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For instance, in some places could have another column for the values with some NaN and dropping the NaN would drop, by default, any column having any NaN.
To fix this, we should use subset=["event_value"] to make sure that we only take the column event_value into account.
Regarding the the use case of storing NaN to the database, I think that as long as we can save a database with an irregular sampling rate, we are good to go.
For instance, in some places could have another column for the values with some NaN and dropping the NaN would drop, by default, any column having any NaN.
To fix this, we should use
subset=["event_value"]
to make sure that we only take the columnevent_value
into account.Regarding the the use case of storing NaN to the database, I think that as long as we can save a database with an irregular sampling rate, we are good to go.
Originally posted by @victorgarcia98 in #735 (review)
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