[Solved] Identify the potential issue in this code:import numpy as np arr = np.array([True, False, True]) result = np.logical_not(arr, out=arr) print(result) | NumPy

NumPy - Array Operations

Identify the potential issue in this code:

import numpy as np
arr = np.array([True, False, True])
result = np.logical_not(arr, out=arr)
print(result)

AUsing the same array as output causes unexpected results

Bnp.logical_not does not accept an 'out' parameter

CThe array must be of integers, not booleans

Dnp.logical_not requires two input arrays

Step-by-Step Solution

Solution:

Step 1: Check use of 'out' parameter
Using the same array as input and output can overwrite data during computation, causing unexpected results.
Step 2: Proper usage
Use a different array or omit 'out' to avoid overwriting input during operation.
Final Answer:
Using the same array as output causes unexpected results -> Option A
Quick Check:
Same input/output array in logical_not = Risky [OK]

Quick Trick: Avoid using same array as input and output [OK]

Common Mistakes:

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