Hashing a file in Python

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I want python to read to the EOF so I can get an appropriate hash, whether it is sha1 or md5. Please help. Here is what I have so far:

import hashlib

inputFile = raw_input("Enter the name of the file:")
openedFile = open(inputFile)
readFile = openedFile.read()

md5Hash = hashlib.md5(readFile)
md5Hashed = md5Hash.hexdigest()

sha1Hash = hashlib.sha1(readFile)
sha1Hashed = sha1Hash.hexdigest()

print "File Name: %s" % inputFile
print "MD5: %r" % md5Hashed
print "SHA1: %r" % sha1Hashed

TL;DR use buffers to not use tons of memory.

We get to the crux of your problem, I believe, when we consider the memory implications of working with very large files. We don't want this bad boy to churn through 2 gigs of ram for a 2 gigabyte file so, as pasztorpisti points out, we gotta deal with those bigger files in chunks!

import sys
import hashlib

# BUF_SIZE is totally arbitrary, change for your app!
BUF_SIZE = 65536  # lets read stuff in 64kb chunks!

md5 = hashlib.md5()
sha1 = hashlib.sha1()

with open(sys.argv[1], 'rb') as f:
    while True:
        data = f.read(BUF_SIZE)
        if not data:

print("MD5: {0}".format(md5.hexdigest()))
print("SHA1: {0}".format(sha1.hexdigest()))

What we've done is we're updating our hashes of this bad boy in 64kb chunks as we go along with hashlib's handy dandy update method. This way we use a lot less memory than the 2gb it would take to hash the guy all at once!

You can test this with:

$ mkfile 2g bigfile
$ python hashes.py bigfile
MD5: a981130cf2b7e09f4686dc273cf7187e
SHA1: 91d50642dd930e9542c39d36f0516d45f4e1af0d
$ md5 bigfile
MD5 (bigfile) = a981130cf2b7e09f4686dc273cf7187e
$ shasum bigfile
91d50642dd930e9542c39d36f0516d45f4e1af0d  bigfile

Hope that helps!

Also all of this is outlined in the linked question on the right hand side: Get MD5 hash of big files in Python


In general when writing python it helps to get into the habit of following pep-8. For example, in python variables are typically underscore separated not camelCased. But that's just style and no one really cares about those things except people who have to read bad style... which might be you reading this code years from now.

Hashing Files with Python, TL;DR use buffers to not use tons of memory. We get to the crux of your problem, I believe, when we consider the memory implications of  How to Hash Files in Python. Hashing files allows us to generate a string/byte sequence that can help identify a file. This can then be used by comparing the hashes of two or more files to see if these files are the same as well as other applications.

Hashing a file in Python, We will use the SHA-1 hashing algorithm. The digest of SHA-1 is 160 bits long. We do not feed the data from the file all at once, because some files are very  Python File I/O Hash functions take an arbitrary amount of data and return a fixed-length bit string. The output of the function is called the digest message. They are widely used in cryptography for authentication purposes.

I would propose simply:

def get_digest(file_path):
    h = hashlib.sha256()

    with open(file_path, 'rb') as file:
        while True:
            # Reading is buffered, so we can read smaller chunks.
            chunk = file.read(h.block_size)
            if not chunk:

    return h.hexdigest()

All other answers here seem to complicate too much. Python is already buffering when reading (in ideal manner, or you configure that buffering if you have more information about underlying storage) and so it is better to read in chunks the hash function finds ideal which makes it faster or at lest less CPU intensive to compute the hash function. So instead of disabling buffering and trying to emulate it yourself, you use Python buffering and control what you should be controlling: what the consumer of your data finds ideal, hash block size.

Python Program to Find Hash of File, Python module to facilitate calculating the checksum or hash of a file. Tested against Python 2.7.x, Python 3.6.x, Python 3.7.x, Python 3.8.x,  For the correct and efficient computation of the hash value of a file (in Python 3): Open the file in binary mode (i.e. add 'b' to the filemode) to avoid character encoding Don't read the complete file into memory, since that is a waste of memory. Eliminate double buffering, i.e. don't use

I have programmed a module wich is able to hash big files with different algorithms.

pip3 install py_essentials

Use the module like this:

from py_essentials import hashing as hs
hash = hs.fileChecksum("path/to/the/file.txt", "sha256")

filehash · PyPI, You can find the hash of a file using the hashlib library. Note that file sizes can be pretty big. Its best to use a buffer to load chunks and process  Checksums are used to validate data in a file. ZIP files use checksums to ensure a file is not corrupt when decompressing. Unlike Python’s built-in hashing, it’s deterministic. The same data will

import hashlib
user = input("Enter ")
h = hashlib.md5(user.encode())
h2 = h.hexdigest()
with open("encrypted.txt","w") as e:

with open("encrypted.txt","r") as e:
    p = e.readline().strip()

How to Find Hash of File using Python?, Included are the FIPS secure hash algorithms SHA1, SHA224, SHA256, SHA384​, from hashlib import blake2b >>> FILES_HASH_PERSON = b'MyApp Files  But in Python 3 it matters on every platform. sha256() requires binary input, but you opened the file in text mode. That's why @BrenBam suggested you open the file in binary mode. Since you opened the file in text mode, Python 3 believes it needs to decode the bits in the file to turn the bytes into Unicode strings.

hashlib — Secure hashes and message digests, SHA 256 hashing algorithm is widely used in security applications and protocols. The following python program computes the SHA256 hash value of a file. Note  SHA 256 hashing algorithm is widely used in security applications and protocols. The following python program computes the SHA256 hash value of a file. Note that the computed hash is converted to a readable hexadecimal string. The above program may fail for large input files since we read the entire string to compute the hash.

How to Calculate SHA256 Hash of a File in Python, import hashlib as hash. # Specify how many bytes of the file you want to open at a time. BLOCKSIZE = 65536. sha = hash.sha256(). with open('kali.iso', 'rb') as  Python stands out as a language to implement much of the good sects of this. Python offers hash() method to encode the data into unrecognisable value. Syntax : hash(obj) Parameters : obj : The object which we need to convert into hash. Returns : Returns the hashed value if possible.

Hash a large file in Python · GitHub, ZIP files use checksums to ensure a file is not corrupt when decompressing. Unlike Python's built-in hashing, it's deterministic. The same data  Python - Hash Table. Hash tables are a type of data structure in which the address or the index value of the data element is generated from a hash function. That makes accessing the data faster as the index value behaves as a key for the data value. In other words Hash table stores key-value pairs but the key is generated through a hashing function.

  • and what is the problem?
  • I want it to be able to hash a file. I need it to read until the EOF, whatever the file size may be.
  • that is exactly what file.read() does - read the entire file.
  • The documentation for the read() method says?
  • You should go through "what is hashing?".
  • @ranman Hello, I couldn't get the {0}".format(sha1.hexdigest()) part. Why do we use it instead of just using sha1.hexdigest() ?
  • @Belial What wasn't working? I was mainly just using that to differentiate between the two hashes...
  • @ranman Everything is working, I just never used this and haven't seen it in the literature. "{0}".format() ... unknown to me. :)
  • How should I choose BUF_SIZE?
  • This does doesn't generate the same results as the shasum binaries. The other answer listed below (the one using memoryview) is compatible with other hashing tools.
  • How do you know what is an optimal block size?
  • @Mitar, a lower bound is the maximum of the physical block (traditionally 512 bytes or 4KiB with newer disks) and the systems page size (4KiB on many system, other common choices: 8KiB and 64 KiB). Then you basically do some benchmarking and/or look at published benchmark results and related work (e.g. check what current rsync/GNU cp/... use).
  • Would resource.getpagesize be of any use here, if we wanted to try to optimize it somewhat dynamically? And what about mmap?