How does the inertia cython implementation in scikit-learn for kmeans work? Specifically, what do the &amp; symbol stand for? and why is the column index always 0?
    cpdef floating _inertia_dense(
        np.ndarray[floating, ndim=2, mode='c'] X,  # IN
        floating[::1] sample_weight,               # IN
        floating[:, ::1] centers,                  # IN
        int[::1] labels):                          # IN
    &quot;&quot;&quot;Compute inertia for dense input data
    Sum of squared distance between each sample and its assigned center.
    &quot;&quot;&quot;
    cdef:
        int n_samples = X.shape[0]
        int n_features = X.shape[1]
        int i, j

        floating sq_dist = 0.0
        floating inertia = 0.0

    for i in range(n_samples):
        j = labels[i]
        sq_dist = _euclidean_dense_dense(&amp;X[i, 0], &amp;centers[j, 0],
                                         n_features, True)
        inertia += sq_dist * sample_weight[i]

    return inertia
&amp; is the &quot;address-of&quot; operator in c, and that appears to be how it's being used here.  See these two SO posts.
Note the signature of _euclidean_dense_dense:
cdef floating _euclidean_dense_dense(
        floating* a, # IN
        floating* b, # IN
        int n_features,
        bint squared) nogil:

The first two inputs are pointers. So you need to pass the address, not a copy of the data.
Notice too that only the address of the element in the first column gets passed. If you look at the definition for _euclidean_dense_dense that becomes clearer: that function actually loops over the addresses of the rest of the columns in its computation.