How to do it...

In the following steps, we will enumerate all the 4-grams of a sample file and select the 50 most frequent ones:

1. We begin by importing the collections library to facilitate counting and the ngrams library from nltk to ease extraction of N-grams:

import collections
from nltk import ngrams

1. We specify which file we would like to analyze:

file_to_analyze = "python-3.7.2-amd64.exe"

1. We define a convenience function to read in a file's bytes:

def read_file(file_path):
    """Reads in the binary sequence of a binary file."""
    with open(file_path, "rb") as binary_file:
        data = binary_file.read()
    return data

1. We write a convenience function to take a byte sequence and obtain N-grams:

def byte_sequence_to_Ngrams(byte_sequence, N):
    """Creates a list of N-grams from a byte sequence."""
    Ngrams = ngrams(byte_sequence, N)
    return list(Ngrams)

1. We write a function to take a file and obtain its count of N-grams:

def binary_file_to_Ngram_counts(file, N):
    """Takes a binary file and outputs the N-grams counts of its binary sequence."""
    filebyte_sequence = read_file(file)
    file_Ngrams = byte_sequence_to_Ngrams(filebyte_sequence, N)
    return collections.Counter(file_Ngrams)

1. We specify that our desired value is N=4 and obtain the counts of all 4-grams in the file:

extracted_Ngrams = binary_file_to_Ngram_counts(file_to_analyze, 4)

1. We list the 10 most common 4-grams of our file:

print(extracted_Ngrams.most_common(10))

The result is as follows:

[((0, 0, 0, 0), 24201), ((139, 240, 133, 246), 1920), ((32, 116, 111, 32), 1791), ((255, 255, 255, 255), 1663), ((108, 101, 100, 32), 1522), ((100, 32, 116, 111), 1519), ((97, 105, 108, 101), 1513), ((105, 108, 101, 100), 1513), ((70, 97, 105, 108), 1505), ((101, 100, 32, 116), 1503)]