Real matrix multiplication without automatic index matching
Automatic index matching is really great, but sometimes you need a real matrix multiplication, for example to compute the Covariance matrix (in my case using weights, hence .cov() is not usable).

Current workarounds include using numpy.outer() (which is still not really a real matrix multiplication, because it will always give the outer product), or t = t[:, None], but I feel this last solution may hamper the performances on big dataset.

It would be really nice to have a method that just does a "blind" matrix multiplication, multiplying the rows of the first matrix with the columns of the second one, without caring about index matching.

Edit: I'm here talking about DataFrame and Series, which could respectively be considered as matrix and vector. 
