What is the memory layout of NumPy arrays?
An example of an AI chatbot conversation about the memory layout of NumPy arrays.
What is the memory layout of NumPy arrays?
NumPy arrays can be stored in two main layouts: C-contiguous (row-major) and Fortran-contiguous (column-major).
Can you explain the difference between C-contiguous and Fortran-contiguous?
Sure! C-contiguous means that the last axis is stored contiguously in memory, while Fortran-contiguous means the first axis is stored contiguously.
How does this affect performance?
The memory layout can impact performance, especially in operations that involve looping through the array. C-contiguous arrays are generally faster for row-wise operations.
What about memory usage?
Both layouts use the same amount of memory for the same data, but the layout can affect how efficiently that memory is accessed.
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