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How can I transpose a list of documents in MongoDB?

I have a document like:
{
"_id": "6345e01473144cec0073ea95",
"results": [
{"total_cost": 10, "total_time": 20},
{"total_cost": 30, "total_time": 40}
]
}
And I want to 'transpose' the list of documents to get:
{
"total_cost": [10, 30],
"total_time": [20, 40]
}
How can I find an object by ID, and then apply a transform on a list of documents with a mongo aggregation?
Every question/answer I have seen doing this has been for multiple documents, however this is for a single document with a list field.
(I am using MongoEngine/Pymongo so I have included the python tag)

simply access the fields with dot notation. You can think the projection of the fields as an individual array, i.e. results.total_cost is an array with content [10, 30]
db.collection.aggregate([
{
$project: {
total_cost: "$results.total_cost",
total_time: "$results.total_time"
}
}
])
Here is the Mongo Playground with your reference.

Google Sheets Python batchUpdate repeatCell -> issue with range and number format

I am trying to use the google sheets api for python to format only a specific columns results to a "NUMBER" type but am struggling to get it to work properly. Am I doing something wrong with the "range" block? There are values that are getting appended to the column and when they get appended (via a different api set) they do not come back as formatted numbers that, when highlighting the entire column, result in a numbered sum.
id_sampleforstackoverflow = 'abcdefg123xidjadsfh192810'
cost_sav_body = {
"requests": [
{
"repeatCell": {
"range": {
"sheetId": 0,
"startRowIndex": 2,
"endRowIndex": 6,
"startColumnIndex": 0,
"endColumnIndex": 6
},
"cell": {
"userEnteredFormat": {
"numberFormat": {
"type": "NUMBER",
"pattern": "#.0#;#.0#"
}
}
},
"fields": "userEnteredFormat.numberFormat"
}
}
]
}
cost_sav_sum = service.spreadsheets().batchUpdate(spreadsheetId=id_sampleforstackoverflow, body=cost_sav_body).execute()
So when I run the above with the rest of my code, the values get appended, however, when highlighting the column, it simply gives me a count of the objects, and not a formatted number summing the total of the values (i.e. there are three values of -24, but only see a "Count" of 3 instead of -72).
I am using the GCP recommendations api for machineType to append the cost projection -> costs -> units value to the column (they append for example like i.e. -24).
Can someone help?
Documentation I have already gone through:
https://cloud.google.com/blog/products/application-development/formatting-cells-with-the-google-sheets-api
https://developers.google.com/sheets/api/guides/formats
https://developers.google.com/sheets/api/reference/rest/v4/spreadsheets/other#GridRange

#all
I was able to figure out the problem. When doing straight reporting of the values for the cost (as explained above as an objective) I was converting the output to string using the str() python method. I removed that str() method and kept the rest of the code you see above and now things are posting correctly:
#spend = str(element.primary_impact.cost_projection.cost.units)
spend = element.primary_impact.cost_projection.cost.units
So FYI for anyone else wondering, make sure that str() method is not used if you need to do a custom formatting code to those particular cells!

Converting csv to nested Json using python

I want to convert csv file to json file.
I have large data in csv file.
CSV Column Structure
This is my column structure in csv file . I has 200+ records.
id.oid libId personalinfo.Name personalinfo.Roll_NO personalinfo.addr personalinfo.marks.maths personalinfo.marks.physic clginfo.clgName clginfo.clgAddr clginfo.haveCert clginfo.certNo clginfo.certificates.cert_name_1 clginfo.certificates.cert_no_1 clginfo.certificates.cert_exp_1 clginfo.certificates.cert_name_2 clginfo.certificates.cert_no_2 clginfo.certificates.cert_exp_2 clginfo.isDept clginfo.NoofDept clginfo.DeptDetails.DeptName_1 clginfo.DeptDetails.location_1 clginfo.DeptDetails.establish_date_1 _v updatedAt.date
Expected Json
[{
"id":
{
"$oid": "00001"
},
"libId":11111,
"personalinfo":
{
"Name":"xyz",
"Roll_NO":101,
"addr":"aa bb cc ddd",
"marks":
[
"maths":80,
"physic":90
.....
]
},
"clginfo"
{
"clgName":"pqr",
"clgAddr":"qwerty",
"haveCert":true, //this is boolean true or false
"certNo":1, //this could be 1-10
"certificates":
[
{
"cert_name_1":"xxx",
"cert_no_1":12345,
"cert_exp.1":"20/2/20202"
},
{
"cert_name_2":"xxx",
"cert_no_2":12345,
"cert_exp_2":"20/2/20202"
},
......//could be up to 10
],
"isDept":true, //this is boolean true or false
"NoofDept":1 , //this could be 1-10
"DeptDetails":
[
{
"DeptName_1":"yyy",
"location_1":"zzz",
"establish_date_1":"1/1/1919"
},
......//up to 10 records
]
},
"__v": 1,
"updatedAt":
{
"$date": "2022-02-02T13:35:59.843Z"
}
}]
I have tried using pandas but I'm getting output as
My output
[{
"id.$oid": "00001",
"libId":11111,
"personalinfo.Name":"xyz",
"personalinfo.Roll_NO":101,
"personalinfo.addr":"aa bb cc ddd",
"personalinfo.marks.maths":80,
"personalinfo.marks.physic":90,
"clginfo.clgName":"pqr",
"clginfo.clgAddr":"qwerty",
"clginfo.haveCert":true,
"clginfo.certNo":1,
"clginfo.certificates.cert_name_1":"xxx",
"clginfo.certificates.cert_no_1":12345,
"clginfo.certificates.cert_exp.1":"20/2/20202"
"clginfo.certificates.cert_name_2":"xxx",
"clginfo.certificates.cert_no_2":12345,
"clginfo.certificates.cert_exp_2":"20/2/20202"
"clginfo.isDept":true,
"clginfo.NoofDept":1 ,
"clginfo.DeptDetails.DeptName_1":"yyy",
"clginfo.DeptDetails.location_1":"zzz",
"eclginfo.DeptDetails.stablish_date_1":"1/1/1919",
"__v": 1,
"updatedAt.$date": "2022-02-02T13:35:59.843Z",
}]
I am new to python I only know the basic Please help me getting this output.

200+ records is really tiny, so even naive solution is good.
It can't be totally generic because I don't see how it can be seen from the headers that certificates is a list, unless we rely on all names under certificates having _N at the end.
Proposed solution using only basic python:
read header row - split all column names by period. Iterate over resulting list and create nested dicts with appropriate keys and dummy values (if you want to handle lists: create array if current key ends with _N and use N as an index)
for all rows:
clone dictionary with dummy values
for each column use split keys from above to put the value into the corresponding dict. same solution from above for lists.
append the dictionary to list of rows

how do you convert json output to a data frame in python

I need to convert this json file to a data frame in python:
print(resp2)
{
"totalCount": 1,
"nextPageKey": null,
"result": [
{
"metricId": "builtin:tech.generic.cpu.usage",
"data": [
{
"dimensions": [
"process_345678"
],
"dimensionMap": {
"dt.entity.process_group_instance": "process_345678"
},
"timestamps": [
1642021200000,
1642024800000,
1642028400000
],
"values": [
10,
15,
12
]
}
]
}
]
}
Output needs to be like this:
metricId dimensions timestamps values
builtin:tech.generic.cpu.usage process_345678 1642021200000 10
builtin:tech.generic.cpu.usage process_345678 1642024800000 15
builtin:tech.generic.cpu.usage process_345678 1642028400000 12
I have tried this:
print(pd.json_normalize(resp2, "data"))
I get invalid syntax, any ideas?

Take a look at the examples of json_normalize, and you'll see a list of dictionaries that have the key names of the columns you want, unique to each row. When you have nested lists/objects, then the columns will be flatten to have dot-notation, but nested arrays will not end up duplicated across rows.
Therefore, parse the data into a flat list, then you can use from_records.
data = []
for r in resp2['result']:
metricId = r['metricId']
for d in r['data']:
dimension = d['dimensions'][0] # unclear why this is an array
timestamps = d['timestamps']
values = d['values']
for t, v in zip(timestamps, values):
data.append({'metricId': metricId, 'dimensions': dimension, 'timestamps': t, 'values': v})
df = pd.DataFrame.from_records(data)

Setting column in Google Sheets API (with Python) to be number-formatted

I'm trying to format a column of numbers in Google Sheets using the API (Sheets API v.4 and Python 3.6.1, specifically). A portion of my non-functional code is below. I know it's executing, as the background color of the column gets set, but the numbers still show as text, not numbers.
Put another way, I'm trying to get the equivalent of clicking on a column header (A, B, C, or whatever) then choosing the Format -> Number -> Number menu item in the GUI.
def sheets_batch_update(SHEET_ID,data):
print ( ("Sheets: Batch update"))
service.spreadsheets().batchUpdate(spreadsheetId=SHEET_ID,body=data).execute() #,valueInputOption='RAW'
data={
"requests": [
{
"repeatCell": {
"range": {
"sheetId": all_sheets['Users'],
"startColumnIndex": 19,
"endColumnIndex": 20
},
"cell": {
"userEnteredFormat": {
"numberFormat": {
"type": "NUMBER",
"pattern": "#,##0",
},
"backgroundColor": {
"red": 0.0,
"green": 0.4,
"blue": 0.4
},
}
},
"fields": "userEnteredFormat(numberFormat,backgroundColor)"
}
},
]
}
sheets_batch_update(SHEET_ID, data)

The problem is likely that your data is currently stored as strings and therefore not affected by the number format.
"userEnteredValue": {
"stringValue": "1000"
},
"formattedValue": "1000",
"userEnteredFormat": {
"numberFormat": {
"type": "NUMBER",
"pattern": "#,##0"
}
},
When you set a number format via the UI (Format > Number > ...) it's actually doing two things at once:
Setting the number format.
Converting string values to number values, if possible.
Your API call is only doing #1, so any cells that are currently set with a string value will remain a string value and will therefore be unaffected by the number format. One solution would be to go through the affected values and move the stringValue to a numberValue if the cell contains a number.

To flesh out the answer from Eric Koleda a bit more, I ended up solving this two ways, depending on how I was getting the data for the Sheet:
First, if I was appending cells to the sheet, I used a function:
def set_cell_type(cell_contents):
current_cell_contents=str(cell_contents).replace(',', '')
float_cell=re.compile("^\d+\.\d+$")
int_cell=re.compile("^\d+$")
if int_cell.search(current_cell_contents):
data = {"userEnteredValue": {"numberValue": int(current_cell_contents)}}
elif float_cell.search(current_cell_contents):
data = {"userEnteredValue": {"numberValue": float(current_cell_contents)}}
else:
data = {"userEnteredValue": {"stringValue": str(cell_contents)}}
return data
To format the cells properly. Here's the call that actually did the appending:
rows = [{"values": [set_cell_type(cell) for cell in row]} for row in daily_data_output]
data = { "requests": [ { "appendCells": { "sheetId": all_sheets['Daily record'], "rows": rows, "fields": "*", } } ], }
sheets_batch_update(SHEET_ID,data)
Second, if I was replacing a whole sheet, I did:
#convert the ints to ints and floats to floats
float_cell=re.compile("^\d+\.\d+$")
int_cell=re.compile("^\d+$")
row_list=error_message.split("\t")
i=0
while i < len(row_list):
current_cell=row_list[i].replace(',', '') #remove the commas from any numbers
if int_cell.search(current_cell):
row_list[i]=int(current_cell)
elif float_cell.search(current_cell):
row_list[i]=float(current_cell)
i+=1
error_output.append(row_list)
then the following to actually save error_output to the sheet:
data = {'values': [row for row in error_output]}
sheets_update(SHEET_ID,data,'Errors!A1')
those two techniques, coupled with the formatting calls I had already figured out in my initial question, did the trick.