How to use StringIO in Python3?

Roman Shapovalov's code should work in Python 3.x as well as Python 2.6/2.7. Here it is again with the complete example:

import io
import numpy
x = "1 3\n 4.5 8"
numpy.genfromtxt(io.BytesIO(x.encode()))

Output:

array([[ 1. ,  3. ],
       [ 4.5,  8. ]])

Explanation for Python 3.x:

• numpy.genfromtxt takes a byte stream (a file-like object interpreted as bytes instead of Unicode).
• io.BytesIO takes a byte string and returns a byte stream. io.StringIO, on the other hand, would take a Unicode string and and return a Unicode stream.
• x gets assigned a string literal, which in Python 3.x is a Unicode string.
• encode() takes the Unicode string x and makes a byte string out of it, thus giving io.BytesIO a valid argument.

The only difference for Python 2.6/2.7 is that x is a byte string (assuming from __future__ import unicode_literals is not used), and then encode() takes the byte string x and still makes the same byte string out of it. So the result is the same.

Since this is one of SO's most popular questions regarding StringIO, here's some more explanation on the import statements and different Python versions.

Here are the classes which take a string and return a stream:

• io.BytesIO (Python 2.6, 2.7, and 3.x) - Takes a byte string. Returns a byte stream.
• io.StringIO (Python 2.6, 2.7, and 3.x) - Takes a Unicode string. Returns a Unicode stream.
• StringIO.StringIO (Python 2.x) - Takes a byte string or Unicode string. If byte string, returns a byte stream. If Unicode string, returns a Unicode stream.
• cStringIO.StringIO (Python 2.x) - Faster version of StringIO.StringIO, but can't take Unicode strings which contain non-ASCII characters.

Note that StringIO.StringIO is imported as from StringIO import StringIO, then used as StringIO(...). Either that, or you do import StringIO and then use StringIO.StringIO(...). The module name and class name just happen to be the same. It's similar to datetime that way.

What to use, depending on your supported Python versions:

• If you only support Python 3.x: Just use io.BytesIO or io.StringIO depending on what kind of data you're working with.

• If you support both Python 2.6/2.7 and 3.x, or are trying to transition your code from 2.6/2.7 to 3.x: The easiest option is still to use io.BytesIO or io.StringIO. Although StringIO.StringIO is flexible and thus seems preferred for 2.6/2.7, that flexibility could mask bugs that will manifest in 3.x. For example, I had some code which used StringIO.StringIO or io.StringIO depending on Python version, but I was actually passing a byte string, so when I got around to testing it in Python 3.x it failed and had to be fixed.

  Another advantage of using io.StringIO is the support for universal newlines. If you pass the keyword argument newline='' into io.StringIO, it will be able to split lines on any of \n, \r\n, or \r. I found that StringIO.StringIO would trip up on \r in particular.

  Note that if you import BytesIO or StringIO from six, you get StringIO.StringIO in Python 2.x and the appropriate class from io in Python 3.x. If you agree with my previous paragraphs' assessment, this is actually one case where you should avoid six and just import from io instead.

• If you support Python 2.5 or lower and 3.x: You'll need StringIO.StringIO for 2.5 or lower, so you might as well use six. But realize that it's generally very difficult to support both 2.5 and 3.x, so you should consider bumping your lowest supported version to 2.6 if at all possible.

Here is another example for Python 3. It will use two functions to add two numbers and then use CProfile to save the .prof file. Then it will load the save file using pstats.Stats and ```StringIO`` to convert the data to a string for further usage.

main.py

import cProfile
import time
import pstats
from io import StringIO

def add_slow(a, b):
    time.sleep(0.5)
    return a+b

def add_fast(a, b):
    return a+b

prof = cProfile.Profile()

def main_func():
    arr = []
    prof.enable()
    for i in range(10):
        if i%2==0:
            arr.append(add_slow(i,i))
        else:
            arr.append(add_fast(i,i))
    prof.disable()
    #prof.print_stats(sort='time')
    prof.dump_stats("main_funcs.prof")
    return arr

main_func()
stream = StringIO();
stats = pstats.Stats("main_funcs.prof", stream=stream); 
stats.print_stats()
stream.seek(0)
print(16*'=',"RESULTS",16*'=')
print (stream.read())

Usage:

python3 main.py

Output:

================ RESULTS ================
Tue Jul  6 17:36:21 2021    main_funcs.prof

         26 function calls in 2.507 seconds

   Random listing order was used

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
       10    0.000    0.000    0.000    0.000 {method 'append' of 'list' objects}
        5    2.507    0.501    2.507    0.501 {built-in method time.sleep}
        5    0.000    0.000    2.507    0.501 profiler.py:39(add_slow)
        5    0.000    0.000    0.000    0.000 profiler.py:43(add_fast)
        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}

Comments: We can observe that in the above code, the time.sleep function is taking about 2.507 seconds.

I hope this will meet your requirement

import PyPDF4
import io

pdfFile = open(r'test.pdf', 'rb')
pdfReader = PyPDF4.PdfFileReader(pdfFile)
pageObj = pdfReader.getPage(1)
pagetext = pageObj.extractText()

for line in io.StringIO(pagetext):
    print(line)