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.
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combine()
Why it's wrong here
DataFrame.combine() element-wise merges two same-shaped DataFrames using a function, aligning by index and column labels rather than matching product_id keys. It suits reconciling two aligned numeric frames, such as choosing the larger of two values per cell, not joining sales to products.
- ✓
merge()
Why this is correct
merge() performs a database-style join on a shared key, so passing on='product_id' combines sales and products into one DataFrame with product_name attached to each sale. concat() only stacks frames, and join() defaults to index alignment, neither matching this key-based requirement.
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join()
Why it's wrong here
DataFrame.join() merges on the index or a named index level by default, so it cannot align the two frames on the product_id column without first calling set_index. It is the right tool when both DataFrames already carry a meaningful index you intend to join on.
- ✗
concat()
Why it's wrong here
concat() stacks DataFrames along an axis, appending rows or columns positionally, and performs no key matching on product_id. It is correct for combining identically shaped frames, such as concatenating monthly sales files into one DataFrame, not for relational lookups.
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