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added flag for clean columns for joining table dataset with source dataset #302
added flag for clean columns for joining table dataset with source dataset #302
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@ywkim312 Good to know that we already have a method for that. Im most curious about how fast it is? Could you have some benchmark for the SLC data I shared? |
Actually, it is very hard to do make a fair comparison because this one happens in the local machine but the service's join happens in the server side. However, using this code in pyincore is very meaningful because it will reduce the workload from the server significantly and we can avoid some errors like time out. |
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Test ran fine. Changes look good. Approve~
@longshuicy @jonglee1 @navarroc There was already an method for joining the table dataset with source dataset. I just added another variable to clean up the columns. If the new variable is true, it will remove all the columns from source dataset except 'geometry' and 'guid'. So eventually. the output geodataframe will only have the columns from table dataset with geometry. The user can easily convert this GeoDataFrame to shapefile or geopakage using geopands.
I am not very sure how we will handle this join process at this moment. We probably need to discuss more how to do it. However, having this PR will be helpful in some cases