
},
{
"value": "23436.29"
},
{
"value": "23436.29"
},
{
"value": "23436.29"
},
{
"value": "23436.29"
}
]
},
"type": "Section",
"group": "TotalLiabilitiesAndEquity"
}
]
}
}

Dictionary data is not seperated into columns in Pandas DataFrame

I have created a variable that stores my json data. It looks like this:
datasett = '''
{
"data": {
"trafficRegistrationPoints": [
{
"id": "99100B1687283",
"name": "Menstad sykkeltellepunkt",
"location": {
"coordinates": {
"latLon": {
"lat": 59.173876,
"lon": 9.641772
}
}
}
},
{
"id": "11101B1800681",
"name": "Garpa - sykkel",
"location": {
"coordinates": {
"latLon": {
"lat": 63.795114,
"lon": 11.494511
}
}
}
},
{
"id": "30961B1175469",
"name": "STENMALEN-SYKKEL",
"location": {
"coordinates": {
"latLon": {
"lat": 59.27665,
"lon": 10.411814
}
}
}
},
{
"id": "53749B1700621",
"name": "TUNEVANNET SYKKEL",
"location": {
"coordinates": {
"latLon": {
"lat": 59.292846,
"lon": 11.084058
}
}
}
},
{
"id": "80565B1689290",
"name": "Nenset sykkeltellepunkt",
"location": {
"coordinates": {
"latLon": {
"lat": 59.168377,
"lon": 9.634257
}
}
}
},
{
"id": "24783B2045151",
"name": "Orstad sykkel- begge retn.",
"location": {
"coordinates": {
"latLon": {
"lat": 58.798377,
"lon": 5.72743
}
}
}
},
{
"id": "46418B2616452",
"name": "Elgeseter bru sykkel øst",
"location": {
"coordinates": {
"latLon": {
"lat": 63.425015,
"lon": 10.393928
}
}
}
},
{
"id": "35978B1700571",
"name": "Tune kirke nord",
"location": {
"coordinates": {
"latLon": {
"lat": 59.292626,
"lon": 11.084066
}
}
}
},
{
"id": "21745B1996708",
"name": "Munkedamsveien Sykkel",
"location": {
"coordinates": {
"latLon": {
"lat": 59.911198,
"lon": 10.725568
}
}
}
},
{
"id": "33443B2542097",
"name": "KANALBRUA-SYKKEL",
"location": {
"coordinates": {
"latLon": {
"lat": 59.261823,
"lon": 10.416349
}
}
}
},
{
"id": "77570B384357",
"name": "HAVRENESVEGEN (SYKKEL)",
"location": {
"coordinates": {
"latLon": {
"lat": 61.598202,
"lon": 5.016999
}
}
}
},
{
"id": "95959B971385",
"name": "JELØGATA SYKKEL",
"location": {
"coordinates": {
"latLon": {
"lat": 59.43385,
"lon": 10.65388
}
}
}
},
{
"id": "61523B971803",
"name": "ST.HANSFJELLET SYKKEL",
"location": {
"coordinates": {
"latLon": {
"lat": 59.218978,
"lon": 10.93455
}
}
}
},
}
}
}
]
}
}
'''
Next, I have used json.loads() to turn it into a dictionary in Python, using the following code:
dict = json.loads(datasett)
Because the result I get is a nested dictionary,I we want to move further into the nest.
movedDict = dict['data']
I then want to this into a Pandas DataFrame
df = pd.DataFrame.from_dict(movedDict)
However, when I print this. The data is not seperated into unique columns. What do I do wrong?

You can use json_normalize here, I also removed some extra } from your JSON:
data = json.loads(datasett)
df = pd.json_normalize(data, record_path=['data', 'trafficRegistrationPoints'])
print(df)
id name location.coordinates.latLon.lat location.coordinates.latLon.lon
0 99100B1687283 Menstad sykkeltellepunkt 59.173876 9.641772
1 11101B1800681 Garpa - sykkel 63.795114 11.494511
2 30961B1175469 STENMALEN-SYKKEL 59.276650 10.411814
3 53749B1700621 TUNEVANNET SYKKEL 59.292846 11.084058
4 80565B1689290 Nenset sykkeltellepunkt 59.168377 9.634257
5 24783B2045151 Orstad sykkel- begge retn. 58.798377 5.727430
6 46418B2616452 Elgeseter bru sykkel øst 63.425015 10.393928
7 35978B1700571 Tune kirke nord 59.292626 11.084066
8 21745B1996708 Munkedamsveien Sykkel 59.911198 10.725568
9 33443B2542097 KANALBRUA-SYKKEL 59.261823 10.416349
10 77570B384357 HAVRENESVEGEN (SYKKEL) 61.598202 5.016999
11 95959B971385 JELØGATA SYKKEL 59.433850 10.653880
12 61523B971803 ST.HANSFJELLET SYKKEL 59.218978 10.934550

when use from_dict the dict should look like this:
data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
pd.DataFrame.from_dict(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
in your case:
data = {'trafficRegistrationPoints':[.....]}
save the 'trafficRegistrationPoints' as a list and then create the dataFrame

The values for the data key in your dict are not individual dicts but rather a list of dicts under trafficRegistrationPoints key, so you need to move further into that key:
df = pd.DataFrame.from_dict(movedDict['trafficRegistrationPoints'])

Get different values from repeating item JSON

I have this json derived dict:
{
"stats": [
{
"name": "Jengas",
"time": 166,
"uid": "177098244407558145",
"id": 1
},
{
"name": "- k",
"time": 20,
"uid": "199295228664872961",
"id": 2
},
{
"name": "MAD MARX",
"time": "0",
"uid": "336539711785009153",
"id": 3
},
{
"name": "loli",
"time": 20,
"uid": "366299640976375818",
"id": 4
},
{
"name": "Woona",
"time": 20,
"uid": "246996981178695686",
"id": 5
}
]
}
I want to get the "time" from everybody in the list and use it with sort.
So the result I get has this:
TOP 10:
Jengas: 166
Loli: 20
My first try is to list different values from repeating item.
Right now the code is:
with open('db.json') as json_data:
topvjson = json.load(json_data)
print(topvjson)
d = topvjson['stats'][0]['time']
print(d)

Extract the stats list, apply sort to it with the appropriate key:
from json import loads
data = loads("""{
"stats": [{
"name": "Jengas",
"time": 166,
"uid": "177098244407558145",
"id": 1
}, {
"name": "- k",
"time": 20,
"uid": "199295228664872961",
"id": 2
}, {
"name": "MAD MARX",
"time": "0",
"uid": "336539711785009153",
"id": 3
}, {
"name": "loli",
"time": 20,
"uid": "366299640976375818",
"id": 4
}, {
"name": "Woona",
"time": 20,
"uid": "246996981178695686",
"id": 5
}]
}""")
stats = data['stats']
stats.sort(key = lambda entry: int(entry['time']), reverse=True)
print("TOP 10:")
for entry in stats[:10]:
print("%s: %d" % (entry['name'], int(entry['time'])))
This prints:
TOP 10:
Jengas: 166
- k: 20
loli: 20
Woona: 20
MAD MARX: 0
Note that your time is neither an integer nor string: there are both 0 and "0" in the dataset. That's why you need the conversion int(...).

You can sort the list of dict values like:
Code:
top_three = [(x[1], -x[0]) for x in sorted(
(-int(user['time']), user['name']) for user in stats['stats'])][:3]
This works by taking the time and the name and building a tuple. The tuples can the be sorted, and then the names can be extracted (via: x[1]) after the sort.
Test Code:
stats = {
"stats": [{
"name": "Jengas",
"time": 166,
"uid": "177098244407558145",
"id": 1
}, {
"name": "- k",
"time": 20,
"uid": "199295228664872961",
"id": 2
}, {
"name": "MAD MARX",
"time": "0",
"uid": "336539711785009153",
"id": 3
}, {
"name": "loli",
"time": 20,
"uid": "366299640976375818",
"id": 4
}, {
"name": "Woona",
"time": 20,
"uid": "246996981178695686",
"id": 5
}]
}
top_three = [x[1] for x in sorted(
(-int(user['time']), user['name']) for user in stats['stats'])][:3]
print(top_three)
Results:
[('Jengas', 166), ('- k', 20), ('Woona', 20)]

Here's a way to do it using the built-in sorted() function:
data = {
"stats": [
{
"name": "Jengas",
"time": 166,
"uid": "177098244407558145",
"id": 1
},
{
etc ...
}
]
}
print('TOP 3')
sorted_by_time = sorted(data['stats'], key=lambda d: int(d['time']), reverse=True)
for i, d in enumerate(sorted_by_time, 1):
if i > 3: break
print('{name}: {time}'.format(**d))
Output:
TOP 3
Jengas: 166
- k: 20
loli: 20

Combine 2 JSON files into 1 file in Node or Python (i.e. longitude and latitude)

I want to append the longitude to a latitude stored in 2 separated json files
The result should be stored in a 3rd file
How can I do that on Python OR Javascript/Node?
Many thanks for your support,
LATITUDE
{
"tags": [{
"name": "LATITUDE_deg",
"results": [{
"groups": [{
"name": "type",
"type": "number"
}],
"values": [
[1123306773000, 46.9976859318, 3],
[1123306774000, 46.9976859319, 3]
],
"attributes": {
"customer": ["Acme"],
"host": ["server1"]
}
}],
"stats": {
"rawCount": 2
}
}]
}
LONGITUDE
{
"tags": [{
"name": "LONGITUDE_deg",
"results": [{
"groups": [{
"name": "type",
"type": "number"
}],
"values": [
[1123306773000, 36.9976859318, 3],
[1123306774000, 36.9976859317, 3]
],
"attributes": {
"customer": ["Acme"],
"host": ["server1"]
}
}],
"stats": {
"rawCount": 2
}
}]
}
Expected result: LATITUDE_AND_LONGITUDE
{
"tags": [{
"name": "LATITUDE_AND_LONGITUDE_deg",
"results": [{
"groups": [{
"name": "type",
"type": "number"
}],
"values": [
[1123306773000, 46.9976859318, 36.9976859318, 3],
[1123306774000, 46.9976859319, 36.9976859317, 3]
],
"attributes": {
"customer": ["Acme"],
"host": ["server1"]
}
}],
"stats": {
"rawCount": 2
}
}]
}

I have written the solution with a colleague, find the source code on github: https://gist.github.com/Abdelkrim/715eb222cc318219196c8be293c233bf