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Re: Calculations using analyser or beast mode
Can you provide a little more commentary about what exactly you are looking to solve for here? It looks to me like you've already figured out the solution.
If you're looking to calculate numbers based on different filter criteria I'd recommend using a CASE statement on a beast mode or in magic ETL to solve.
Pretend I'm running a fruit stand -- I have 3 apples (that are red), 2 peaches (that are pink), and 5 blueberries (which are blue). I want to lump anything that is red or pink into a 'red' bucket.
I can write a beastmode called 'red fruits':
CASE WHEN fruit color IN ('Red', 'Pink') Then SUM(Quantity) End
This would add apples and peaches together.
Re: Data format from Jupyter to Dataset
it's unclear what your output from (presumably) magic etl scripting tiles looks like. can you send intermediate screenshots?
Re: Weighted Averages of Summary Data
I think it would be something along the lines of...
sum(`N` * `X-Y Average`) / sum(`N`)
Re: Query to create a condition on a database
you can easily created a beastmode case statement as
CASE
WHEN `PPG` = 'MPA 90GRS' THEN 90
WHEN `PPG` = 'CTA 90GRS' THEN 90
END
Adding as many conditions as you want individually or this is another option
CASE
WHEN `PPG` IN ('MPA 90GRS', 'CTA 90GRS') THEN 90
WHEN `PPG` = 'CLUMINOUS 70GRS' THEN 70
END
now if , PPG contains a lot distinct values , you could ETL a table with those values and join it to your data.
anyways, there is a lot different ways to do that. hope this helps
Re: Zendesk 1000 row limit
@Tsharma8724 It looks like it is a limitation of Zendesk API on results per query. https://developer.zendesk.com/api-reference/ticketing/ticket-management/search/
A connector enhancement request from your accounts team would be required for Domo Engineering to build it out.
You would need to adjust your query and run it on append daily so it is under the max.
TDanis
Re: Python Tile Typecasting while Reading Data
Have you tried converting your series directly to a category after you import it?
raw['column'] = raw['column'].astype('category')
Re: Getting incorrect result while computing an output using Beast mode
When you’re filtering and including a and b your lag function will pull data from metric a and b. You’ll want to add PARTITION BY ` metric ` to your lag statements to make sure you’re not jumping metrics with the lag



