【代码】<python数据分析> 向量加法比较

最新推荐文章于 2024-06-21 20:55:42 发布

原创最新推荐文章于 2024-06-21 20:55:42 发布 · 840 阅读

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本文通过对比纯Python与NumPy库在向量加法运算上的性能表现，展示了使用NumPy可以显著提高数值计算的速度。实验结果显示，在处理大量数据时，NumPy的运行速度远超纯Python实现。

对比pure python和numpy对向量加法的运算效率：

<pre name="code" class="python">import numpy
import sys
from datetime import datetime

def pythonsum(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



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

#size = int(sys.argv[1])

size = long(40000)#numpy的arange默认32位，计算大数的时候会造成溢出

start = datetime.now()
c = pythonsum(size)
#print c
delta = datetime.now()-start
print 'The last 2 element of the sum', c[-2:]
print 'PythonSum elapsed time in microseconds', delta.microseconds
print type(c[-1])



start = datetime.now()
c = numpysum(size)
#print c
delta = datetime.now()-start
print 'The last 2 element of the sum', c[-2:]
print 'PythonSum elapsed time in microseconds', delta.microseconds
print type(c[-1])

输出

<pre name="code" class="python">The last 2 element of the sum [63992000319996L, 63996800040000L]
PythonSum elapsed time in microseconds 72000
<type 'long'>
The last 2 element of the sum [63992000319996 63996800040000]
PythonSum elapsed time in microseconds 4000
<type 'numpy.int64'>