What are the five python speed-up skills?

What are the five python speed-up techniques? in view of this problem, this article introduces the corresponding analysis and solutions in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible way.

1. Skip the beginning of the iterative object 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) 2, avoid data replication # not recommended Code time: 6.5 seconds 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 () # recommended Code time: 4.8s def main (): size = 10000 for _ in range (size): value = range (size) square_list = [x * x for x in value] # avoid meaningless copying 3, avoid variable intermediate variable # not recommended Code time: 0.07s def main (): size = 1000000 for _ in range (size): a = 3b = 5 temp = an a = b = temp main () # recommended writing Code time: 0.06s def main (): size = 1000000 for _ in range (size): a = 3b = 5a, b = b, a # without intermediate variable main () 4, loop optimization # is not recommended. Code time: 6.7 seconds 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 loop instead of while loop sum_ + = I return sum_ def main (): size = 10000 for _ in range (size): sum_ = computeSum (size) main ()

Implicit for loop replaces explicit for loop

# recommended writing method. Code time: 1.7s def computeSum (size: int)-> int: return sum (range (size)) # implicit for loop instead of explicit for loop def main (): size = 10000 for _ in range (size): sum = computeSum (size) main () 5, using numba.jit# recommended writing. Code time: 0.62 seconds # numba can compile the Python function JIT into machine code execution, greatly improving the speed of code execution. Import numba @ numba.jitdef 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 () the answers to the questions about the five python speed-up techniques are shared here. I hope the above content can be of some help to you, if you still have a lot of questions unsolved. You can follow the industry information channel for more related knowledge.