Asked about pandas on a real table
After a join, your total revenue doubled. What happened?
An answer that works
The right-hand table probably had duplicate keys, so each left row matched several right rows and was repeated. Check key uniqueness before joining, use validate to enforce the expected relationship, and compare row counts before and after.
Then they push further
How would you find the duplicates?
Group the right table by the join key and count, or use duplicated on the key column, and inspect those rows. Often it is a history table where each change adds a row, and you need the latest one per key.
What gets marked up
Marked up for naming duplicate keys immediately and for a preventive habit such as validate.
Reading an answer is not being able to give one. The section that teaches this is pandas on a real table, 5 hours of NumPy & pandas.
Learn this properlyAlso asked about NumPy & pandas