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Why is it recommended to use NumPy's logical functions (like np.logical_and) instead of Python's and, or, not operators when working with arrays?

hard📝 Conceptual Q10 of 15
NumPy - Array Operations
Why is it recommended to use NumPy's logical functions (like np.logical_and) instead of Python's and, or, not operators when working with arrays?
ABecause NumPy functions are faster for scalar values
BBecause Python's operators convert arrays to strings
CBecause Python's logical operators do not work element-wise on arrays and cause errors
DBecause NumPy functions automatically convert booleans to integers
Step-by-Step Solution
Solution:
  1. Step 1: Understand Python logical operators with arrays

    Python's and, or, not do not perform element-wise operations on arrays and raise errors.
  2. Step 2: Role of NumPy logical functions

    NumPy's logical functions perform element-wise logical operations safely on arrays.
  3. Final Answer:

    Because Python's logical operators do not work element-wise on arrays and cause errors -> Option C
  4. Quick Check:

    Use NumPy logical functions for arrays, not Python operators [OK]
Quick Trick: Python logical ops don't work element-wise on arrays [OK]
Common Mistakes:
  • Using Python 'and'/'or' with arrays
  • Assuming automatic conversion
  • Confusing speed with correctness

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