Hi Alexander, In my opinion - because they don't do the same thing, especially when you think in terms in lower-level. ndarray.flat returns an iterator; ndarray.flatten() returns a copy; ndarray.ravel() only makes copies when necessary; ndarray.reshape() is more general purpose, even though you can use it to flatten arrays. They are very distinct in behavior - for example, copies and views may store in the memory very differently and you would have to pay attention to the stride size if you are passing them down onto C/Fortran code. (Correct me if I am wrong please) -Shawn On Mon, Oct 27, 2014 at 8:06 PM, Alexander Belopolsky <ndarray@mac.com> wrote:

Given an n-dim array x, I can do

1. x.flat 2. x.flatten() 3. x.ravel() 4. x.reshape(-1)

Each of these expressions returns a flat version of x with some variations. Why does NumPy implement four different ways to do essentially the same thing?

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