python - Why is float() faster than int()?

Question

Experimenting with some code and doing some microbenchmarks I just found out that using the float function on a string containing an integer number is a factor 2 faster than using int on the same string.

>>> python -m timeit int('1')
1000000 loops, best of 3: 0.548 usec per loop

>>> python -m timeit float('1')
1000000 loops, best of 3: 0.273 usec per loop

It gets even stranger when testing int(float('1')) which runtime is shorter than the bare int('1').

>>> python -m timeit int(float('1'))
1000000 loops, best of 3: 0.457 usec per loop

I tested the code under Windows 7 running cPython 2.7.6 and Linux Mint 16 with cPython 2.7.6.

I have to add that only Python 2 is affected, Python 3 shows a way smaller (not remarkable) difference between the runtimes.

I know that the information I get by such microbenchmarks are easy to misuse, but I'm curious why there is such a difference in the functions' runtime.

I tried to find the implementations of int and float but I can not find it in the sources.

Answer 1

int has lots of bases.

*, 0*, 0x*, 0b*, 0o* and it can be long, it takes time to determine the base and other things

if the base is set, it saves a lot of time

python -m timeit "int('1',10)"       
1000000 loops, best of 3: 0.252 usec per loop

python -m timeit "int('1')"   
1000000 loops, best of 3: 0.594 usec per loop

as @Martijn Pieters metions the code the Object/intobject.c(int_new) and Object/floatobject.c(float_new)