Numpy 数组处理比列表处理 Python 花费更多时间

Question

我正在尝试比较python列表和numpy数组的处理时间。我知道 numpy 数组比 python 列表快的事实，但是当我实际检查时，我得到的列表比 numpy 数组快。

这是我的代码：

import numpy
from datetime import datetime
def pythonsum(n):  
    '''
    This function  calculates the sum of two python list
    ''' 
    a = list(range(n))
    b = list(range(n))
    c = []
    total = 0
    for i in range(len(a)):       
        a[i] = i ** 2
        b[i] = i ** 3       
        c.append(a[i] + b[i])
    return c
def numpysum(n):
    '''
    This function  calculates the sum of two numpy array
    ''' 
    a  = numpy.arange(n) ** 2
    b = numpy.arange(n) ** 3
    c = a + b
    return c 
if __name__ == "__main__":
    n = int(input("enter the range"))
    start = datetime.now()
    result = pythonsum(n)
    delta = datetime.now()-start
    print("time required by pythonsum is",delta.microseconds)
    start = datetime.now()
    result1 = numpysum(n)
    delta = datetime.now()-start
    print("time required by numpysum is",delta.microseconds)
    delta = datetime.now()-start
    print("time required by numpysum is",delta.microseconds)

输出：

In [32]: run numpy_practice.py
enter the range7
time required by pythonsum is 0
time required by numpysum is 1001

Answer 1

将样板下面的代码更改为此代码，并改用timeit模块。此代码允许我多次执行函数并返回平均值和标准偏差执行时间。

if __name__ == "__main__":
    import timeit
    n = int(input("enter the range"))
    setup = """
import numpy as np
from __main__ import numpysum,pythonsum
n = {iterations}
    """.format(iterations=n)
    npex = numpy.array(timeit.repeat('numpysum(n)',setup=setup,repeat=100,number=1000))
    pyex = numpy.array(timeit.repeat('pythonsum(n)',setup=setup,repeat=100,number=1000))
    print("Numpy Execution Time, mean={m},std={sd}".format(m=npex.mean(),sd=npex.std(0)))
    print("Python Execution Time, mean={m},std={sd}".format(m=pyex.mean(), sd=pyex.std(0)))

通过 n = 100 进行测试，很明显 numpy 要快得多

enter the range100
Numpy Execution Time, mean=0.00482892623162404,std=0.0005752600215192671
Python Execution Time, mean=0.1037349765653833,std=0.004933817536363718