Areas of NumPy that have come up as possible slow points:

• PyArray?_EnsureArray. It seems to be slow for python ints. This came up as a possible culprit for some pow() slowness. PyArray?_EnsureArray could special-check Python scalars for a speed-up.

• Default (digits=0) case of around is 10x slower than (x+0.5).astype(int).astype(float). Resolved: changeset:2151 implements fast rint function and adds a round method to ndarray that is about as fast for digits=0 case.

• As of changeset:2173 x.fill(1) is 2x slower than x += 1.

> python -m timeit -s "from numpy import zeros; x = zeros(10000,'b')" "x.fill(1)"
10000 loops, best of 3: 69.5 usec per loop
> python -m timeit -s "from numpy import zeros; x = zeros(10000,'h')" "x.fill(1)"
10000 loops, best of 3: 66.1 usec per loop
> python -m timeit -s "from numpy import zeros; x = zeros(10000,'i')" "x.fill(1)"
10000 loops, best of 3: 66.3 usec per loop
> python -m timeit -s "from numpy import zeros; x = zeros(10000,'d')" "x.fill(1)"
10000 loops, best of 3: 73.2 usec per loop

> python -m timeit -s "from numpy import zeros; x = zeros(10000,'b')" "x += 1"
10000 loops, best of 3: 58 usec per loop
> python -m timeit -s "from numpy import zeros; x = zeros(10000,'h')" "x += 1"
10000 loops, best of 3: 33.7 usec per loop
> python -m timeit -s "from numpy import zeros; x = zeros(10000,'i')" "x += 1"
10000 loops, best of 3: 33.6 usec per loop
> python -m timeit -s "from numpy import zeros; x = zeros(10000,'d')" "x += 1"
10000 loops, best of 3: 36.9 usec per loop