Why does np.min() return a scalar when applied to a 1D array but an array when used with axis parameter on a 2D array?

hard📝 Conceptual Q10 of 15

NumPy - Aggregation Functions

Why does np.min() return a scalar when applied to a 1D array but an array when used with axis parameter on a 2D array?

ABecause np.min() cannot handle 2D arrays without axis

BBecause np.min() always returns arrays regardless of input

CBecause 1D arrays have no axis parameter

DBecause axis specifies dimension to reduce, returning array of mins per slice

Step-by-Step Solution

Solution:

Step 1: Understand np.min() behavior on 1D arrays
On 1D arrays, np.min() returns a single smallest value (scalar).
Step 2: Understand axis parameter effect on 2D arrays
When axis is given, np.min() reduces along that axis, returning an array of minimums per row or column.
Final Answer:
Because axis specifies dimension to reduce, returning array of mins per slice -> Option D
Quick Check:
axis controls output shape of np.min() [OK]

Quick Trick: axis parameter controls output shape of np.min() [OK]

Common Mistakes:

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