How to get data from nested list in response.json() - python

There is a json response from an API request in the following schema:
[
{
"id": "1",
"variable": "x",
"unt": "%",
"results": [
{
"classification": [
{
"id": "1",
"name": "group",
"category": {
"555": "general"
}
}
],
"series": [
{
"location": {
"id": "1",
"level": {
"id": "n1",
"name": "z"
},
"name": "z"
},
"serie": {
"202001": "0.08",
"202002": "0.48",
"202003": "0.19"
}
}
]
}
]
}
]
I want to transform the data from the "serie" key into a pandas DataFrame.
I can do that explicitly:
content = val[0]["results"][0]["series"][0]["serie"]
df = pd.DataFrame(content.items())
df
0 1
0 202001 0.08
1 202002 0.48
2 202003 0.19
But if there is more than one record, that would get only the data from the first element because of the positional arguments [0].
Is there a way to retrieve that data not considering the positional arguments?


Try:
val = [
{
"id": "1",
"variable": "x",
"unt": "%",
"results": [
{
"classification": [
{"id": "1", "name": "group", "category": {"555": "general"}}
],
"series": [
{
"location": {
"id": "1",
"level": {"id": "n1", "name": "z"},
"name": "z",
},
"serie": {"202001": "0.08", "202002": "0.48", "202003": "0.19"},
}
],
}
],
},
{
"id": "2",
"variable": "x",
"unt": "%",
"results": [
{
"classification": [
{"id": "1", "name": "group", "category": {"555": "general"}}
],
"series": [
{
"location": {
"id": "1",
"level": {"id": "n1", "name": "z"},
"name": "z",
},
"serie": {"202001": "1.08", "202002": "1.48", "202003": "1.19"},
}
],
}
],
},
]
df = pd.DataFrame(
[k, v]
for i in val
for ii in i["results"]
for s in ii["series"]
for k, v in s["serie"].items()
)
print(df)
Prints:
0 1
0 202001 0.08
1 202002 0.48
2 202003 0.19
3 202001 1.08
4 202002 1.48
5 202003 1.19

Related

Extract data from JSON index loaded file

My JSON file looks like:
{
"numAccounts": xxxx,
"filtersApplied": {
"accountIds": "All",
"checkIds": "All",
"categories": [
"cost_optimizing"
],
"statuses": "All",
"regions": "All",
"organizationalUnitIds": [
"yyyyy"
]
},
"categoryStatusMap": {
"cost_optimizing": {
"statusMap": {
"RULE_ERROR": {
"name": "Blue",
"count": 11
},
"ERROR": {
"name": "Red",
"count": 11
},
"OK": {
"name": "Green",
"count": 11
},
"WARN": {
"name": "Yellow",
"count": 11
}
},
"name": "Cost Optimizing",
"monthlySavings": 1111
}
},
"accountStatusMap": {
"xxxxxxxx": {
"cost_optimizing": {
"statusMap": {
"OK": {
"name": "Green",
"count": 1111
},
"WARN": {
"name": "Yellow",
"count": 111
}
},
"name": "Cost Optimizing",
"monthlySavings": 1111
}
},
Which I load into memory using pandas:
df = pd.read_json('file.json', orient='index')
I find the index orient the most suitable because it gives me:
print(df)
0
numAccounts 125
filtersApplied {'accountIds': 'All', 'checkIds': 'All', 'cate...
categoryStatusMap {'cost_optimizing': {'statusMap': {'RULE_ERROR...
accountStatusMap {'xxxxxxx': {'cost_optimizing': {'statusM...
Now, how can I access the accountStatusMap entry?
I tried account_status_map = df['accountStatusMap'] which gives me a
KeyError: 'accountStatusMap'
Is there something specific to the index orientation in how to access specific entries in a dataframe?

How to group child objects in python list

I have the parent child structure like following which is generated from the database entries by iterating rows data and append childrens
"path_data": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": 1,
"key": "0",
"name": "Home"
},
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": 2,
"key": "0",
"name": "About Us"
}
],
"count": 1,
"id": 1,
"key": "0",
"name": "Home"
},
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PRODUCT_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [],
"count": 1,
"id": 1,
"key": "0",
"name": "Home"
}
],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": 2,
"key": "0",
"name": "About Us"
}
],
"count": 1,
"id": 1,
"key": "0",
"name": "Home"
},
{
"action": "PAGE_VIEW",
"children": [
[]
],
"count": 1,
"id": 1,
"key": "0",
"name": "Home"
}
],
Expected output need to convert this by grouping the same level child & increase count of it like following.
"path_data": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
},{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [
{
"action": "PAGE_VIEW",
"children": [],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 1,
"id": "",
"key": "0",
"name": "Untagged"
}, {
"action": "PAGE_VIEW",
"children": [],
"count": 1,
"id": 1,
"key": "0",
"name": "Home"
}
],
"count": 2,
"id": "",
"key": "0",
"name": "Untagged"
}
],
"count": 2,
"id": 2,
"key": "0",
"name": "About Us"
}
],
"count": 4,
"id": 1,
"key": "0",
"name": "Home"
},
],
Is there any inbuilt functions or library to do this?
This code is written in python 3.8

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"
}
]
}
}

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

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