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What happens if you specify dtype='int32' when creating a NumPy array from floating-point numbers?

easy📝 Conceptual Q1 of 15
NumPy - Array Data Types
What happens if you specify dtype='int32' when creating a NumPy array from floating-point numbers?
AThe array elements become strings representing the numbers.
BThe array will keep the floating-point numbers as they are.
CAn error occurs because floats cannot be converted to integers.
DThe floating-point numbers are converted to 32-bit integers by truncating decimals.
Step-by-Step Solution
Solution:

  1. Step 1: Understand dtype conversion

    Specifying dtype='int32' converts all elements to 32-bit integers.

  2. Step 2: Effect on floating-point numbers

    Floating-point numbers are truncated (not rounded) to integers during conversion.

  3. Final Answer:

    The floating-point numbers are converted to 32-bit integers by truncating decimals. -> Option D

  4. Quick Check:

    dtype conversion truncates floats to int = A [OK]
Quick Trick: dtype='int32' truncates floats to integers [OK]
Common Mistakes:
  • Thinking floats remain unchanged
  • Expecting rounding instead of truncation
  • Assuming conversion causes error

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