将字符串转换为 int 太慢 [英] Converting string to int is too slow

问题描述

我有一个程序，每行读取 3 个字符串 50000.然后它会做其他事情.读取文件并转换为整数的部分占用了总运行时间的 80%.

I've got a program that reads in 3 strings per line for 50000. It then does other things. The part that reads the file and converts to integers is taking 80% of the total running time.

我的代码片段如下:

import time
file = open ('E:/temp/edges_big.txt').readlines()
start_time = time.time()
for line in file[1:]:
    label1, label2, edge = line.strip().split()
    # label1 = int(label1); label2 = int(label2); edge = float(edge)
    # Rest of the loop deleted
print ('processing file took ', time.time() - start_time, "seconds")

上述过程大约需要 0.84 秒.现在，当我取消注释该行

The above takes about 0.84 seconds. Now, when I uncomment the line

label1 = int(label1);label2 = int(label2);edge = float(edge)

运行时间上升到大约 3.42 秒.

the runtime rises to about 3.42 seconds.

输入文件格式为:str1 str2 str3 per line

The input file is in the form: str1 str2 str3 per line

函数 int() 和 float() 有那么慢吗?我该如何优化?

Are the functions int() and float() that slow? How could I optimize this?

推荐答案

我能够获得与您几乎相同的时间安排.我认为问题出在我的代码上:

I'm able to get almost same timings as yours. I think the problem was with my code that was doing the timings:

read_james_otigo                  40 msec big.txt
read_james_otigo_with_int_float  116 msec big.txt
read_map                         134 msec big.txt
read_map_local                   131 msec big.txt
read_numpy_loadtxt               400 msec big.txt
read_read                        488 usec big.txt
read_readlines                  9.24 msec big.txt
read_readtxt                    4.36 msec big.txt

name                                 time ratio comment
read_read                        488 usec  1.00 big.txt
read_readtxt                    4.36 msec  8.95 big.txt
read_readlines                  9.24 msec 18.95 big.txt
read_james_otigo                  40 msec 82.13 big.txt
read_james_otigo_with_int_float  116 msec 238.64 big.txt
read_map_local                   131 msec 268.05 big.txt
read_map                         134 msec 274.87 big.txt
read_numpy_loadtxt               400 msec 819.42 big.txt


read_james_otigo                39.4 msec big.txt
read_readtxt                    4.37 msec big.txt
read_readlines                  9.21 msec big.txt
read_map_local                   131 msec big.txt
read_james_otigo_with_int_float  116 msec big.txt
read_map                         134 msec big.txt
read_read                        487 usec big.txt
read_numpy_loadtxt               398 msec big.txt

name                                 time ratio comment
read_read                        487 usec  1.00 big.txt
read_readtxt                    4.37 msec  8.96 big.txt
read_readlines                  9.21 msec 18.90 big.txt
read_james_otigo                39.4 msec 80.81 big.txt
read_james_otigo_with_int_float  116 msec 238.51 big.txt
read_map_local                   131 msec 268.84 big.txt
read_map                         134 msec 275.11 big.txt
read_numpy_loadtxt               398 msec 816.71 big.txt

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