[python] How can I read large text files in Python, line by line, without loading it into memory?

I need to read a large file, line by line. Lets say that file has more than 5GB and I need to read each line, but obviously I do not want to use readlines() because it will create a very large list in the memory.

How will the code below work for this case? Is xreadlines itself reading one by one into memory? Is the generator expression needed?

f = (line for line in open("log.txt").xreadlines())  # how much is loaded in memory?

f.next()  

Plus, what can I do to read this in reverse order, just as the Linux tail command?

I found:

http://code.google.com/p/pytailer/

and

"python head, tail and backward read by lines of a text file"

Both worked very well!

Thank you! I have recently converted to python 3 and have been frustrated by using readlines(0) to read large files. This solved the problem. But to get each line, I had to do a couple extra steps. Each line was preceded by a "b'" which I guess that it was in binary format. Using "decode(utf-8)" changed it ascii.

Then I had to remove a "=\n" in the middle of each line.

Then I split the lines at the new line.

b_data=(fh.read(ele[1]))#endat This is one chunk of ascii data in binary format
        a_data=((binascii.b2a_qp(b_data)).decode('utf-8')) #Data chunk in 'split' ascii format
        data_chunk = (a_data.replace('=\n','').strip()) #Splitting characters removed
        data_list = data_chunk.split('\n')  #List containing lines in chunk
        #print(data_list,'\n')
        #time.sleep(1)
        for j in range(len(data_list)): #iterate through data_list to get each item 
            i += 1
            line_of_data = data_list[j]
            print(line_of_data)

Here is the code starting just above "print data" in Arohi's code.

Here's what you do if you dont have newlines in the file:

with open('large_text.txt') as f:
  while True:
    c = f.read(1024)
    if not c:
      break
    print(c)

An old school approach:

fh = open(file_name, 'rt')
line = fh.readline()
while line:
    # do stuff with line
    line = fh.readline()
fh.close()

The best solution I found regarding this, and I tried it on 330 MB file.

lineno = 500
line_length = 8
with open('catfour.txt', 'r') as file:
    file.seek(lineno * (line_length + 2))
    print(file.readline(), end='')

Where line_length is the number of characters in a single line. For example "abcd" has line length 4.

I have added 2 in line length to skip the '\n' character and move to the next character.

Heres the code for loading text files of any size without causing memory issues. It support gigabytes sized files

https://gist.github.com/iyvinjose/e6c1cb2821abd5f01fd1b9065cbc759d

download the file data_loading_utils.py and import it into your code

usage

import data_loading_utils.py.py
file_name = 'file_name.ext'
CHUNK_SIZE = 1000000


def process_lines(data, eof, file_name):

    # check if end of file reached
    if not eof:
         # process data, data is one single line of the file

    else:
         # end of file reached

data_loading_utils.read_lines_from_file_as_data_chunks(file_name, chunk_size=CHUNK_SIZE, callback=self.process_lines)

process_lines method is the callback function. It will be called for all the lines, with parameter data representing one single line of the file at a time.

You can configure the variable CHUNK_SIZE depending on your machine hardware configurations.

How about this? Divide your file into chunks and then read it line by line, because when you read a file, your operating system will cache the next line. If you are reading the file line by line, you are not making efficient use of the cached information.

Instead, divide the file into chunks and load the whole chunk into memory and then do your processing.

def chunks(file,size=1024):
    while 1:

        startat=fh.tell()
        print startat #file's object current position from the start
        fh.seek(size,1) #offset from current postion -->1
        data=fh.readline()
        yield startat,fh.tell()-startat #doesnt store whole list in memory
        if not data:
            break
if os.path.isfile(fname):
    try:
        fh=open(fname,'rb') 
    except IOError as e: #file --> permission denied
        print "I/O error({0}): {1}".format(e.errno, e.strerror)
    except Exception as e1: #handle other exceptions such as attribute errors
        print "Unexpected error: {0}".format(e1)
    for ele in chunks(fh):
        fh.seek(ele[0])#startat
        data=fh.read(ele[1])#endat
        print data

I couldn't believe that it could be as easy as @john-la-rooy's answer made it seem. So, I recreated the cp command using line by line reading and writing. It's CRAZY FAST.

#!/usr/bin/env python3.6

import sys

with open(sys.argv[2], 'w') as outfile:
    with open(sys.argv[1]) as infile:
        for line in infile:
            outfile.write(line)

You are better off using an iterator instead. Relevant: http://docs.python.org/library/fileinput.html

From the docs:

import fileinput
for line in fileinput.input("filename"):
    process(line)

This will avoid copying the whole file into memory at once.

I demonstrated a parallel byte level random access approach here in this other question:

Getting number of lines in a text file without readlines

Some of the answers already provided are nice and concise. I like some of them. But it really depends what you want to do with the data that's in the file. In my case I just wanted to count lines, as fast as possible on big text files. My code can be modified to do other things too of course, like any code.

All you need to do is use the file object as an iterator.

for line in open("log.txt"):
    do_something_with(line)

Even better is using context manager in recent Python versions.

with open("log.txt") as fileobject:
    for line in fileobject:
        do_something_with(line)

This will automatically close the file as well.

Please try this:

with open('filename','r',buffering=100000) as f:
    for line in f:
        print line

This might be useful when you want to work in parallel and read only chunks of data but keep it clean with new lines.

def readInChunks(fileObj, chunkSize=1024):
    while True:
        data = fileObj.read(chunkSize)
        if not data:
            break
        while data[-1:] != '\n':
            data+=fileObj.read(1)
        yield data

The blaze project has come a long way over the last 6 years. It has a simple API covering a useful subset of pandas features.

dask.dataframe takes care of chunking internally, supports many parallelisable operations and allows you to export slices back to pandas easily for in-memory operations.

import dask.dataframe as dd

df = dd.read_csv('filename.csv')
df.head(10)  # return first 10 rows
df.tail(10)  # return last 10 rows

# iterate rows
for idx, row in df.iterrows():
    ...

# group by my_field and return mean
df.groupby(df.my_field).value.mean().compute()

# slice by column
df[df.my_field=='XYZ'].compute()

f=open('filename','r').read()
f1=f.split('\n')
for i in range (len(f1)):
    do_something_with(f1[i])

hope this helps.