NumPy stores arrays as a single flat block of memory, and the transpose operation changes only how NumPy walks through this memory (via strides) without moving any data, which is why it's computationally fast.
Deep Dive
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Deep Dive
NumPy Changed This Array Without Moving It 🤯Added:
This NumPy array isn't really 3D.
Even though this looks like a multi-dimensional array, NumPy stores everything as a single block of memory just a flat sequence of bytes.
Strides tell NumPy how many bytes to jump to move along each axis.
For this 3D array, moving along columns?
small jump.
along rows?
bigger jump.
across layers?
even bigger jump.
Now when you transpose this from shape (2, 3, 4) to (4, 3, 2) NumPy... doesn't move any data.
It just changes how it walks through memory.
Same bytes.
New perspective.
And that’s why it’s fast.
More NumPy insights coming up Stay tuned.
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