import time
def foo(x,y):
  tt = time.time()
  s = 0
  for i in range(x,y):
    s += i
  print('Time used: {} sec'.format(time.time()-tt))
  return s
print(foo(1,100000000))

Time used: 6.779874801635742 sec
4999999950000000

import numba as nb
from numba import jit
@jit('f8(f8[:])')
def sum1d(array):
 s = 0.0
 n = array.shape[0]
 for i in range(n):
  s += array[i]
 return s
import numpy as np
array = np.random.random(10000)
%timeit sum1d(array)
%timeit np.sum(array)
%timeit sum(array)
10000 loops, best of 3: 38.9 us per loop
10000 loops, best of 3: 32.3 us per loop
100 loops, best of 3: 12.4 ms per loop

#!/usr/bin/env python
# encoding: utf-8
'''
@author: Victor
@Company：华中科技大学电气学院聚变与等离子研究所
@version: V1.0
@contact: 1650996069@qq.com 2018--2020
@software: PyCharm2018
@file: quickPython3.py
@time: 2018/9/21 20:54
@desc:使用numba的jit是python代码运行速度提高100倍左右
'''
'''平常运行'''
import time
def add(x,y):
    tt = time.time()
    s = 0
    for i in range(x,y):
        s += i
    print('The time used: {} seconds'.format(time.time()-tt))
    return s
 
add(1,100000000)
##########结果###############
# D:\Python3\python.exe D:/Pycharm2018Works/InsteringPython3/SomeBasics/quickPython3.py
# The time used: 6.712835788726807 seconds
# Process finished with exit code 0

'''调用numba运行'''
import time
from numba import jit
@jit
def add(x,y):
    tt = time.time()
    s = 0
    for i in range(x,y):
        s += i
    print('The time used: {} seconds'.format(time.time()-tt))
    return s
 
add(1,100000000)
##########结果###############
# D:\Python3\python.exe D:/Pycharm2018Works/InsteringPython3/SomeBasics/quickPython3.py
# The time used: 0.06396007537841797 seconds
# 
# Process finished with exit code 0