Python学习之NumPy篇

安装NumPy

使用anaconda进行安装，

pip install numpy

即可。

案例

编写一个求和函数，使得ci=ai^2+bi^3.

不使用numpy的程序如下：

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

使用numpy的程序较为简洁：

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

运行效率

对上述两个函数测试其运行效率，代码如下：

#!/usr/bin/env/python
import sys
from datetime import datetime
import numpy as np
"""
This program demonstrates vector addition the Python way.
Run from the command line as follows
python vectorsum.py n
where n is an integer that specifies the size of the vectors.
The first vector to be added contains the squares of 0 up to n.
The second vector contains the cubes of 0 up to n.
The program prints the last 2 elements of the sum and the elapsed
time.
"""
def numpysum(n):
a = np.arange(n) ** 2
b = np.arange(n) ** 3
c = a + b
return c
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
size = int(sys.argv[1])
start = datetime.now()
c = pythonsum(size)
delta = datetime.now() - start
print "The last 2 elements of the sum", c[-2:]
print "PythonSum elapsed time in microseconds", delta.microseconds
start = datetime.now()
c = numpysum(size)
delta = datetime.now() - start
print "The last 2 elements of the sum", c[-2:]
print "NumPySum elapsed time in microseconds", delta.microseconds

测试1000维，2000维，4000维的数组，输出结果如下：

$ python vectorsum.py 1000
The last 2 elements of the sum [995007996, 998001000]
PythonSum elapsed time in microseconds 707
The last 2 elements of the sum [995007996 998001000]
NumPySum elapsed time in microseconds 171
$ python vectorsum.py 2000
The last 2 elements of the sum [7980015996, 7992002000]
PythonSum elapsed time in microseconds 1420
The last 2 elements of the sum [7980015996 7992002000]
NumPySum elapsed time in microseconds 168
$ python vectorsum.py 4000
The last 2 elements of the sum [63920031996, 63968004000]
PythonSum elapsed time in microseconds 2829
The last 2 elements of the sum [63920031996 63968004000]
NumPySum elapsed time in microseconds 274

可见使用numpy的array操作确实优于python的list操作，尽量使用numpy的array，可以带来性能的提升。