Source Code Optimizer

Here are some suggestions to optimize the given Python code for counting word frequencies in a text file:

1. Use collections.defaultdict instead of a regular dictionary

Using a defaultdict can simplify the logic for incrementing word counts and eliminate the need for the if-else block:

from collections import defaultdict

def count_words(file_path):
    freq = defaultdict(int)
    with open(file_path, 'r') as file:
        for line in file:
            words = re.findall(r'\w+', line.lower())
            for word in words:
                freq[word] += 1
    return freq

2. Use collections.Counter for counting frequencies

The Counter class from the collections module is specifically designed for counting hashable objects. It can further simplify the code:

from collections import Counter

def count_words(file_path):
    with open(file_path, 'r') as file:
        words = re.findall(r'\w+', file.read().lower())
        return Counter(words)

3. Read the file in chunks to handle large files

If the text file is very large, reading it line by line may not be memory-efficient. Instead, read the file in chunks using a buffer:

def count_words(file_path):
    freq = Counter()
    with open(file_path, 'r') as file:
        while True:
            chunk = file.read(4096)  # Read in 4KB chunks
            if not chunk:
                break
            words = re.findall(r'\w+', chunk.lower())
            freq.update(words)
    return freq

4. Compile the regular expression pattern

Compiling the regular expression pattern before using it can improve performance, especially if the function is called multiple times:

word_pattern = re.compile(r'\w+')

def count_words(file_path):
    freq = Counter()
    with open(file_path, 'r') as file:
        while True:
            chunk = file.read(4096)
            if not chunk:
                break
            words = word_pattern.findall(chunk.lower())
            freq.update(words)
    return freq

Here's the optimized version of the code incorporating all the above suggestions:

import re
from collections import Counter

word_pattern = re.compile(r'\w+')

def count_words(file_path):
    freq = Counter()
    with open(file_path, 'r') as file:
        while True:
            chunk = file.read(4096)
            if not chunk:
                break
            words = word_pattern.findall(chunk.lower())
            freq.update(words)
    return freq

print(count_words('sample.txt'))

These optimizations make the code more concise, efficient, and capable of handling larger text files. The use of Counter simplifies the frequency counting logic, while reading the file in chunks reduces memory usage. Compiling the regular expression pattern ahead of time also provides a small performance boost.