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Description of Issue
I'm joining two dataframes, person and track. They have key columns named trackingid and tracking_id.
In the default case, the merge operation will keep both key columns in the output, but naturally, they are duplicate column content. If I select "Drop some columns", and select the key column, Bamboolib will make sure the key is not removed, since it's required in the merge.
# Step: Inner Join with track where trackingid=tracking_id
person2 = pd.merge(person, track.drop(columns=[]), how='inner', left_on=['trackingid'], right_on=['tracking_id'])
This behavior is counter intuitive, and the Transformation should drop the columns after the join.
Reproduction Steps
Join any two tables.
Try to drop the key column.
What steps have you taken to resolve this already?
Anything else?
...
The text was updated successfully, but these errors were encountered:
Environment
Description of Issue
I'm joining two dataframes,
person
andtrack
. They have key columns namedtrackingid
andtracking_id
.In the default case, the merge operation will keep both key columns in the output, but naturally, they are duplicate column content. If I select "Drop some columns", and select the key column, Bamboolib will make sure the key is not removed, since it's required in the merge.
This behavior is counter intuitive, and the Transformation should drop the columns after the join.
Reproduction Steps
What steps have you taken to resolve this already?
Anything else?
...
The text was updated successfully, but these errors were encountered: