分享5个python提速技巧,速度瞬间提上来了

目录

• 1、跳过迭代对象的开头
• 2、避免数据复制
• 3、避免变量中间变量
• 4、循环优化
• 5、使用numba.jit

string_from_file = """  
// Wooden: ...  
// LaoLi: ...  
//  
// Whole: ...  
Wooden LaoLi... 
 """ 
import itertools  
for line in itertools.dropwhile(lambda line: line.startswith("//"), string_from_file.split(" ")):
    print(line) 

# 不推荐写法，代码耗时：6.5秒
def main():
    size = 10000
    for _ in range(size):
        value = range(size)
        value_list = [x for x in value]
        square_list = [x * x for x in value_list]
 
main()

# 推荐写法，代码耗时：4.8秒
def main():
    size = 10000
    for _ in range(size):
        value = range(size)
        square_list = [x * x for x in value]  # 避免无意义的复制

# 不推荐写法，代码耗时：0.07秒
def main():
    size = 1000000
    for _ in range(size):
        a = 3
        b = 5
        temp = a
        a = b
        b = temp
 
main()

# 推荐写法，代码耗时：0.06秒
def main():
    size = 1000000
    for _ in range(size):
        a = 3
        b = 5
        a, b = b, a  # 不借助中间变量
 
main()

# 不推荐写法。代码耗时：6.7秒
def computeSum(size: int) -> int:
    sum_ = 0
    i = 0
    while i < size:
        sum_ += i
        i += 1
    return sum_
 
def main():
    size = 10000
    for _ in range(size):
        sum_ = computeSum(size)
 
main()

# 推荐写法。代码耗时：4.3秒
def computeSum(size: int) -> int:
    sum_ = 0
    for i in range(size):  # for 循环代替 while 循环
        sum_ += i
    return sum_
 
def main():
    size = 10000
    for _ in range(size):
        sum_ = computeSum(size)
 
main()

# 推荐写法。代码耗时：1.7秒
def computeSum(size: int) -> int:
    return sum(range(size))  # 隐式 for 循环代替显式 for 循环
 
def main():
    size = 10000
    for _ in range(size):
        sum = computeSum(size)
 
main()

# 推荐写法。代码耗时：0.62秒
# numba可以将 Python 函数 JIT 编译为机器码执行，大大提高代码运行速度。
import numba
 
@numba.jit
def computeSum(size: float) -> int:
    sum = 0
    for i in range(size):
        sum += i
    return sum
 
def main():
    size = 10000
    for _ in range(size):
        sum = computeSum(size)
 
main()