There were a lot of uninteresting results, but there were two I thought were worth sharing. Hello, can you provide some more information on the methods you used to gather those benchmark results? I ran a quick test with python 3. How big are the files that you are running this test on?
I am working with pretty large files 20Gb and up and wanted to try this out to see if it makes a difference. I wrote this 3 years ago and I wonder if at the time I was still using Python 2. Certainly in Python 3 I agree with you.
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Share Copy sharable link for this gist. Learn more about clone URLs. Download ZIP. BufferedReader gz for line in f. This comment has been minimized. Sign in to view. Copy link Quote reply. What version of Python? Content of lines I ran a quick test with python 3. That's a good point. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.Table of Contents Previous: bz2 — bzip2 compression Next: tarfile — Tar archive access.
Some of the features described here may not be available in earlier versions of Python. Now available for Python 3! Buy the book! The gzip module provides a file-like interface to GNU zip files, using zlib to compress and uncompress the data. The module-level function open creates an instance of the file-like class GzipFile.
The usual methods for writing and reading data are provided. To write data into a compressed file, open the file with mode 'w'. Different amounts of compression can be used by passing a compresslevel argument.
Valid values range from 1 to 9, inclusive. Lower values are faster and result in less compression. Higher values are slower and compress more, up to a point. The center column of numbers in the output of the script is the size in bytes of the files produced.
As you see, for this input data, the higher compression values do not necessarily pay off in decreased storage space. Results will vary, depending on the input data. A GzipFile instance also includes a writelines method that can be used to write a sequence of strings. To read data back from previously compressed files, simply open the file with mode 'r'. The seek position is relative to the uncompressed data, so the caller does not even need to know that the data file is compressed.
When working with a data stream instead of a file, use the GzipFile class directly to compress or uncompress it. This is useful when the data is being transmitted over a socket or from read an existing already open file handle.
A StringIO buffer can also be used.Data Instead Of Unicode Vs. Whetting Your Appetite 2. Using the Python Interpreter 2. Invoking the Interpreter 2. Argument Passing 2. Interactive Mode 2. The Interpreter and Its Environment 2. Source Code Encoding 3. An Informal Introduction to Python 3. Using Python as a Calculator 3. Numbers 3. Strings 3. Lists 3. First Steps Towards Programming 4.
More Control Flow Tools 4. The range Function 4. Defining Functions 4. More on Defining Functions 4. Default Argument Values 4. Keyword Arguments 4. Special parameters 4. Positional-or-Keyword Arguments 4. Positional-Only Parameters 4. Keyword-Only Arguments 4. Function Examples 4.
Recap 4. Arbitrary Argument Lists 4. Unpacking Argument Lists 4.I think there is a much simpler solution than the others presented given the op only wanted to extract all the files in a directory:. Note that I do not want to read the files, only uncompress them. After searching this site for a while, I have this code segment, but it does not work:. Since my goal was to simply uncompress the archive, the above code accomplishes this.
The archived files are located in a central location, and are copied to a working area, uncompressed, and used in a test case. You should use with to open files and, of course, store the result of reading the compressed file. See gzip documentation :. Depending on what exactly you want to do, you might want to have a look at tarfile and its 'r:gz' option for opening files.
You're decompressing file into s variable, and do nothing with it.
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You should stop searching and read at least python tutorial. Because in your code, you're unpacking the original gzip file and not the copy. This is not an error, just a very bad practice.
After searching this site for a while, I have this code segment, but it does not work: import gzip import glob import os for file in glob. I was able to resolve this issue by using the subprocess module: for file in glob. Thanks for everyone's help. It is much appreciated! See gzip documentation : import gzip import glob import os import os. Anyway, there's several thing wrong with your code: you need is to STORE the unzipped data in s into some file.
This should probably do what you wanted: import gzip import glob import os import os. Calling an external command in Python What are metaclasses in Python? What is the difference between staticmethod and classmethod? Finding the index of an item given a list containing it in Python Difference between append vs.
The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. So much so that the program exits and prints Killed to the terminal. Maybe it is loading the entire extracted file into memory? As the documentation explains:. Obviously, that requires reading reading and decompressing the entire file, and building up an absolutely gigantic list.
Most likely, it's actually the malloc calls to allocate all that memory that are taking forever. And then, at the end of this scope assuming you're using CPythonit has to GC that whole gigantic list, which will also take forever.
You almost never want to use readlines. Unless you're using a very old Python, just do this:. A file is an iterable full of lines, just like the list returned by readlines —except that it's not actually a listit generates more lines on the fly by reading out of a buffer. So, at any given time, you'll only have one line and a couple of buffers on the order of 10MB each, instead of a 25GB list. And the reading and decompressing will be spread out over the lifetime of the loop, instead of done all at once.
From a quick test, with a 3. But if I do for line in f. Since this has come up a dozen more times since this answer, I wrote this blog post which explains a bit more. Have a look at pandas, in particular IO tools. They support gzip compression when reading files and you can read files in chunks. Besides, pandas is very fast and memory efficient. As I never tried, I don't know how well the compression and reading in chunks live together, but it might be worth giving a try. Learn more. Asked 7 years, 2 months ago.
Active 2 years, 7 months ago. Viewed 30k times.#65 Python Tutorial for Beginners - File handling
So I have some fairly gigantic. But is there a cleaner way? Are you sure it's open and not readlines that's taking forever?
Active Oldest Votes. As the documentation explains: f. Unless you're using a very old Python, just do this: for line in f: A file is an iterable full of lines, just like the list returned by readlines —except that it's not actually a listit generates more lines on the fly by reading out of a buffer. FirefighterBlu3 You're referring to a 4-year-old comment, that refers to an answer that turned out to be misguided and was deleted by the answerer.
Probably better to just flag it as no longer needed or ignore it than to reply to it. Francesco Montesano Francesco Montesano 6, 1 1 gold badge 34 34 silver badges 58 58 bronze badges. And it's also quite fast and memory efficient.Over the course of my working life I have had the opportunity to use many programming concepts and technologies to do countless things.
Some of these things involve relatively low-value fruits of my labor, such as automating the error prone or mundane like report generation, task automation, and general data reformatting. Others have been much more valuable, such as developing data products, web applications, and data analysis and processing pipelines. One thing that is notable about nearly all of these projects is the need to simply open a file, parse its contents, and do something with it.
However, what do you do when the file you are trying to consume is quite large? What if the file is several GB of data or larger? The answer to this problem is to read in chunks of a file at a time, process it, then free it from memory so you can pull in and process another chunk until the whole massive file has been processed. While it is up to the programmer to determine a suitable chunk size, for many applications it is suitable to process a file one line at a time.
Throughout this article we'll be covering a number of code examples to show how to read files line by line. In case you want to try out some of these examples by yourself, the code used in this article can be found in the following GitHub repo. Being a great general purpose programming language, Python has a number of very useful file IO functionality in its standard library of built-in functions and modules.
The built-in open function is what you use to open a file object for either reading or writing purposes. The open function takes in multiple arguments. We will be focusing on the first two, with the first being a positional string parameter representing the path to the file that should be opened. The second optional parameter is also a string, which specifies the mode of interaction you intend for the file object being returned by the function call.
The most common modes are listed in the table below, with the default being 'r' for reading. Once you have written or read all of the desired data for a file object you need to close the file so that resources can be reallocated on the operating system that the code is running on.
You will often see many code snippets on the internet or in programs in the wild that do not explicitly close file objects that have been generated in accord with the example above. It is always good practice to close a file object resource, but many of us either are too lazy or forgetful to do so or think we are smart because documentation suggests that an open file object will self close once a process terminates.
This is not always the case.
How to Read a gzip File in Python?
Instead of harping on how important it is to always call close on a file object, I would like to provide an alternate and more elegant way to open a file object and ensure that the Python interpreter cleans up after us :.
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I need to know how many times a number appears in a gzip file with linesI have the following:. The issue with the gzip module is not that it can't decompress the partial file, the error occurs only at the end when it tries to verify the checksum of the decompressed content.
The original checksum is stored at the end of the compressed file so the verification will never, ever work with a partial file. The key is to trick gzip into skipping the verification. The answer by caesar does this by modifying the gzip source code, but it's not necessary to go that far, simple monkey patching will do. I wrote this context manager to temporarily replace gzip. Found by searching google for a stackexchange link to "python gzip crc check failed" first result.
Learn more. Read line by line gzip big file Ask Question. Asked 5 years, 11 months ago. Active 5 years, 11 months ago. Viewed 1k times. Perhaps you could run gunzip as a subprocess and read its output. Active Oldest Votes. Go to that link and read the entire respose. Amazingred Amazingred 5 5 silver badges 13 13 bronze badges. Sign up or log in Sign up using Google.
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