NumPy: function for simultaneous max() and min()

Is there a function in the numpy API that finds both max and min with only a single pass through the data?

No. At the time of this writing, there is no such function. (And yes, if there were such a function, its performance would be significantly better than calling numpy.amin() and numpy.amax() successively on a large array.)

I don't think that passing over the array twice is a problem. Consider the following pseudo-code:

minval = array[0]
maxval = array[0]
for i in array:
    if i < minval:
       minval = i
    if i > maxval:
       maxval = i

While there is only 1 loop here, there are still 2 checks. (Instead of having 2 loops with 1 check each). Really the only thing you save is the overhead of 1 loop. If the arrays really are big as you say, that overhead is small compared to the actual loop's work load. (Note that this is all implemented in C, so the loops are more or less free anyway).

---

EDIT Sorry to the 4 of you who upvoted and had faith in me. You definitely can optimize this.

Here's some fortran code which can be compiled into a python module via f2py (maybe a Cython guru can come along and compare this with an optimized C version ...):

subroutine minmax1(a,n,amin,amax)
  implicit none
  !f2py intent(hidden) :: n
  !f2py intent(out) :: amin,amax
  !f2py intent(in) :: a
  integer n
  real a(n),amin,amax
  integer i

  amin = a(1)
  amax = a(1)
  do i=2, n
     if(a(i) > amax)then
        amax = a(i)
     elseif(a(i) < amin) then
        amin = a(i)
     endif
  enddo
end subroutine minmax1

subroutine minmax2(a,n,amin,amax)
  implicit none
  !f2py intent(hidden) :: n
  !f2py intent(out) :: amin,amax
  !f2py intent(in) :: a
  integer n
  real a(n),amin,amax
  amin = minval(a)
  amax = maxval(a)
end subroutine minmax2

Compile it via:

f2py -m untitled -c fortran_code.f90

And now we're in a place where we can test it:

import timeit

size = 100000
repeat = 10000

print timeit.timeit(
    'np.min(a); np.max(a)',
    setup='import numpy as np; a = np.arange(%d, dtype=np.float32)' % size,
    number=repeat), " # numpy min/max"

print timeit.timeit(
    'untitled.minmax1(a)',
    setup='import numpy as np; import untitled; a = np.arange(%d, dtype=np.float32)' % size,
    number=repeat), '# minmax1'

print timeit.timeit(
    'untitled.minmax2(a)',
    setup='import numpy as np; import untitled; a = np.arange(%d, dtype=np.float32)' % size,
    number=repeat), '# minmax2'

The results are a bit staggering for me:

8.61869883537 # numpy min/max
1.60417699814 # minmax1
2.30169081688 # minmax2

I have to say, I don't completely understand it. Comparing just np.min versus minmax1 and minmax2 is still a losing battle, so it's not just a memory issue ...

notes -- Increasing size by a factor of 10**a and decreasing repeat by a factor of 10**a (keeping the problem size constant) does change the performance, but not in a seemingly consistent way which shows that there is some interplay between memory performance and function call overhead in python. Even comparing a simple min implementation in fortran beats numpy's by a factor of approximately 2 ...