原生python与numpy数组向量相加效率对比

原生python与numpy数组向量相加效率对比

计算一个数据元素的平方与立方之和

1、原生python

#向量相加 - 原生Python
def pythonvector(n):
    a = range(n)
    b = range(n)
    c = []
    for i in range(len(a)):
        a[i] = i ** 2
        b[i] = i ** 3
        c.append(a[i] + b[i])
    return c

2、numpy实现

#向量相加 - numpy
import numpy

def numpyvector(n):
    a = numpy.arange(n) ** 2
    b = numpy.arange(n) ** 3
    c = a + b
    return c

3、效率对比

#效率比较
import sys
from datetime import datetime

n = 1000

start = datetime.now()
c = pythonvector(n)
end = datetime.now() -start

print "The last 3 elements of the result ",c[-3:]
print "pythonvector elapsed time ",end.microseconds

start = datetime.now()
c = numpyvector(n)
end = datetime.now() -start

print "The last 3 elements of the result ",c[-3:]
print "numpyvector elapsed time ",end.microseconds

结果是一样的，效率上numpy远快于原生的python,并且写法上更简洁。