merge complex list of nested dicts - python

I'm trying to merge nested Dicts in a list based on "name" like the following:
[
{
"name": "abc",
"metadata": [
{
"name": "foo",
"data": [
{
"version": "1.0"
}
]
},
{
"name": "foo",
"data": [
{
"version": "2.0"
}
]
},
{
"name": "bar",
"data": [
{
"version": "1.0"
}
]
}
]
},
{
"name": "xyz",
"metadata": [
{
"name": "bob",
"data": [
{
"version": "3.2"
}
]
},
{
"name": "alice",
"data": [
{
"version": "2.2"
}
]
}
]
},
{
"name": "xyz",
"metadata": [
{
"name": "mike",
"data": [
{
"version": "3.2"
}
]
},
{
"name": "alice",
"data": [
{
"version": "2.2"
}
]
}
]
}
]
Considering that the merged items should not have duplicates in the metadata, how can I do that in Python? Metadata entries should be unique, if name+data+version exist in the metadata, then the item should not be merged.
my desired output should look like this
[
{
"name": "abc",
"metadata": [
{
"name": "foo",
"data": [
{
"version": "1.0"
}
]
},
{
"name": "foo",
"data": [
{
"version": "2.0"
}
]
},
{
"name": "bar",
"data": [
{
"version": "1.0"
}
]
}
]
},
{
"name": "xyz",
"metadata": [
{
"name": "bob",
"data": [
{
"version": "3.2"
}
]
},
{
"name": "mike",
"data": [
{
"version": "3.2"
}
]
},
{
"name": "alice",
"data": [
{
"version": "2.2"
}
]
}
]
}
]


You can use itertools.groubpy:
import itertools
d = [{'name': 'abc', 'metadata': [{'name': 'foo', 'data': [{'version': '1.0'}]}, {'name': 'foo', 'data': [{'version': '2.0'}]}, {'name': 'bar', 'data': [{'version': '1.0'}]}]}, {'name': 'xyz', 'metadata': [{'name': 'bob', 'data': [{'version': '3.2'}]}, {'name': 'alice', 'data': [{'version': '2.2'}]}]}, {'name': 'xyz', 'metadata': [{'name': 'mike', 'data': [{'version': '3.2'}]}, {'name': 'alice', 'data': [{'version': '2.2'}]}]}]
new_d = [[a, list(b)] for a, b in itertools.groupby(sorted(d, key=lambda x:x['name']), key=lambda x:x['name'])]
result = [{'name':a, 'metadata':[c for j in b for c in j['metadata']]} for a, b in new_d]
final_result = [{**i, 'metadata':[c for d, c in enumerate(i['metadata']) if all(a != c for a in i['metadata'][:d])]} for i in result]
import json
print(json.dumps(final_result, indent=4))
Output:
[
{
"name": "abc",
"metadata": [
{
"name": "foo",
"data": [
{
"version": "1.0"
}
]
},
{
"name": "foo",
"data": [
{
"version": "2.0"
}
]
},
{
"name": "bar",
"data": [
{
"version": "1.0"
}
]
}
]
},
{
"name": "xyz",
"metadata": [
{
"name": "bob",
"data": [
{
"version": "3.2"
}
]
},
{
"name": "alice",
"data": [
{
"version": "2.2"
}
]
},
{
"name": "mike",
"data": [
{
"version": "3.2"
}
]
}
]
}
]

Related

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"
}
]
}
}

How to merge two DSL query for aggregation and filter

I need to search BusinessArea which is Research or Accounting this is array of fields(OR) statement
I need to search Role is Developer or Tester condition this is array of fields(OR) statement
I want to get the count of masterid of BusinessArea, designationNames, Role which is all the names
Name filter is "Group1"
Below is the dictionary
test= [ { 'masterid': '1', 'name': 'Group1', 'BusinessArea': [ 'Accounting','Research'], 'Designation': [ 'L1' 'L2' ] }, { 'masterid': '2', 'name': 'Group1', 'BusinessArea': ['Research','Accounting' ], 'Role': [ { 'id': '5032', 'name': 'Tester' }, { 'id': '5033', 'name': 'Developer' } ], 'Designation': [ 'L1' 'L2' ]}, { 'masterid': '3', 'name': 'Group1', 'BusinessArea': [ 'Engineering' ], 'Role': [ { 'id': '5032', 'name': 'Developer' }, { 'id': '5033', 'name': 'Developer', 'parentname': '' } ], 'Designation': [ 'L1' 'L2' ]}]
Below is the aggregation function
{
"size": 0,
"aggs": {
"countNames": {
"terms": {
"field": "BusinessArea.keyword"
}
},
"designationNames": {
"terms": {
"field": "Designation.keyword"
}
},
"Role": {
"terms": {
"field": "Role.name.keyword"
}
}
}
}
Below is the filtering query
{
"query": {
"bool": {
"must": [
{
"terms": {
"BusinessArea.keyword": [
"Research",
"Accounting"
]
}
},
{
"terms": {
"Role.name.keyword": [
"Developer",
"Tester"
]
}
}
]
}
}
}
"filter": [
"term": {
"name.keyword": "Group1"}]
I need to merge both query and output will be having from the both

Nice start !!! Now you can simply combine all those snippets like this:
{
"size": 0,
"query": {
"bool": {
"filter": [
{
"term": {
"name.keyword": "Group1"
}
},
{
"terms": {
"BusinessArea.keyword": [
"Research",
"Accounting"
]
}
},
{
"terms": {
"Role.name.keyword": [
"Developer",
"Tester"
]
}
}
]
}
},
"aggs": {
"countNames": {
"terms": {
"field": "BusinessArea.keyword"
}
},
"designationNames": {
"terms": {
"field": "Designation.keyword"
}
},
"Role": {
"terms": {
"field": "Role.name.keyword"
}
}
}
}

Merge lists of complex dicts with arbitrary keys

I have this code:
dotteds = ["apple.orange.banana", "a.b.c", "a.b.d"]
name = "name"
avtype = "type"
fields = "fields"
main_dictionary_list = []
for x in dotteds:
split_name = x.split('.')
if len(split_name) > 1:
value = {name: split_name[-1], avtype: 'string'}
dicts = []
for y in split_name:
dicts.append({name: y, avtype: {name: y, avtype: "record", fields: []}})
dicts[-1] = value
value = value['name']+split_name[-1]
for z in reversed(range(len(dicts))):
if z != 0:
dicts[z - 1]['type']['fields'].append(dicts[z])
main_dictionary_list.append(dicts[0])
else:
dicts = []
value = {name: split_name[-1], avtype: 'string'}
dicts.append(value)
main_dictionary_list.append(dicts[0])
print(main_dictionary_list)
Which gives me an output like this:
[{
'name': 'apple',
'type': {
'name': 'apple',
'type': 'record',
'fields': [{
'name': 'orange',
'type': {
'name': 'orange',
'type': 'record',
'fields': [{
'name': 'banana',
'type': 'string'
}
]
}
}
]
}
}, {
'name': 'a',
'type': {
'name': 'a',
'type': 'record',
'fields': [{
'name': 'b',
'type': {
'name': 'b',
'type': 'record',
'fields': [{
'name': 'c',
'type': 'string'
}
]
}
}
]
}
}, {
'name': 'a',
'type': {
'name': 'a',
'type': 'record',
'fields': [{
'name': 'b',
'type': {
'name': 'b',
'type': 'record',
'fields': [{
'name': 'd',
'type': 'string'
}
]
}
}
]
}
}
]
Ideally I need:
[{
'name': 'apple',
'type': {
'name': 'apple',
'type': 'record',
'fields': [{
'name': 'orange',
'type': {
'name': 'orange',
'type': 'record',
'fields': [{
'name': 'banana',
'type': 'string'
}
]
}
}
]
}
}, {
'name': 'a',
'type': {
'name': 'a',
'type': 'record',
'fields': [{
'name': 'b',
'type': {
'name': 'b',
'type': 'record',
'fields': [{
'name': 'c',
'type': 'string'
},
{
'name': 'd',
'type': 'string'
}
]
}
}
]
}
}
]
I need to be able to do this with any number of combinations:
apple.orange.banana, a.b.c, a.b.d, a.b.q.e.a.s.d, etc.
I cannot figure out how to combine the similar 'name: key' combinations. It's intended to be avro format.
I have also tried making the dotted values into a dictionary which is a bit of trouble on its own.

You can use recursion with collections.defaultdict:
from collections import defaultdict
def group(vals, last=None):
if any(len(i) == 1 for i in vals):
return [{'name':last, 'type':{'name':last, 'type':'record', 'fields':[{'name':i[0], 'type':'string'} if len(i) == 1 else group([i], i[0])[0] for i in vals]}}]
_d = defaultdict(list)
for i in vals:
_d[i[0]].append(i[1:])
return [{'name':a, 'type':group(b, last=a)} if last is None else
{'name':last, 'type':'record', 'fields':group(b, last=a)} for a, b in _d.items()]
import json
vals = ['apple.orange.banana', 'a.b.c', 'a.b.d']
print(json.dumps(group([i.split('.') for i in vals]), indent=4))
Output:
[
{
"name": "apple",
"type": [
{
"name": "apple",
"type": "record",
"fields": [
{
"name": "orange",
"type": {
"name": "orange",
"type": "record",
"fields": [
{
"name": "banana",
"type": "string"
}
]
}
}
]
}
]
},
{
"name": "a",
"type": [
{
"name": "a",
"type": "record",
"fields": [
{
"name": "b",
"type": {
"name": "b",
"type": "record",
"fields": [
{
"name": "c",
"type": "string"
},
{
"name": "d",
"type": "string"
}
]
}
}
]
}
]
}
]
vals = ['asd.2', 'asd.3', 'asd.5.3.4']
print(json.dumps(group([i.split('.') for i in vals]), indent=4))
Output:
[
{
"name": "asd",
"type": [
{
"name": "asd",
"type": {
"name": "asd",
"type": "record",
"fields": [
{
"name": "2",
"type": "string"
},
{
"name": "3",
"type": "string"
},
{
"name": "5",
"type": "record",
"fields": [
{
"name": "5",
"type": "record",
"fields": [
{
"name": "3",
"type": {
"name": "3",
"type": "record",
"fields": [
{
"name": "4",
"type": "string"
}
]
}
}
]
}
]
}
]
}
}
]
}
]

Adding new pairs to a json file

I have a json file I need to add pairs to, I convert it into a dict, but now I need to put my new values in a specific place.
This is some of the json file I convert:
"rootObject": {
"id": "6ff0010c-00fe-485b-b695-4ddd6aca4dcd",
"type": "IDO_GEAR",
"children": [
{
"id": "1dd94d1a-e52d-40b3-a82b-6db02a8fbbab",
"type": "IDO_SYSTEM_LOADCASE",
"children": [],
"childList": "SYSTEMLOADCASE",
"properties": [
{
"name": "IDCO_IDENTIFICATION",
"value": "1dd94d1a-e52d-40b3-a82b-6db02a8fbbab"
},
{
"name": "IDCO_DESIGNATION",
"value": "Lastfall 1"
},
{
"name": "IDSLC_TIME_PORTION",
"value": 100
},
{
"name": "IDSLC_DISTANCE_PORTION",
"value": 100
},
{
"name": "IDSLC_OPERATING_TIME_IN_HOURS",
"value": 1
},
{
"name": "IDSLC_OPERATING_TIME_IN_SECONDS",
"value": 3600
},
{
"name": "IDSLC_OPERATING_REVOLUTIONS",
"value": 1
},
{
"name": "IDSLC_OPERATING_DISTANCE",
"value": 1
},
{
"name": "IDSLC_ACCELERATION",
"value": 9.81
},
{
"name": "IDSLC_EPSILON_X",
"value": 0
},
{
"name": "IDSLC_EPSILON_Y",
"value": 0
},
{
"name": "IDSLC_EPSILON_Z",
"value": 0
},
{
"name": "IDSLC_CALCULATION_WITH_OWN_WEIGHT",
"value": "CO_CALCULATION_WITHOUT_OWN_WEIGHT"
},
{
"name": "IDSLC_CALCULATION_WITH_TEMPERATURE",
"value": "CO_CALCULATION_WITH_TEMPERATURE"
},
{
"name": "IDSLC_FLAG_FOR_LOADCASE_CALCULATION",
"value": "LB_CALCULATE_LOADCASE"
},
{
"name": "IDSLC_STATUS_OF_LOADCASE_CALCULATION",
"value": false
}
I want to add somthing like ENTRY_ONE and ENTRY_TWO like this:
"rootObject": {
"id": "6ff0010c-00fe-485b-b695-4ddd6aca4dcd",
"type": "IDO_GEAR",
"children": [
{
"id": "1dd94d1a-e52d-40b3-a82b-6db02a8fbbab",
"type": "IDO_SYSTEM_LOADCASE",
"children": [],
"childList": "SYSTEMLOADCASE",
"properties": [
{
"name": "IDCO_IDENTIFICATION",
"value": "1dd94d1a-e52d-40b3-a82b-6db02a8fbbab"
},
{
"name": "IDCO_DESIGNATION",
"value": "Lastfall 1"
},
{
"name": "IDSLC_TIME_PORTION",
"value": 100
},
{
"name": "IDSLC_DISTANCE_PORTION",
"value": 100
},
{
"name": "ENTRY_ONE",
"value": 100
},
{
"name": "ENTRY_TWO",
"value": 55
},
{
"name": "IDSLC_OPERATING_TIME_IN_HOURS",
"value": 1
},
{
"name": "IDSLC_OPERATING_TIME_IN_SECONDS",
"value": 3600
},
{
"name": "IDSLC_OPERATING_REVOLUTIONS",
"value": 1
},
{
"name": "IDSLC_OPERATING_DISTANCE",
"value": 1
},
{
"name": "IDSLC_ACCELERATION",
"value": 9.81
},
{
"name": "IDSLC_EPSILON_X",
"value": 0
},
{
"name": "IDSLC_EPSILON_Y",
"value": 0
},
{
"name": "IDSLC_EPSILON_Z",
"value": 0
},
{
"name": "IDSLC_CALCULATION_WITH_OWN_WEIGHT",
"value": "CO_CALCULATION_WITHOUT_OWN_WEIGHT"
},
{
"name": "IDSLC_CALCULATION_WITH_TEMPERATURE",
"value": "CO_CALCULATION_WITH_TEMPERATURE"
},
{
"name": "IDSLC_FLAG_FOR_LOADCASE_CALCULATION",
"value": "LB_CALCULATE_LOADCASE"
},
{
"name": "IDSLC_STATUS_OF_LOADCASE_CALCULATION",
"value": false
}
How can I add the entries so that they are under the IDCO_IDENTIFICATION tag, and not under the rootObject?

The way I see your json file, it WOULD be under rootObject as EVERYTHING is under that key. There's quite a few closing brackets and braces missing.
So I can only assume you are meaning you want it directly under IDCO_IDENTIFICATION (which is nested under rootObject). But that doesn't match what you have as your example output either. You have the new ENTRY_ONE and ENTRY_TWO within the properties, within the children, within the rootObject, not "under" IDCO_IDENTIFICATION. So I'm going to follow what you are asking for from your example output.
import json
with open('C:/test.json') as f:
data = json.load(f)
new_elements = [{"name":"ENTRY_ONE", "value":100},
{"name":"ENTRY_TWO", "value":55}]
for each in new_elements:
data['rootObject']['children'][0]['properties'].append(each)
with open('C:/test.json', 'w') as f:
json.dump(data, f)
Output:
import pprint
pprint.pprint(data)
{'rootObject': {'children': [{'childList': 'SYSTEMLOADCASE',
'children': [],
'id': '1dd94d1a-e52d-40b3-a82b-6db02a8fbbab',
'properties': [{'name': 'IDCO_IDENTIFICATION',
'value': '1dd94d1a-e52d-40b3-a82b-6db02a8fbbab'},
{'name': 'IDCO_DESIGNATION',
'value': 'Lastfall 1'},
{'name': 'IDSLC_TIME_PORTION',
'value': 100},
{'name': 'IDSLC_DISTANCE_PORTION',
'value': 100},
{'name': 'IDSLC_OPERATING_TIME_IN_HOURS',
'value': 1},
{'name': 'IDSLC_OPERATING_TIME_IN_SECONDS',
'value': 3600},
{'name': 'IDSLC_OPERATING_REVOLUTIONS',
'value': 1},
{'name': 'IDSLC_OPERATING_DISTANCE',
'value': 1},
{'name': 'IDSLC_ACCELERATION',
'value': 9.81},
{'name': 'IDSLC_EPSILON_X',
'value': 0},
{'name': 'IDSLC_EPSILON_Y',
'value': 0},
{'name': 'IDSLC_EPSILON_Z',
'value': 0},
{'name': 'IDSLC_CALCULATION_WITH_OWN_WEIGHT',
'value': 'CO_CALCULATION_WITHOUT_OWN_WEIGHT'},
{'name': 'IDSLC_CALCULATION_WITH_TEMPERATURE',
'value': 'CO_CALCULATION_WITH_TEMPERATURE'},
{'name': 'IDSLC_FLAG_FOR_LOADCASE_CALCULATION',
'value': 'LB_CALCULATE_LOADCASE'},
{'name': 'IDSLC_STATUS_OF_LOADCASE_CALCULATION',
'value': False},
{'name': 'ENTRY_ONE',
'value': 100},
{'name': 'ENTRY_TWO',
'value': 55}],
'type': 'IDO_SYSTEM_LOADCASE'}],
'id': '6ff0010c-00fe-485b-b695-4ddd6aca4dcd',
'type': 'IDO_GEAR'}}

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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