Is there any benefit in performance using integer division operator over math.floor?
7 // 2
over
math.floor(7/2)
Is there any benefit in performance using integer division operator over math.floor?
7 // 2
over
math.floor(7/2)
Integer division is much faster than a math.floor
function call:
>>> import timeit
>>> timeit.timeit('7//2')
0.024671780910702337
>>> timeit.timeit('floor(7/2)', setup='from math import floor')
0.27053647879827736
>>> timeit.timeit('math.floor(7/2)', setup='import math')
0.3131167508719699
As you can see with this disassembly, using the math
module's floor
function (with import math
and math.floor
or from math import floor
and floor
) involve extra lookups and function calls over the plain integer division:
>>> import dis
>>> import math
>>> from math import floor
>>> def integer_division():
... 7//2
...
>>> def math_floor():
... floor(7/2)
...
>>> def math_full_floor():
... math.floor(7/2)
...
>>> dis.dis(integer_division)
2 0 LOAD_CONST 3 (3)
3 POP_TOP
4 LOAD_CONST 0 (None)
7 RETURN_VALUE
>>> dis.dis(math_floor)
2 0 LOAD_GLOBAL 0 (floor)
3 LOAD_CONST 3 (3.5)
6 CALL_FUNCTION 1 (1 positional, 0 keyword pair)
9 POP_TOP
10 LOAD_CONST 0 (None)
13 RETURN_VALUE
>>> dis.dis(math_full_floor)
2 0 LOAD_GLOBAL 0 (math)
3 LOAD_ATTR 1 (floor)
6 LOAD_CONST 3 (3.5)
9 CALL_FUNCTION 1 (1 positional, 0 keyword pair)
12 POP_TOP
13 LOAD_CONST 0 (None)
16 RETURN_VALUE
Certainly, there's some overhead in a function call, and if you do this enough times, you see a difference in speed:
Run time for //: 12.940021991729736 sec
Run time for floor: 26.933987855911255 sec
Here's the code I ran:
from time import time
from math import floor
start_time = time()
for i in range(1, 10000):
for j in range(1, 10000):
result = i // j
print('Run time for //:', time() - start_time, 'sec')
start_time = time()
for i in range(1, 10000):
for j in range(1, 10000):
result = floor(i/j)
print('Run time for floor:', time() - start_time, 'sec')