That still doesn't matter. So much of the work is being done in python that you're benchmarking the overhead of python, not the numpy speed.

I mean this is a terrible benchmark. Comparing to pure python, the numpy code is about 4-5x faster on my machine, whereas in reasonable real world benchmarks, numpy is hundreds of times faster than pure python.

For reference my benchmark was

    %timeit [sum(row)/len(row) for row in mat]

and it completed in about 900us, given mat was a python 2d array.

These results are as valuable as the ones pypy gave me:

    pypy -mtimeit -s "[sum(row)/len(row) for row in [[range(100000)[i*j] for i in range(100)] for j in range(1000)]]"  
    1000000000 loops, best of 3: 0.00101 usec per loop

Edit:

In other words, because the code returns a python array of length 1000, what you're benchmarking is python's speed of array instantiation vs. Ds mathematical speed. Its obvious that D will win. The only fair way to benchmark the mathematics here is if you compare the D implementation of O(n) algorithm that returns a single value, or an O(n^2) algorithm that returns a 1d array or single value etc. Otherwise the python overhead of creating O(n) objects will overshadow the actual mathematical calculations on those O(n) objects.