Regarding speeding up python (memo)

speed01.py

import time


def test01():
    time01 = time.time()
    str1 = ""
    for i in range(1000000):
        str1 += str(i)
    time02 = time.time()
    print("Time taken in test01:{:.6f}s".format(time02 - time01))


def test02():
    time01 = time.time()
    lst1 = []
    for i in range(1000000):
        lst1.append(str(i))
    str1 = "".join(lst1)
    time02 = time.time()
    print("Time taken in test02:{:.6f}s".format(time02 - time01))


test01()
test02()

Execution result:
Time taken in test01: 1.515000s
Time taken in test02: 0.330000s

• The speed is faster when using join.

speed02.py

import time


def test01():
    time01 = time.time()
    n = 0
    sum = 0
    while (n < 10000000):
        sum += n
        n += 1
    time02 = time.time()
    print("Time taken in test01:{:.6f}s".format(time02 - time01))


def test02():
    time01 = time.time()
    sum = 0
    for n in range(10000000):
        sum += n
    time02 = time.time()
    print("Time taken in test02:{:.6f}s".format(time02 - time01))


test01()
test02()

Execution result:
Time taken in test01: 0.970000s
Time taken in test02: 0.559000s

• The speed is faster when using for than while.

speed03.py

from functools import lru_cache
import time


def fibonacci_number1(n):
    return n if n < 2 else fibonacci_number1(n - 1) + fibonacci_number1(n - 2)


@lru_cache()
def fibonacci_number2(n):
    return n if n < 2 else fibonacci_number2(n - 1) + fibonacci_number2(n - 2)


t1 = time.time()
fibonacci_number1(35)
t2 = time.time()
fibonacci_number2(35)
t3 = time.time()

print("lru_Time required when not using cache:{:.6f}s".format(t2 - t1))
print("lru_Time required when using cache:{:.6f}s".format(t3 - t2))

Execution result:
lru_Time required when not using cache: 3.699000s
lru_Time required when using cache: 0.000000s

• In some cases, using functools.lru_cache is overwhelmingly faster.

speed04.py

import time
from math import sqrt


def test01():
    time01 = time.time()
    for i in range(10000000):
        sqrt(33)
    time02 = time.time()
    print("Time taken in test01:{:.6f}s".format(time02 - time01))


def test02():
    time01 = time.time()
    sqrt_in = sqrt
    for i in range(10000000):
        sqrt_in(33)
    time02 = time.time()
    print("Time taken in test02:{:.6f}s".format(time02 - time01))


test01()
test02()

Execution result:
Time taken in test01: 1.631000s
Time taken in test02: 1.533000s

• Local speed is faster

speed05.py

import time


def test01():
    time01 = time.time()
    lst = []
    for i in range(10000000):
        lst.append(i)
    time02 = time.time()
    print("Time taken in test01:{:.6f}s".format(time02 - time01))


def test02():
    time01 = time.time()
    lst = []
    append = lst.append
    for i in range(10000000):
        append(i)  # lst.Speed can be improved by using append instead of append
    time02 = time.time()
    print("Time taken in test02:{:.6f}s".format(time02 - time01))


def test03():
    time01 = time.time()
    lst = [i for i in range(10000000)]
    time02 = time.time()
    print("Time taken in test03:{:.6f}s".format(time02 - time01))


test01()
test02()
test03()

Execution result:
Time taken in test01: 1.023000s
Time taken in test02: 0.686000s
Time taken in test03: 0.542000s

• List comprehension is faster

speed06.py

import time
from numba import jit


def test01(x, y):
    time01 = time.time()
    sum = 0
    for i in range(x, y):
        sum += i
    time02 = time.time()
    print("Time taken in test01:{:.6f}s".format(time02 - time01))
    return sum


@jit
def test02(x, y):
    time01 = time.time()
    sum = 0
    for i in range(x, y):
        sum += i
    time02 = time.time()
    print("Time taken in test02:{:.6f}s".format(time02 - time01))
    return sum


sum1 = test01(1, 100000000)
sum2 = test02(1, 100000000)

Execution result:
Time taken in test01: 5.534000s
Time taken in test02: 0.040000s

• JIT compiler library Numba can be executed 100 times faster, It seems that you can get performance like C language

• You need to install Numba In addition, when python is installed with anaconda, it seems that Numba is included from the beginning.