Python 性能优化方法整理

python 常因速度慢而被诟病。其实优化代码性能的方案有很多，最主要的一般是两个方向：
- 从算法本身优化，这是最根本和彻底的优化；
- 从语言本身和工具层面优化，不外乎类型化、空间换时间等；

刚好看到一篇文章 讲的比较好，整理一下。

一、Fibonacci 函数

常见的Fibonacci 函数实现有两种，一种是递归，一种是非递归（好像是废话。。。。）

• 常规代码

def fib(n): if n<2:   return n return fib(n-1)+fib(n-2)def fib_seq(n): if n < 2:   return n a,b = 1,0 for i in range(n-1):   a,b = a+b,a return a %timeit fib(20)%timeit fib_seq(20)100 loops, best of 3: 2.28 ms per loop1000000 loops, best of 3: 1.04 µs per loop

• cython 版本

%%cythondef fib_cython(n): if n<2:  return n return fib_cython(n-1)+fib_cython(n-2)def fib_seq_cython(n): if n < 2:   return n a,b = 1,0 for i in range(n-1):   a,b = a+b,a return a cpdef long fib_seq_cython_type(long n): if n < 2:   return n cdef long a,b a,b = 1,0 for i in range(n-1):   a,b = a+b,b return a%timeit fib_cython(20)%timeit fib_seq_cython(20)%timeit fib_seq_cython_type(20)1000 loops, best of 3: 741 µs per loop1000000 loops, best of 3: 599 ns per loop10000000 loops, best of 3: 33 ns per loop

• cache 版本

from functools import lru_cache as cache@cache(maxsize=None)def fib_cache(n): if n<2:  return n return fib_cache(n-1)+fib_cache(n-2)@cache(maxsize=None)def fib_seq_cache(n): if n < 2:   return n a,b = 1,0 for i in range(n-1):   a,b = a+b,a return a %timeit fib_cache(20)%timeit fib_seq_cache(20)10000000 loops, best of 3: 84.1 ns per loop10000000 loops, best of 3: 87.9 ns per loop

• numba 版本

from numba import jit@jitdef fib_numba(n): if n<2:  return n return fib_numba(n-1)+fib_numba(n-2)@jitdef fib_seq_numba(n): if n < 2:  return n (a,b) = (1,0) for i in range(n-1):  (a,b) = (a+b,a) return a%timeit fib_numba(20)%timeit fib_seq_numba(20)10000 loops, best of 3: 47.6 µs per loop10000000 loops, best of 3: 164 ns per loop

二、快速排序

• 常规代码

def qsort_kernel(a, lo, hi):    i = lo    j = hi    while i < hi:        pivot = a[(lo+hi) // 2]        while i <= j:            while a[i] < pivot:                i += 1            while a[j] > pivot:                j -= 1            if i <= j:                a[i], a[j] = a[j], a[i]                i += 1                j -= 1        if lo < j:            qsort_kernel(a, lo, j)        lo = i        j = hi    return aimport randomlst = [ random.random() for i in range(1,5000) ]%timeit qsort_kernel(lst, 0, len(lst)-1)100 loops, best of 3: 4.95 ms per loop

• cython 版本

%%cythondef qsort_cython(a, lo, hi):    i = lo    j = hi    while i < hi:        pivot = a[(lo+hi) // 2]        while i <= j:            while a[i] < pivot:                i += 1            while a[j] > pivot:                j -= 1            if i <= j:                a[i], a[j] = a[j], a[i]                i += 1                j -= 1        if lo < j:            qsort_cython(a, lo, j)        lo = i        j = hi    return alst = [ random.random() for i in range(1,5000) ]%timeit qsort_cython(lst, 0, len(lst)-1)100 loops, best of 3: 2.47 ms per loop

• numba 版本

@jitdef qsort_numba(a, lo, hi):    i = lo    j = hi    while i < hi:        pivot = a[(lo+hi) // 2]        while i <= j:            while a[i] < pivot:                i += 1            while a[j] > pivot:                j -= 1            if i <= j:                a[i], a[j] = a[j], a[i]                i += 1                j -= 1        if lo < j:            qsort_numba(a, lo, j)        lo = i        j = hi    return alst = [ random.random() for i in range(1,5000) ]%timeit qsort_numba(lst, 0, len(lst)-1)10000 loops, best of 3: 150 µs per loop