pandas Merge, Join and Concat, what does print(merged.shape) print after left = pd.DataFrame({'k': [1, 1, 2], 'a': ['a1', 'a2', 'a3']})?

Answer, C. (4, 3)

A. (3, 3)

Treats the merge as if only one pair of the duplicated key had matched instead of every combination of the two occurrences on each side.

B. (6, 3)

Multiplies as if every key, not just the duplicated one, produced several matches.

C. (4, 3)

Correct. Key 1 appears twice on each side, so an inner join produces two times two, four matching rows for that key alone, while keys 2 and 3 have no match on the other side and are dropped, giving four rows in total.

D. (2, 3)

Assumes duplicate keys line up pairwise instead of multiplying, as if row order matched row order across the two frames.

When a key value is duplicated on both sides of a merge, every occurrence on the left is paired with every occurrence on the right, so the number of matching rows is the product of how many times the key appears on each side. A key present on only one side contributes nothing to an inner join.