Summing With A For Loop Faster Than With Reduce?

I wanted to see how much faster reduce was than using a for loop for simple numerical operations. Here's what I found (using the standard timeit library): In [54]: print(setup) fro

Solution 1:

The reduce(add, r) must invoke the add() function 100 times, so the overhead of the function calls adds up -- reduce uses PyEval_CallObject to invoke add on each iteration:

for (;;) {
    ...
    if (result == NULL)
        result = op2;
    else {
        # here it is creating a tuple to pass the previous result and the next# value from range(100) into func add():
        PyTuple_SetItem(args, 0, result);
        PyTuple_SetItem(args, 1, op2);
        if ((result = PyEval_CallObject(func, args)) == NULL)
            goto Fail;
    }

Updated: Response to question in comments.

When you type 1 + 2 in Python source code, the bytecode compiler performs the addition in place and replaces that expression with 3:

f1 = lambda: 1 + 2
c1 = byteplay.Code.from_code(f1.func_code)
print c1.code

1           1 LOAD_CONST           3
            2 RETURN_VALUE         

If you add two variables a + b the compiler will generate bytecode which loads the two variables and performs a BINARY_ADD, which is far faster than calling a function to perform the addition:

f2 = lambda a, b: a + b
c2 = byteplay.Code.from_code(f2.func_code)
print c2.code

1           1 LOAD_FAST            a
            2 LOAD_FAST            b
            3 BINARY_ADD           
            4 RETURN_VALUE         

Solution 2:

edit: Switching out zeroes instead of array multiply closes the gap big time.

from functools import reduce
from numpy import array, arange, zeros
fromtime import time

def add(x, y):
    return x + y

def sum_columns(x):
    if x.any():
        width = len(x[0])
        total = zeros(width)
    for row in x:
        total += array(row)
    return total

l = arange(3000000)
l = array([l, l, l])

start = time()
print(reduce(add, l))
print('Reduce took {}'.format(time() - start))

start = time()
print(sum_columns(l))
print('For loop took took {}'.format(time() - start))

Gets you down almost no difference at all.

Reduce took 0.03230619430541992 For loop took took 0.058577775955200195

old: If reduce is used for adding together NumPy arrays by index, it can be faster than a for loop.

from functools import reduce
from numpy import array, arange
fromtime import time

def add(x, y):
    return x + y

def sum_columns(x):
    if x.any():
        width = len(x[0])
        total = array([0] * width)
    for row in x:
        total += array(row)
    return total

l = arange(3000000)
l = array([l, l, l])

start = time()
print(reduce(add, l))
print('Reduce took {}'.format(time() - start))

start = time()
print(sum_columns(l))
print('For loop took took {}'.format(time() - start))

With the result of

[      0       3       6 ..., 8999991 8999994 8999997]
Reduce took 0.024930953979492188[      0       3       6 ..., 8999991 8999994 8999997]
For loop took took 0.3731539249420166