Converting string to int is too slow

Question

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.

My code snippet is below:

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")

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

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

the runtime rises to about 3.42 seconds.

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

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

Answer 1

If the file is in OS cache then parsing the file takes milliseconds on my machine:

name                                 time ratio comment
read_read                        145 usec  1.00 big.txt
read_readtxt                    2.07 msec 14.29 big.txt
read_readlines                  4.94 msec 34.11 big.txt
read_james_otigo                29.3 msec 201.88 big.txt
read_james_otigo_with_int_float 82.9 msec 571.70 big.txt
read_map_local                  93.1 msec 642.23 big.txt
read_map                        95.6 msec 659.57 big.txt
read_numpy_loadtxt               321 msec 2213.66 big.txt

Where the read_*() functions are:

def read_read(filename):
    with open(filename, 'rb') as file:
        data = file.read()

def read_readtxt(filename):
    with open(filename, 'rU') as file:
        text = file.read()

def read_readlines(filename):
    with open(filename, 'rU') as file:
        lines = file.readlines()

def read_james_otigo(filename):
    file = open (filename).readlines()
    for line in file[1:]:
        label1, label2, edge = line.strip().split()

def read_james_otigo_with_int_float(filename):
    file = open (filename).readlines()
    for line in file[1:]:
        label1, label2, edge = line.strip().split()
        label1 = int(label1); label2 = int(label2); edge = float(edge)

def read_map(filename):
    with open(filename) as file:
        L = [(int(l1), int(l2), float(edge))
             for line in file
             for l1, l2, edge in [line.split()] if line.strip()]

def read_map_local(filename, _i=int, _f=float):
    with open(filename) as file:
        L = [(_i(l1), _i(l2), _f(edge))
             for line in file
             for l1, l2, edge in [line.split()] if line.strip()]

import numpy as np

def read_numpy_loadtxt(filename):
    a = np.loadtxt('big.txt', dtype=[('label1', 'i'),
                                     ('label2', 'i'),
                                     ('edge', 'f')])

And big.txt is generated using:

#!/usr/bin/env python
import numpy as np

n = 50000
a = np.random.random_integers(low=0, high=1<<10, size=2*n).reshape(-1, 2)
np.savetxt('big.txt', np.c_[a, np.random.rand(n)], fmt='%i %i %s')

It produces 50000 lines:

150 952 0.355243621018
582 98 0.227592557278
478 409 0.546382780254
46 879 0.177980983303
...

To reproduce results, download the code and run:

# write big.txt
python generate-file.py
# run benchmark
python read-array.py