Why does np.cumsum() return a flattened array when called without an axis on a multi-dimensional array?

hard📝 Conceptual Q10 of 15

NumPy - Aggregation Functions

Why does np.cumsum() return a flattened array when called without an axis on a multi-dimensional array?

ABecause it sums only the first dimension by default

BBecause it treats the array as 1D by flattening before summing

CBecause it raises an error and returns a flattened copy

DBecause it sums elements column-wise automatically

Step-by-Step Solution

Solution:

Step 1: Check default axis behavior
When axis is None, numpy flattens the array to 1D before operation.
Step 2: Understand why flattening happens
This ensures cumulative sum is computed over all elements in order, ignoring original shape.
Final Answer:
Because it treats the array as 1D by flattening before summing -> Option B
Quick Check:
Default axis=None means flatten array first [OK]

Quick Trick: No axis means flatten array before cumsum [OK]

Common Mistakes:

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