python 加速运算

原文链接：https://blog.csdn.net/qq_27009517/article/details/103805099

一、加速查找

1.用set而非list

import time
 
data = [i**2+1 for i in range(1000000)]
list_data = list(data)
set_data = set(data)
# normal
tic = time.time()
s = 1098987 in list_data
toc = time.time()
print('userd: {:.5f}s'.format(toc-tic))
# speed up
tic = time.time()
ss = 1098987 in set_data
toc = time.time()
print('userd: {:.5f}s'.format(toc-tic))

2.用dict而非两个list进行匹配查找

import time
 
list_a = [i*2-1 for i in range(1000000)]
list_b = [i**2 for i in list_a]
dict_ab = dict(zip(list_a, list_b))
# normal
tic = time.time()
a = list_b[list_a.index(876567)]
toc = time.time()
print('userd: {:.5f}s'.format(toc-tic))
# speed up
tic = time.time()
aa = dict_ab.get(876567, None)
toc = time.time()
print('userd: {:.5f}s'.format(toc-tic))

二、加速循环，在循环体中避免重复计算，用循环机制代替递归函数

3.用for而非while

import time
 
tic = time.time()
s, i = 0, 0
while i<100000:
    i += 1
    s += i
toc = time.time()
print('userd: {:.5f}s'.format(toc-tic))
 
 
tic = time.time()
s, i = 0, 0
for i in range(1, 100001):
    i += 1
    s += i
toc = time.time()
print('userd: {:.5f}s'.format(toc-tic))

三、利用库函数进行加速

4.用numba加速Python函数

import time
 
tic = time.time()
def my_power(x):
    return (x**2)
 
def my_power_sum(n):
    s = 0
    for i in range(1, n+1):
        s = s + my_power(i)
    return s
s = my_power_sum(1000000)
toc = time.time()
print('used: {:.5f}s'.format(toc-tic))
 
# speed up
from numba import jit
tic = time.time()
@jit
def my_power(x):
    return (x**2)
@jit
def my_power_sum(n):
    s = 0
    for i in range(1, n+1):
        s = s + my_power(i)
    return s
ss = my_power_sum(1000000)
toc = time.time()
print('used: {:.5f}s'.format(toc-tic))

　　代码是使用numpy做数字运算，并且常常有很多的循环，那么使用Numba就是一个很好的选择。numba不适合字典型变量和一些非numpy的函数，尤其是上面numba不能解析pandas，上面的函数内容在运行时也就无法编译。

5. 用map加速Python函数

import time
 
tic = time.time()
res = [x**2 for x in range(1, 1000000, 3)]
toc = time.time()
print('used: {:.5f}s'.format(toc-tic))
 
# speed up
 
tic = time.time()
res = map(lambda x:x**2, range(1, 1000000, 3))
toc = time.time()
print('used: {:.5f}s'.format(toc-tic))

6.用filter加速Python函数

import time
 
tic = time.time()
res = [x**2 for x in range(1, 1000000, 3) if x%7==0]
toc = time.time()
print('used: {:.5f}s'.format(toc-tic))
 
# speed up
 
tic = time.time()
res = filter(lambda x:x%7==0, range(1, 1000000, 3))
toc = time.time()
print('used: {:.5f}s'.format(toc-tic))

7. 用np.where加速if函数

import time
 
import numpy as np
 
array_a = np.arange(-100000, 100000)
tic = time.time()
relu = np.vectorize(lambda x: x if x>0 else 0)
arr = relu(array_a)
toc = time.time()
print('used: {:.5f}s'.format(toc-tic))
 
# speed up
 
tic = time.time()
relu = lambda x:np.where(x>0, x, 0)
arrr = relu(array_a)
toc = time.time()
print('used: {:.5f}s'.format(toc-tic))

8.多线程thread加速

import time
 
import numpy as np
 
 
tic = time.time()
 
def writefile(i):
    with open(str(i)+'.txt', 'w') as f:
        s = ('hello %d\n'%i) * 10000000
        f.write(s)
for i in range(40,50, 1):
    writefile(i)
 
toc = time.time()
print('used: {:.5f}s'.format(toc-tic))
 
# speed up
import threading
 
tic = time.time()
def writefile(i):
    with open(str(i)+'.txt', 'w') as f:
        s = ('hello %d\n'%i) * 10000000
        f.write(s)
 
thread_list = []
for i in range(10, 20, 1):
    t = threading.Thread(target=writefile, args=(i, ))
    t.setDaemon(True)
    thread_list.append(t)
 
for t in thread_list:
    t.start()
for t in thread_list:
    t.join()
 
toc = time.time()
print('used: {:.5f}s'.format(toc-tic))

9.多线程multiprocessing加速

import time
 
import numpy as np
 
 
tic = time.time()
 
def muchjob(x):
    time.sleep(5)
    return(x**2)
 
ans = [muchjob(i) for i in range(8)]
toc = time.time()
print('used: {:.5f}s'.format(toc-tic))
 
# speed up
import multiprocessing
 
tic = time.time()
 
def muchjob(x):
    time.sleep(5)
    return x**2
pool = multiprocessing.Pool(processes=4)
res = []
for i in range(8):
    res.append(pool.apply_async(muchjob, (i, )))
pool.close()
pool.join()
 
toc = time.time()
print('used: {:.5f}s'.format(toc-tic))