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You have a large 2D numpy array stored in Fortran order. You want to perform fast row-wise operations. What should you do to optimize memory layout?

hard📝 Application Q8 of 15
NumPy - Fundamentals
You have a large 2D numpy array stored in Fortran order. You want to perform fast row-wise operations. What should you do to optimize memory layout?
ATranspose array and keep Fortran order
BKeep array as is, Fortran order is best for rows
CConvert array to C-contiguous layout using np.ascontiguousarray
DUse slicing with step to create contiguous rows
Step-by-Step Solution
Solution:

  1. Step 1: Understand memory layout impact on row operations

    C-contiguous layout stores rows continuously, speeding row-wise access.

  2. Step 2: Convert Fortran order array to C-contiguous

    Use np.ascontiguousarray to get a C-contiguous copy for fast row operations.

  3. Final Answer:

    Convert array to C-contiguous layout using np.ascontiguousarray -> Option C

  4. Quick Check:

    Use C-contiguous for fast row-wise ops [OK]
Quick Trick: Use np.ascontiguousarray for row-wise speedup [OK]
Common Mistakes:
  • Assuming Fortran order is best for rows
  • Using transpose without fixing layout
  • Using slicing to fix contiguity

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