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  1. If I have a signal "A" and I push the data to two different notebooks, are these two separate signals that are only available within that workbook? It seems when I do this, that the signal "A" is shared across notebooks, but I thought it should be only local to the workbook pushed to. The code below, I can see the 4,5,6 in both workbooks. Is that right? Note, I didn't put the datetime index within the df, but it would be there. data = {"A":[1,2,3]} df = pd.DataFrame(data) spy.push(data=df_pred, workbook='Workbook1', worksheet=None) data = {"A":[4,5,6]} df = pd.DataFrame(data) spy.push(data=df, workbook='Workbook2', worksheet=None)
  2. I pushed data to a signal using spy.push. I tried to overwrite / 'erase' the pushed results by passing a None value for that particular timestamp. However, when I push the results, I still see the previous values pushed. Now, I can push new values to that particular timestamp and it will be overridden, but None values doesn't appear to work. Note, in order to push the None values without erroring out, I had to pass the metadata. Is there a way to overwrite previously pushed data with 'None' values?
  3. In my example, I have a python script that pushes data to a Worksheet within a WorkBench. I see the data is stored under the category "Seeq Data Lab". I was wondering, are there any storage retention limitations to data pushed using SEEQ Data Lab? Also, where does this data get stored?
  4. Thanks Teddy, Can you create & activate different virtual environments within SEEQ Data Lab? In this case still have python 3.8, but create different environments with different package versions. Thank you, Nate
  5. Hello, Note: I am using the learn.seeq.com environment of SEEQ Data Lab. I see that SEEQ Data Lab uses python version 3.8.8. I have some scripts / packages that work on an earlier version of python. Is there a way to install a different version of python on SEEQ Data Lab? I tried apt-get python3.7.9, but I don't appear to have permissions to do so. Additionally, it would be nice to setup different virtual environments that may use different packages & python versions. Is there a way to create a different virtual environment within SEEQ Data Lab? I tried creating one with conda, and it gets created. However, SEEQ Data Lab throws an error when attempting to activate it. Thank you, Nate
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