用python将多个返回值写入文件

Question

I need to write the returns of each function in to a text file. I have used the code below. However, when I have multiple functions, this process of using file1.write() consumes a lot of time and lines. Is there any smarter method to write the outputs?

def function1():
    #some operation
    return "Status 1" + "\n\n"

def function2():
    #some operation
    return "Status 2" + "\n\n"

def function3():
    #some operation
    return "Status 1" + "\n\n"

if __name__ == '__main__':

    file1 = open("test.txt","w")

    output1 = function1()
    file1.write(output1)

    output2 = function2()
    file1.write(output2)

    output3 = function3()
    file1.write(output3)

    file1.close()

I have modified the code since many are confused with my question.

Answer 1

This code can be made shorter (and possibly clearer) by replacing everything below if by:

with open("text.txt","w") as file1:
    for func in (addition, subtraction, multiplication):
        file1.write(func(5,6))

but this won't improve execution time.

Answer 2

It looks like you're trying not to repeat yourself so much. You can take advantage of the fact that functions are "first class objects" in order to iterate over them. Also, you can take advantage of the features of the functools module to reuse the basic structure of nearly identical functions. The following code will produce identical output to your example with far less repetition of code:

import functools

def base_func(func_id):
    return 'status of {}\n\n'.format(func_id)

functions = [functools.partial(base_func, i+1) for i in range(3)]

if __name__ == '__main__':
    with open('test.txt','w') as file1:
        for func in functions:
            file1.write(func())

However, it will almost certainly not run execute any faster, but given your example I'd hardly thing computing performance should be of great concern.

Answer 3

The fastest text writer I've come across is Pandas, which I usually use with NumPy. So it would look something like this:

import pandas as pd
import numpy as np
a=np.zeros(len(results)) #if you know the result size
#now store all your results in a
a[0]=add(5,6)
a[1]=sub(5,6)
a[2]=multiply(5,6)
#continue
b=pd.DataFrame(a)
b.to_csv('test.csv', header=False, index=False, sep=',')

Hope you get the idea - sep can be any delimiter. If you need a multidimensional array you can do that too.