We Keep Coding

PYTHON: distinguishing between end of file and blank line in file.readline() [duplicate]

I want to iterate over each line of an entire file. One way to do this is by reading the entire file, saving it to a list, then going over the line of interest. This method uses a lot of memory, so I am looking for an alternative.
My code so far:
for each_line in fileinput.input(input_file):
do_something(each_line)
for each_line_again in fileinput.input(input_file):
do_something(each_line_again)
Executing this code gives an error message: device active.
Any suggestions?
The purpose is to calculate pair-wise string similarity, meaning for each line in file, I want to calculate the Levenshtein distance with every other line.
Nov. 2022 Edit: A related question that was asked 8 months after this question has many useful answers and comments. To get a deeper understanding of python logic, do also read this related question How should I read a file line-by-line in Python?

The correct, fully Pythonic way to read a file is the following:
with open(...) as f:
for line in f:
# Do something with 'line'
The with statement handles opening and closing the file, including if an exception is raised in the inner block. The for line in f treats the file object f as an iterable, which automatically uses buffered I/O and memory management so you don't have to worry about large files.
There should be one -- and preferably only one -- obvious way to do it.

Two memory efficient ways in ranked order (first is best) -
use of with - supported from python 2.5 and above
use of yield if you really want to have control over how much to read
1. use of with
with is the nice and efficient pythonic way to read large files. advantages - 1) file object is automatically closed after exiting from with execution block. 2) exception handling inside the with block. 3) memory for loop iterates through the f file object line by line. internally it does buffered IO (to optimized on costly IO operations) and memory management.
with open("x.txt") as f:
for line in f:
do something with data
2. use of yield
Sometimes one might want more fine-grained control over how much to read in each iteration. In that case use iter & yield. Note with this method one explicitly needs close the file at the end.
def readInChunks(fileObj, chunkSize=2048):
"""
Lazy function to read a file piece by piece.
Default chunk size: 2kB.
"""
while True:
data = fileObj.read(chunkSize)
if not data:
break
yield data
f = open('bigFile')
for chunk in readInChunks(f):
do_something(chunk)
f.close()
Pitfalls and for the sake of completeness - below methods are not as good or not as elegant for reading large files but please read to get rounded understanding.
In Python, the most common way to read lines from a file is to do the following:
for line in open('myfile','r').readlines():
do_something(line)
When this is done, however, the readlines() function (same applies for read() function) loads the entire file into memory, then iterates over it. A slightly better approach (the first mentioned two methods above are the best) for large files is to use the fileinput module, as follows:
import fileinput
for line in fileinput.input(['myfile']):
do_something(line)
the fileinput.input() call reads lines sequentially, but doesn't keep them in memory after they've been read or even simply so this, since file in python is iterable.
References
Python with statement

To strip newlines:
with open(file_path, 'rU') as f:
for line_terminated in f:
line = line_terminated.rstrip('\n')
...
With universal newline support all text file lines will seem to be terminated with '\n', whatever the terminators in the file, '\r', '\n', or '\r\n'.
EDIT - To specify universal newline support:
Python 2 on Unix - open(file_path, mode='rU') - required [thanks #Dave]
Python 2 on Windows - open(file_path, mode='rU') - optional
Python 3 - open(file_path, newline=None) - optional
The newline parameter is only supported in Python 3 and defaults to None. The mode parameter defaults to 'r' in all cases. The U is deprecated in Python 3. In Python 2 on Windows some other mechanism appears to translate \r\n to \n.
Docs:
open() for Python 2
open() for Python 3
To preserve native line terminators:
with open(file_path, 'rb') as f:
with line_native_terminated in f:
...
Binary mode can still parse the file into lines with in. Each line will have whatever terminators it has in the file.
Thanks to #katrielalex's answer, Python's open() doc, and iPython experiments.

this is a possible way of reading a file in python:
f = open(input_file)
for line in f:
do_stuff(line)
f.close()
it does not allocate a full list. It iterates over the lines.

Some context up front as to where I am coming from. Code snippets are at the end.
When I can, I prefer to use an open source tool like H2O to do super high performance parallel CSV file reads, but this tool is limited in feature set. I end up writing a lot of code to create data science pipelines before feeding to H2O cluster for the supervised learning proper.
I have been reading files like 8GB HIGGS dataset from UCI repo and even 40GB CSV files for data science purposes significantly faster by adding lots of parallelism with the multiprocessing library's pool object and map function. For example clustering with nearest neighbor searches and also DBSCAN and Markov clustering algorithms requires some parallel programming finesse to bypass some seriously challenging memory and wall clock time problems.
I usually like to break the file row-wise into parts using gnu tools first and then glob-filemask them all to find and read them in parallel in the python program. I use something like 1000+ partial files commonly. Doing these tricks helps immensely with processing speed and memory limits.
The pandas dataframe.read_csv is single threaded so you can do these tricks to make pandas quite faster by running a map() for parallel execution. You can use htop to see that with plain old sequential pandas dataframe.read_csv, 100% cpu on just one core is the actual bottleneck in pd.read_csv, not the disk at all.
I should add I'm using an SSD on fast video card bus, not a spinning HD on SATA6 bus, plus 16 CPU cores.
Also, another technique that I discovered works great in some applications is parallel CSV file reads all within one giant file, starting each worker at different offset into the file, rather than pre-splitting one big file into many part files. Use python's file seek() and tell() in each parallel worker to read the big text file in strips, at different byte offset start-byte and end-byte locations in the big file, all at the same time concurrently. You can do a regex findall on the bytes, and return the count of linefeeds. This is a partial sum. Finally sum up the partial sums to get the global sum when the map function returns after the workers finished.
Following is some example benchmarks using the parallel byte offset trick:
I use 2 files: HIGGS.csv is 8 GB. It is from the UCI machine learning repository. all_bin .csv is 40.4 GB and is from my current project.
I use 2 programs: GNU wc program which comes with Linux, and the pure python fastread.py program which I developed.
HP-Z820:/mnt/fastssd/fast_file_reader$ ls -l /mnt/fastssd/nzv/HIGGS.csv
-rw-rw-r-- 1 8035497980 Jan 24 16:00 /mnt/fastssd/nzv/HIGGS.csv
HP-Z820:/mnt/fastssd$ ls -l all_bin.csv
-rw-rw-r-- 1 40412077758 Feb 2 09:00 all_bin.csv
ga#ga-HP-Z820:/mnt/fastssd$ time python fastread.py --fileName="all_bin.csv" --numProcesses=32 --balanceFactor=2
2367496
real 0m8.920s
user 1m30.056s
sys 2m38.744s
In [1]: 40412077758. / 8.92
Out[1]: 4530501990.807175
That’s some 4.5 GB/s, or 45 Gb/s, file slurping speed. That ain’t no spinning hard disk, my friend. That’s actually a Samsung Pro 950 SSD.
Below is the speed benchmark for the same file being line-counted by gnu wc, a pure C compiled program.
What is cool is you can see my pure python program essentially matched the speed of the gnu wc compiled C program in this case. Python is interpreted but C is compiled, so this is a pretty interesting feat of speed, I think you would agree. Of course, wc really needs to be changed to a parallel program, and then it would really beat the socks off my python program. But as it stands today, gnu wc is just a sequential program. You do what you can, and python can do parallel today. Cython compiling might be able to help me (for some other time). Also memory mapped files was not explored yet.
HP-Z820:/mnt/fastssd$ time wc -l all_bin.csv
2367496 all_bin.csv
real 0m8.807s
user 0m1.168s
sys 0m7.636s
HP-Z820:/mnt/fastssd/fast_file_reader$ time python fastread.py --fileName="HIGGS.csv" --numProcesses=16 --balanceFactor=2
11000000
real 0m2.257s
user 0m12.088s
sys 0m20.512s
HP-Z820:/mnt/fastssd/fast_file_reader$ time wc -l HIGGS.csv
11000000 HIGGS.csv
real 0m1.820s
user 0m0.364s
sys 0m1.456s
Conclusion: The speed is good for a pure python program compared to a C program. However, it’s not good enough to use the pure python program over the C program, at least for linecounting purpose. Generally the technique can be used for other file processing, so this python code is still good.
Question: Does compiling the regex just one time and passing it to all workers will improve speed? Answer: Regex pre-compiling does NOT help in this application. I suppose the reason is that the overhead of process serialization and creation for all the workers is dominating.
One more thing.
Does parallel CSV file reading even help? Is the disk the bottleneck, or is it the CPU? Many so-called top-rated answers on stackoverflow contain the common dev wisdom that you only need one thread to read a file, best you can do, they say. Are they sure, though?
Let’s find out:
HP-Z820:/mnt/fastssd/fast_file_reader$ time python fastread.py --fileName="HIGGS.csv" --numProcesses=16 --balanceFactor=2
11000000
real 0m2.256s
user 0m10.696s
sys 0m19.952s
HP-Z820:/mnt/fastssd/fast_file_reader$ time python fastread.py --fileName="HIGGS.csv" --numProcesses=1 --balanceFactor=1
11000000
real 0m17.380s
user 0m11.124s
sys 0m6.272s
Oh yes, yes it does. Parallel file reading works quite well. Well there you go!
Ps. In case some of you wanted to know, what if the balanceFactor was 2 when using a single worker process? Well, it’s horrible:
HP-Z820:/mnt/fastssd/fast_file_reader$ time python fastread.py --fileName="HIGGS.csv" --numProcesses=1 --balanceFactor=2
11000000
real 1m37.077s
user 0m12.432s
sys 1m24.700s
Key parts of the fastread.py python program:
fileBytes = stat(fileName).st_size # Read quickly from OS how many bytes are in a text file
startByte, endByte = PartitionDataToWorkers(workers=numProcesses, items=fileBytes, balanceFactor=balanceFactor)
p = Pool(numProcesses)
partialSum = p.starmap(ReadFileSegment, zip(startByte, endByte, repeat(fileName))) # startByte is already a list. fileName is made into a same-length list of duplicates values.
globalSum = sum(partialSum)
print(globalSum)
def ReadFileSegment(startByte, endByte, fileName, searchChar='\n'): # counts number of searchChar appearing in the byte range
with open(fileName, 'r') as f:
f.seek(startByte-1) # seek is initially at byte 0 and then moves forward the specified amount, so seek(5) points at the 6th byte.
bytes = f.read(endByte - startByte + 1)
cnt = len(re.findall(searchChar, bytes)) # findall with implicit compiling runs just as fast here as re.compile once + re.finditer many times.
return cnt
The def for PartitionDataToWorkers is just ordinary sequential code. I left it out in case someone else wants to get some practice on what parallel programming is like. I gave away for free the harder parts: the tested and working parallel code, for your learning benefit.
Thanks to: The open-source H2O project, by Arno and Cliff and the H2O staff for their great software and instructional videos, which have provided me the inspiration for this pure python high performance parallel byte offset reader as shown above. H2O does parallel file reading using java, is callable by python and R programs, and is crazy fast, faster than anything on the planet at reading big CSV files.

Katrielalex provided the way to open & read one file.
However the way your algorithm goes it reads the whole file for each line of the file. That means the overall amount of reading a file - and computing the Levenshtein distance - will be done N*N if N is the amount of lines in the file. Since you're concerned about file size and don't want to keep it in memory, I am concerned about the resulting quadratic runtime. Your algorithm is in the O(n^2) class of algorithms which often can be improved with specialization.
I suspect that you already know the tradeoff of memory versus runtime here, but maybe you would want to investigate if there's an efficient way to compute multiple Levenshtein distances in parallel. If so it would be interesting to share your solution here.
How many lines do your files have, and on what kind of machine (mem & cpu power) does your algorithm have to run, and what's the tolerated runtime?
Code would look like:
with f_outer as open(input_file, 'r'):
for line_outer in f_outer:
with f_inner as open(input_file, 'r'):
for line_inner in f_inner:
compute_distance(line_outer, line_inner)
But the questions are how do you store the distances (matrix?) and can you gain an advantage of preparing e.g. the outer_line for processing, or caching some intermediate results for reuse.

Need to frequently read a large file from last position reading ?
I have created a script used to cut an Apache access.log file several times a day.
So I needed to set a position cursor on last line parsed during last execution.
To this end, I used file.seek() and file.seek() methods which allows the storage of the cursor in file.
My code :
ENCODING = "utf8"
CURRENT_FILE_DIR = os.path.dirname(os.path.abspath(__file__))
# This file is used to store the last cursor position
cursor_position = os.path.join(CURRENT_FILE_DIR, "access_cursor_position.log")
# Log file with new lines
log_file_to_cut = os.path.join(CURRENT_FILE_DIR, "access.log")
cut_file = os.path.join(CURRENT_FILE_DIR, "cut_access", "cut.log")
# Set in from_line
from_position = 0
try:
with open(cursor_position, "r", encoding=ENCODING) as f:
from_position = int(f.read())
except Exception as e:
pass
# We read log_file_to_cut to put new lines in cut_file
with open(log_file_to_cut, "r", encoding=ENCODING) as f:
with open(cut_file, "w", encoding=ENCODING) as fw:
# We set cursor to the last position used (during last run of script)
f.seek(from_position)
for line in f:
fw.write("%s" % (line))
# We save the last position of cursor for next usage
with open(cursor_position, "w", encoding=ENCODING) as fw:
fw.write(str(f.tell()))

From the python documentation for fileinput.input():
This iterates over the lines of all files listed in sys.argv[1:], defaulting to sys.stdin if the list is empty
further, the definition of the function is:
fileinput.FileInput([files[, inplace[, backup[, mode[, openhook]]]]])
reading between the lines, this tells me that files can be a list so you could have something like:
for each_line in fileinput.input([input_file, input_file]):
do_something(each_line)
See here for more information

#Using a text file for the example
with open("yourFile.txt","r") as f:
text = f.readlines()
for line in text:
print line
Open your file for reading (r)
Read the whole file and save each line into a list (text)
Loop through the list printing each line.
If you want, for example, to check a specific line for a length greater than 10, work with what you already have available.
for line in text:
if len(line) > 10:
print line

I would strongly recommend not using the default file loading as it is horrendously slow. You should look into the numpy functions and the IOpro functions (e.g. numpy.loadtxt()).
http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html
https://store.continuum.io/cshop/iopro/
Then you can break your pairwise operation into chunks:
import numpy as np
import math
lines_total = n
similarity = np.zeros(n,n)
lines_per_chunk = m
n_chunks = math.ceil(float(n)/m)
for i in xrange(n_chunks):
for j in xrange(n_chunks):
chunk_i = (function of your choice to read lines i*lines_per_chunk to (i+1)*lines_per_chunk)
chunk_j = (function of your choice to read lines j*lines_per_chunk to (j+1)*lines_per_chunk)
similarity[i*lines_per_chunk:(i+1)*lines_per_chunk,
j*lines_per_chunk:(j+1)*lines_per_chunk] = fast_operation(chunk_i, chunk_j)
It's almost always much faster to load data in chunks and then do matrix operations on it than to do it element by element!!

Best way to read large file, line by line is to use python enumerate function
with open(file_name, "rU") as read_file:
for i, row in enumerate(read_file, 1):
#do something
#i in line of that line
#row containts all data of that line

Process data, much larger than physical memory, in chunks

I need to process some data that is a few hundred times bigger than RAM. I would like to read in a large chunk, process it, save the result, free the memory and repeat. Is there a way to make this efficient in python?

The general key is that you want to process the file iteratively.
If you're just dealing with a text file, this is trivial: for line in f: only reads in one line at a time. (Actually it buffers things up, but the buffers are small enough that you don't have to worry about it.)
If you're dealing with some other specific file type, like a numpy binary file, a CSV file, an XML document, etc., there are generally similar special-purpose solutions, but nobody can describe them to you unless you tell us what kind of data you have.
But what if you have a general binary file?
First, the read method takes an optional max bytes to read. So, instead of this:
data = f.read()
process(data)
You can do this:
while True:
data = f.read(8192)
if not data:
break
process(data)
You may want to instead write a function like this:
def chunks(f):
while True:
data = f.read(8192)
if not data:
break
yield data
Then you can just do this:
for chunk in chunks(f):
process(chunk)
You could also do this with the two-argument iter, but many people find that a bit obscure:
for chunk in iter(partial(f.read, 8192), b''):
process(chunk)
Either way, this option applies to all of the other variants below (except for a single mmap, which is trivial enough that there's no point).
There's nothing magic about the number 8192 there. You generally do want a power of 2, and ideally a multiple of your system's page size. beyond that, your performance won't vary that much whether you're using 4KB or 4MB—and if it does, you'll have to test what works best for your use case.
Anyway, this assumes you can just process each 8K at a time without keeping around any context. If you're, e.g., feeding data into a progressive decoder or hasher or something, that's perfect.
But if you need to process one "chunk" at a time, your chunks could end up straddling an 8K boundary. How do you deal with that?
It depends on how your chunks are delimited in the file, but the basic idea is pretty simple. For example, let's say you use NUL bytes as a separator (not very likely, but easy to show as a toy example).
data = b''
while True:
buf = f.read(8192)
if not buf:
process(data)
break
data += buf
chunks = data.split(b'\0')
for chunk in chunks[:-1]:
process(chunk)
data = chunks[-1]
This kind of code is very common in networking (because sockets can't just "read all", so you always have to read into a buffer and chunk into messages), so you may find some useful examples in networking code that uses a protocol similar to your file format.
Alternatively, you can use mmap.
If your virtual memory size is larger than the file, this is trivial:
with mmap.mmap(f.fileno(), access=mmap.ACCESS_READ) as m:
process(m)
Now m acts like a giant bytes object, just as if you'd called read() to read the whole thing into memory—but the OS will automatically page bits in and out of memory as necessary.
If you're trying to read a file too big to fit into your virtual memory size (e.g., a 4GB file with 32-bit Python, or a 20EB file with 64-bit Python—which is only likely to happen in 2013 if you're reading a sparse or virtual file like, say, the VM file for another process on linux), you have to implement windowing—mmap in a piece of the file at a time. For example:
windowsize = 8*1024*1024
size = os.fstat(f.fileno()).st_size
for start in range(0, size, window size):
with mmap.mmap(f.fileno(), access=mmap.ACCESS_READ,
length=windowsize, offset=start) as m:
process(m)
Of course mapping windows has the same issue as reading chunks if you need to delimit things, and you can solve it the same way.
But, as an optimization, instead of buffering, you can just slide the window forward to the page containing the end of the last complete message, instead of 8MB at a time, and then you can avoid any copying. This is a bit more complicated, so if you want to do it, search for something like "sliding mmap window", and write a new question if you get stuck.

Parallel processing of a large .csv file in Python

I'm processing large CSV files (on the order of several GBs with 10M lines) using a Python script.
The files have different row lengths, and cannot be loaded fully into memory for analysis.
Each line is handled separately by a function in my script. It takes about 20 minutes to analyze one file, and it appears disk access speed is not an issue, but rather processing/function calls.
The code looks something like this (very straightforward). The actual code uses a Class structure, but this is similar:
csvReader = csv.reader(open("file","r")
for row in csvReader:
handleRow(row, dataStructure)
Given the calculation requires a shared data structure, what would be the best way to run the analysis in parallel in Python utilizing multiple cores?
In general, how do I read multiple lines at once from a .csv in Python to transfer to a thread/process? Looping with for over the rows doesn't sound very efficient.
Thanks!

This might be too late, but just for future users I'll post anyway. Another poster mentioned using multiprocessing. I can vouch for it and can go into more detail. We deal with files in the hundreds of MB/several GB every day using Python. So it's definitely up to the task. Some of files we deal with aren't CSVs, so the parsing can be fairly complex and take longer than the disk access. However, the methodology is the same no matter what file type.
You can process pieces of the large files concurrently. Here's pseudo code of how we do it:
import os, multiprocessing as mp
# process file function
def processfile(filename, start=0, stop=0):
if start == 0 and stop == 0:
... process entire file...
else:
with open(file, 'r') as fh:
fh.seek(start)
lines = fh.readlines(stop - start)
... process these lines ...
return results
if __name__ == "__main__":
# get file size and set chuck size
filesize = os.path.getsize(filename)
split_size = 100*1024*1024
# determine if it needs to be split
if filesize > split_size:
# create pool, initialize chunk start location (cursor)
pool = mp.Pool(cpu_count)
cursor = 0
results = []
with open(file, 'r') as fh:
# for every chunk in the file...
for chunk in xrange(filesize // split_size):
# determine where the chunk ends, is it the last one?
if cursor + split_size > filesize:
end = filesize
else:
end = cursor + split_size
# seek to end of chunk and read next line to ensure you
# pass entire lines to the processfile function
fh.seek(end)
fh.readline()
# get current file location
end = fh.tell()
# add chunk to process pool, save reference to get results
proc = pool.apply_async(processfile, args=[filename, cursor, end])
results.append(proc)
# setup next chunk
cursor = end
# close and wait for pool to finish
pool.close()
pool.join()
# iterate through results
for proc in results:
processfile_result = proc.get()
else:
...process normally...
Like I said, that's only pseudo code. It should get anyone started who needs to do something similar. I don't have the code in front of me, just doing it from memory.
But we got more than a 2x speed up from this on the first run without fine tuning it. You can fine tune the number of processes in the pool and how large the chunks are to get an even higher speed up depending on your setup. If you have multiple files as we do, create a pool to read several files in parallel. Just be careful no to overload the box with too many processes.
Note: You need to put it inside an "if main" block to ensure infinite processes aren't created.

Try benchmarking reading your file and parsing each CSV row but doing nothing with it. You ruled out disk access, but you still need to see if the CSV parsing is what's slow or if your own code is what's slow.
If it's the CSV parsing that's slow, you might be stuck, because I don't think there's a way to jump into the middle of a CSV file without scanning up to that point.
If it's your own code, then you can have one thread reading the CSV file and dropping rows into a queue, and then have multiple threads processing rows from that queue. But don't bother with this solution if the CSV parsing itself is what's making it slow.

Because of the GIL, Python's threading won't speed-up computations that are processor bound like it can with IO bound.
Instead, take a look at the multiprocessing module which can run your code on multiple processors in parallel.

If the rows are completely independent just split the input file in as many files as CPUs you have. After that, you can run as many instances of the process as input files you have now. This instances, since they are completely different processes, will not be bound by GIL problems.

Just found a solution to this old problem. I tried Pool.imap, and it seems to simplify processing large file significantly. imap has one significant benefit when comes to processing large files: It returns results as soon as they are ready, and not wait for all the results to be available. This saves lot of memory.
(Here is an untested snippet of code which reads a csv file row by row, process each row and write it back to a different csv file. Everything is done in parallel.)
import multiprocessing as mp
import csv
CHUNKSIZE = 10000 # Set this to whatever you feel reasonable
def _run_parallel(csvfname, csvoutfname):
with open(csvfname) as csvf, \
open(csvoutfname, 'w') as csvout\
mp.Pool() as p:
reader = csv.reader(csvf)
csvout.writerows(p.imap(process, reader, chunksize=CHUNKSIZE))

If you use zmq and a DEALER middle man, you'd be able spread the row processing not just to the CPUs on your computer but across a network to as many processes as necessary. This would essentially guarentee that you hit an IO limit vs a CPU limit :)

Python: efficient file io

What is the most efficient (fastest) way to simultaneously read in two large files and do some processing?
I have two files; a.txt and b.txt, each containing about a hundred thousand corresponding lines. My goal is to read in the two files and then do some processing on each line pair
def kernel:
a_file=open('a.txt','r')
b_file=open('b.txt', 'r')
a_line = a_file.readline()
b_line = b_file.readline()
while a_line:
process(a_spl,b_spl) #process requiring both corresponding file lines
I looked in to xreadlines and readlines but i'm wondering if i can do better. speed is of paramount importance for this task.
thank you.

The below code does not accumulate data from the input files in memory, unless the process function does that by itself.
from itertools import izip
def process(line1, line2):
# process a line from each input
with open(file1, 'r') as f1:
with open(file2, 'r') as f2:
for a, b in izip(f1, f2):
process(a, b)
If the process function is efficient, this code should run quickly enough for most purposes. The for loop will terminate when the end of one of the files is reached. If either file contains an extraordinarily long line (i.e. XML, JSON), or if the files are not text, this code may not work well.

You can use with statement to make sure your files are closed after the execution. From this blog entry:
to open a file, process its contents, and make sure to close it, you can simply do:
with open("x.txt") as f:
data = f.read()
do something with data

String IO can be pretty fast -- probably your processing will be what slows things down. Consider a simple input loop to feed a queue like:
queue = multiprocessing.Queue(100)
a_file = open('a.txt')
b_file = open('b.txt')
for pair in itertools.izip(a_file, b_file):
queue.put(pair) # blocks here on full queue
You can set up a pool of processes pulling items from the queue and taking action on each, assuming your problem can be parallelised this way.

I'd change your while condition to the following so that it doesn't fail when a has more lines than b.
while a_line and b_line
Otherwise, that looks good. You are reading in the two lines that you need, then processing. You could even multithread this by reading in N pairs of line and sending each pair off to a new thread or similar.

Download a file part by part in Python 3

I'm using Python 3 to download a file:
local_file = open(file_name, "w" + file_mode)
local_file.write(f.read())
local_file.close()
This code works, but it copies the whole file into memory first. This is a problem with very big files because my program becomes memory hungry. (Going from 17M memory to 240M memory for a 200 MB file)
I would like to know if there is a way in Python to download a small part of a file (packet), write it to file, erase it from memory, and keep repeating the process until the file is completely downloaded.

Try using the method described here:
Lazy Method for Reading Big File in Python?
I am specifically referring to the accepted answer. Let me also copy it here to ensure complete clarity of response.
def read_in_chunks(file_object, chunk_size=1024):
"""Lazy function (generator) to read a file piece by piece.
Default chunk size: 1k."""
while True:
data = file_object.read(chunk_size)
if not data:
break
yield data
f = open('really_big_file.dat')
for piece in read_in_chunks(f):
process_data(piece)
This will likely be adaptable to your needs: it reads the file in smaller chunks, allowing for processing without filling your entire memory. Come back if you have any further questions.