Why does fancy indexing with repeated indices in NumPy return repeated elements in the output array?

hard📝 Conceptual Q10 of 15

NumPy - Indexing and Slicing

ABecause NumPy merges duplicates automatically

BBecause fancy indexing creates a new array copying elements at each index

CBecause fancy indexing returns a view that shares data

DBecause repeated indices cause an error

Step-by-Step Solution

Solution:

Step 1: Understand fancy indexing behavior
Fancy indexing returns a new array copying elements at specified indices, including duplicates.
Step 2: Explain repeated elements
Repeated indices cause repeated copies of those elements in the output array.
Final Answer:
Because fancy indexing creates a new array copying elements at each index -> Option B
Quick Check:
Repeated indices produce repeated elements by copying [OK]

Quick Trick: Repeated indices produce repeated elements in output [OK]

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