DA0-002 Data Acquisition and Preparation Practice Question
A data analyst is using pandas in Python to merge two DataFrames: sales (columns: sale_id, product_id, amount) and products (columns: product_id, product_name). Which pandas function should they use to combine these DataFrames on the 'product_id' column?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
merge()
The pandas merge function is used to combine DataFrames on common columns. The syntax is pd.merge(sales, products, on='product_id').
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
combine()
Why it's wrong here
combine() is used for element-wise operations, not merging.
- ✓
merge()
Why this is correct
Correct. merge() is designed for database-style joins.
- ✗
join()
Why it's wrong here
join() is a method on DataFrames that uses index by default, but merge is more explicit for column joins.
- ✗
concat()
Why it's wrong here
concat() concatenates along an axis, not on a key column.
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