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Why does NumPy prefer fixed-type arrays over Python lists for scientific computing?

hard📝 Conceptual Q10 of 15
NumPy - Fundamentals
Why does NumPy prefer fixed-type arrays over Python lists for scientific computing?
AFixed-type arrays enable efficient memory use and faster operations
BPython lists are immutable and slow
CNumPy arrays can store any data type in one array
DPython lists do not support slicing
Step-by-Step Solution
Solution:

  1. Step 1: Understand fixed-type arrays

    NumPy arrays store elements of the same type, allowing compact storage.

  2. Step 2: Benefits of fixed types

    This uniformity allows NumPy to optimize memory and speed for numerical operations.

  3. Final Answer:

    Fixed-type arrays enable efficient memory use and faster operations -> Option A

  4. Quick Check:

    Fixed-type arrays improve efficiency = C [OK]
Quick Trick: Same data type arrays run faster and use less memory [OK]
Common Mistakes:
  • Thinking Python lists are immutable
  • Believing NumPy arrays hold mixed types easily
  • Assuming Python lists lack slicing

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