How Can I Optimize Regex Replacements in Python 3 for Speed and Word Boundary Accuracy?

Optimizing Regex Replacements in Python 3

In your scenario, you aim to perform regex replacements on a large number of strings, with the added complexity of ensuring replacements occur only at word boundaries. While a basic regex approach using nested loops can be slow, there are more efficient solutions.

Using the str.replace Method

The str.replace method can provide significant speed improvements compared to regex. However, to enforce word boundary replacements, you can use a regular expression within the str.replace arguments:

sentence = sentence.replace(r'\b' + word + r'\b', '')

This method combines the speed of str.replace with the word boundary enforcement of a regular expression.

Optimizing the re.sub Method

If you prefer to use the re.sub method, there are techniques to optimize its performance:

• Avoid re-compiling regex patterns: If the list of banned words is constant, pre-compile the regex pattern and store it in a variable. This eliminates the overhead of compiling the pattern for each replacement.
• Skip unnecessary checks: Similar to the optimization you mentioned, skipping word substitutions when the word length exceeds the sentence length can lead to performance gains.
• Use a Trie-Based Approach: Consider implementing a Trie data structure to represent the list of banned words. This approach can significantly speed up the replacement process, as it allows for efficient searching and matching of word boundaries.

Example Implementation Using a Trie

import re
import trie

banned_words = ['word1', 'word2', ...]

trie_obj = trie.Trie()
for word in banned_words:
    trie_obj.add(word)

trie_regex = r"\b" + trie_obj.pattern() + r"\b"
pattern = re.compile(trie_regex)

for sentence in sentences:
    sentence = pattern.sub('', sentence)

This approach leverages the speed of a Trie for word boundary matching, significantly reducing the processing time for large datasets.

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