Hi, I’m trying to do some QA from my ontology objects within Jupyter notebooks. I have imported the relevant object types and followed the steps in the SDK versions tab but still finding it challenging. Is there a way for me to turn the imported objects into pandas dataframes or am I required to followed the developer console steps for everything?
Thanks!
Is there a specific reason why you’re not using datasets?
Object’s can have materialisations (previously called ‘write back’) where the current Object date will be written to. These can be pulled in as aliases in your notebook, and used in the way you describe.
Let me know if this solves your issue!
Thanks @jakehop for responding. The reason we were looking to use objects instead of datasets is that we are facing a lot of issues with filtering on a timestamp column
Interesting – I can definitely understand that this can be an issue, and there are some benefits to get from using the Ontology. However, I am curious why you’re looking to Pandas, as this is generally not well optimised.
I can probably be of more help if you let me know what is your concrete timestamp problem and what is the work you are trying to do.
Cheers!
@amhall Could you provide some more details on the issues you’re facing with filtering a timestamp column? This might be a known issue but want to confirm
Hi @calebh
I am now struggling to find an example but found we’d often get issues when trying to filter or match on timestamp columns. This was often linked to the columns being differing data types (despite both saying they are timestamp), I think because some would be in timestamp format 2014-09-12T19:34:29Z and others would differ.
Sorry I cannot be more specific - if I notice this issue again, I will reach out. Hope this helps in the meantime.
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