Dynamic axis indexing of Numpy ndarray

Transpose is cheap (timewise). There are numpy functions that use it to move the operational axis (or axes) to a known location - usually the front or end of the shape list. tensordot is one that comes to mind.

Other functions construct an indexing tuple. They may start with a list or array for ease of manipulation, and then turn it into a tuple for application. For example

I = [slice(None)]*A.ndimI[axis] = idxA[tuple(I)]

np.apply_along_axis does something like that. It's instructive to look at the code for functions like this.

I imagine the writers of the numpy functions worried most about whether it works robustly, secondly about speed, and lastly whether it looks pretty. You can bury all kinds of ugly code in a function!.

tensordot ends with

at = a.transpose(newaxes_a).reshape(newshape_a)bt = b.transpose(newaxes_b).reshape(newshape_b)res = dot(at, bt)return res.reshape(olda + oldb)

where the previous code calculated newaxes_.. and newshape....

apply_along_axis constructs a (0...,:,0...) index tuple

i = zeros(nd, 'O')i[axis] = slice(None, None)i.put(indlist, ind)....arr[tuple(i.tolist())]

To index a dimension dynamically, you can use swapaxes, as shown below:

a = np.arange(7 * 8 * 9).reshape((7, 8, 9))axis = 1idx = 2np.swapaxes(a, 0, axis)[idx]

Runtime comparison

Natural method (non dynamic) :

%timeit a[:, idx, :]300 ns ± 1.58 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

swapaxes:

%timeit np.swapaxes(a, 0, axis)[idx]752 ns ± 4.54 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

Index with list comprehension:

%timeit a[[idx if i==axis else slice(None) for i in range(a.ndim)]]