Summing with a for loop faster than with reduce?

Question

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):

I         


        

Answer 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