ENH: ndarray.T2 for 2D transpose.

When you try to transpose a 1D array, it does nothing. This is the correct behavior, since it transposing a 1D array is meaningless. However, this can often lead to unexpected errors since this is rarely what you want. You can convert the array to 2D, using np.atleast_2d or arr[None], but this makes simple linear algebra computations more difficult.

I propose adding an argument to transpose, perhaps called expand or expanddim, which if True (it is False by default) will force the array to be at least 2D. A shortcut property, ndarray.T2, would be the same as ndarray.transpose(True).

[insertinterestingnamehere]

I’ve known a few other people who think that the behavior of transpose for 1-D arrays isn’t as useful as it could be. The current behavior is somewhat counter intuitive, especially for beginners. +1 to providing an API that helps alleviate that.

I agree with the improvement suggestion as well. It’d be really nice if we could also make it do a broadcasting 2D transpose by swapping the last two dimensions of any higher dimensional array rather than reversing the dimensions the way transpose currently does. When chaining together a series of gufunc based operations to apply a linear algebra-like transformation, it seems odd that the idiom for a transpose is np.swapaxes(a, 1, 2). For example, the expression a @ b.T with 2D arrays could follow gufunc-like semantics when expressed as a @ b.T2 rather than a @ np.swapaxes(b, -1, -2).

All that said, a mailing list post is better for discussing something like this.

[njsmith]

There’s some discussion of details here: https://github.com/numpy/numpy/issues/9530