FAQ: Working with Multiple DataFrames - Mismatched Merges


This community-built FAQ covers the “Mismatched Merges” exercise from the lesson “Working with Multiple DataFrames”.

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This exercise can be found in the following Codecademy content:

Data Science

Data Analysis with Pandas

FAQs on the exercise Mismatched Merges

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Yes… what did happen to order 3?
Is this a rhetorical question or are we supposed to know the answer?

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I’m thinking the same …

If you go to the next lesson the answer is given

from lesson 8/11:

“In the previous exercise, we saw that when we merge two DataFrames whose rows don’t match perfectly, we lose the unmatched rows.”

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I’ve applied the following code:


merged_df = orders.merge(products)


and get the following output:

Question: why is the order changed? shouldn’t it just append the additional columns into the existing order of the ‘orders’ dataframe?

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It may be wrong because I don’t understand the internal operation of the merge method exactly, but it seems that rows are organized by each value of product_id, the join key, respectively.

The values of product_id in orders is 3, 2, … So I’m guessing that the joining process is organized in this order (rows whose product_id is 3, and then rows whose product_id is 2, …).

I’ll appreciate if someone who knows exactly will reply.