Python数据分析学习（一）：Numpy与纯Python计算向量加法速度比较

import sys

from datetime import datetime

import numpy as np

def numpysum(n):

    a = np.arange(n) ** 2

    b = np.arange(n) ** 3

    c = a + b

    return c

def pythonsum(n):

    a = list(range(n))

    b = list(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)

python vectorsum.py 100000

The last 2 elements of the sum [999950000799996, 999980000100000]

PythonSum elapsed time in microseconds  91446

The last 2 elements of the sum [999950000799996 999980000100000]

NumPySum elapsed time in microseconds  2824

python vectorsum.py 200000

The last 2 elements of the sum [7999800001599996, 7999920000200000]

PythonSum elapsed time in microseconds  178237

The last 2 elements of the sum [7999800001599996 7999920000200000]

NumPySum elapsed time in microseconds  6453

python vectorsum.py 300000

The last 2 elements of the sum [26999550002399996, 26999820000300000]

PythonSum elapsed time in microseconds 264677

The last 2 elements of the sum [26999550002399996 26999820000300000]

NumPySum elapsed time in microseconds 9951