Processing | Script for processing data from OpenBounds projects | Data Processing library

I am a beginner in Data Science, so please sorry if my mistake is dumb.

Here, I have a loop which views my data frame and makes changes using .loc The problem is that the changes are not saved at the end. I checked every step, everything is processing right. I even checked the modified cell right after working on it (look below) and its gives the value I put into it. However, when the program finishes the my excel data frame is not changed at all.

Help please. Thank you in advance!

I'm writing a Firebase function (Gist) which

1. Queries a realtime database ref (events) in the following fashion:

   await admin.database().ref('/events_geo').once('value').then(snapshots => {

2. Iterates through all the events

   snapshots.forEach(snapshot => {

3. Events are filtered by a criteria for further processing

4. Several queries are fired off towards realtime DB to get details related to the event

   await database().ref("/ratings").orderByChild('fk_event').equalTo(snapshot.key).once('value').then(snapshots => {

5. Data is prepared for SendGrid and the processing is finished

All of the data processing works perfectly fine but I can't get the outer await (point 1 in my list) to wait for the inner awaits (queries towards realtime DB) and thus when SendGrid should be called the data is empty. The data arrives a little while later. Example output from Firebase function logs can be seen below:

10:54:12.642 AM Function execution started

10:54:13.945 AM There are no emails to be sent in afterEventHostMailGoodRating

10:54:14.048 AM There are no emails to be sent in afterEventHostMailBadRating

10:54:14.052 AM Function execution took 1412 ms, finished with status: 'ok'

10:54:14.148 AM

Super hyggelig aften :)

super oplevelse, ... long string generated

Gist showing the function in question

I'm probably mixing up my async/awaits because of the awaits inside the await. But I don't see how else the code could be written without splitting it out into many atomic pieces but that would still require stitching a bunch of awaits together and make it harder to read.

So, two questions in total. Can this code work and what would be the ideal way to handle this pattern of making further processing on top of data fetched from Realtime DB?

Best regards, Simon

Update: Added a simpler demonstration jsfiddle, https://jsfiddle.net/47sfj3Lv/3/.

reproducing the problem in much less code I'm trying to move away from jQuery.

Some of my code, for populating some tables, has code like this

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I have to formulate SQL Query to display total success and failed device processing. Suppose User selects 2 devices and runs some process. Each Process which user triggers will spawn 4 jobs (1 devices has 2 jobs to run). Since here user select 2 devices so 4 records comes in db. Now based on ParentTaskId I need to display total successfull,failed jobs with total devices.

We count a job on a device as success only when both jobs(Type 1,Type 2) are success.

Note : If jobtype 1 fails , jobtype 2 will not trigger

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However, if I apply a MapFunction to the data before returning from function, I get

Exception in thread "main" org.apache.spark.SparkException: Task not serializable

Caused by: java.io.NotSerializableException: com.Workflow.

I know Spark's working and its need to serialize objects for distributed processing, however, I'm NOT using any reference to Workflow class in my mapping logic. I'm not calling any Workflow class function in my mapping logic. So why is Spark trying to serialize Workflow class? Any help will be appreciated.

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That's the code:

I have a function that accepts an argument which can be either of type A or B. In case A is passed, it is converted to B for further processing internally.

To keep it simple, consider the following example using int/float to illustrate the scenario:

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