How to parse very big files in python?

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I have a very big tsv file: 1.5 GB. i want to parse this file. Im using the following function:

def readEvalFileAsDictInverse(evalFile):
  eval = open(evalFile, "r")
  evalIDs = {}
  for row in eval:
    ids = row.split("\t")
    if ids[0] not in evalIDs.keys():
      evalIDs[ids[0]] = []

  return evalIDs 

It is take more than 10 hours and it is still working. I dont know how to accelerate this step and if there is another method to parse such as file

Maybe, you can make it somewhat faster; change it:

if ids[0] not in evalIDs.keys():
      evalIDs[ids[0]] = []



The 1st solution searches 3 times in the "evalID" dictionary.

Tip for Opening Large Text Files in Windows, How do I read a large csv file in Python? Navigating Through Large Text Files. Although the above step allowed us to read large text files by extracting lines from that large file and sending those lines to another text file, directly navigating through the large file without the need to extract it line by line would be a preferable idea.

How to read a large CSV file in chunks with Pandas in Python, My first big data tip for python is learning how to break your files into smaller units For mere number crunching, the Pool object is very good. Reading Large Text Files in Python. We can use the file object as an iterator. The iterator will return each line one by one, which can be processed. This will not read the whole file into memory and it’s suitable to read large files in Python. Here is the code snippet to read large file in Python by treating it as an iterator.

Some suggestions:

Use a defaultdict(list) instead of creating inner lists yourself or using dict.setdefault().

dict.setfdefault() will create the defautvalue every time, thats a time burner - defautldict(list) does not - it is optimized:

from collections import defaultdict    
def readEvalFileAsDictInverse(evalFile):
  eval = open(evalFile, "r")
  evalIDs = defaultdict(list)
  for row in eval:
    ids = row.split("\t")

If your keys are valid file names you might want to investigate awk for much more performance then doing this in python.

Something along the lines of

awk -F $'\t' '{print > $1}' file1

will create your split files much faster and you can simply use the latter part of the following code to read from each file (assuming your keys are valid filenames) to construct your lists. (Attributation: here ) - You would need to grab your created files with os.walk or similar means. Each line inside the files will still be tab-seperated and contain the ID in front

If your keys are not filenames in their own right, consider storing your different lines into different files and only keep a dictionary of key,filename around.

After splitting the data, load the files as lists again:

Create testfile:

with open ("file.txt","w") as w:




# f.e.
def make_filename(k):
    """In case your keys contain non-filename-characters, make it a valid name"""          
    return k # assuming k is a valid file name else modify it

evalFile = "file.txt"
files = {}
with open(evalFile, "r") as eval_file:
    for line in eval_file:
        if not line.strip():
        key,value, *rest = line.split("\t") # omit ,*rest if you only have 2 values
        fn = files.setdefault(key, make_filename(key))

        # this wil open and close files _a lot_ you might want to keep file handles
        # instead in your dict - but that depends on the key/data/lines ratio in 
        # your data - if you have few keys, file handles ought to be better, if 
        # have many it does not matter
        with open(fn,"a") as f:

# create your list data from your files:
data = {}
for key,fn in files.items():
    with open(fn) as r:
        data[key] = [x.strip() for x in r]



# for my data: loaded from files called '1', '2' and '3'
{'1': ['tata', 'TTtata', 'tttttttata'], 
 '2': ['yipp', 'YYyipp', 'yyyyyyyipp'], 
 '3': ['urks', 'UUurks', 'uuuuuuurks']}

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  1. Change evalIDs to a collections.defaultdict(list). You can avoid the if to check if a key is there.
  2. Consider splitting the file externally using split(1) or even inside python using a read offset. Then use multiprocessing.pool to parallelise the loading.

Quick Tip: How to Read Extremely Large Text Files Using Python, How to read large files in Python? We can use python file object as iterator to read it line by line. We can also use buffer to read large binary files. I’m currently working on a project that has multiple very large CSV files (6 gigabytes+). Normally when working with CSV data, I read the data in using pandas and then start munging and analyzing the data. With files this large, reading the data into pandas directly can be difficult (or impossible) due to memory constrictions, especially if you’re working on a prosumer computer.

How to Read Large Text Files in Python, Read file in python is very simple, you can use read and readlines function to easily read them. But there are also some tricks in using them. After that, the 6.4 gig CSV file processed without any issues. Creating Large XML Files in Python. This part of the process, taking each row of csv and converting it into an XML element, went fairly smoothly thanks to the xml.sax.saxutils.XMLGenerator class.

Python Read Big File Example ·, I have a large txt file (3 Million lines). Like to use python , to parse the file , so it can be managed by excel. I am very new with Programming and  Parsing large XML files efficiently with Python Parsing XML with python is not a difficult task if you have some familiarity with python and any of the library that deals with providing you methods to parse XML.

Parsing large text file in Python [SOLVED], Just open the file in a with block to avoid having to close it. Then, iterate over each line in the file object in a for loop and process those lines. This article is aimed at Python beginners who are interested in learning to parse text files. In this article, I will introduce you to my system for parsing files. I will briefly touch on parsing files in standard formats, but what I want to focus on is the parsing of complex text files.