I have a dictionary with each item containing a quote in another dictionary:

  'example1': {'quote': 'And it must follow, as the night the day, thou canst not then be false to any man.\n'},
  'example2': {'quote': 'What a piece of work is man! how noble in reason!.\n'}

I need to completely remove each entry whose quote contains a badword, not just checking if the string contains the badword but if it matches the full word. For instance, following the above example, considering as to be a badword, it should remove example1 but not example2 (that contains reASon).


#!/usr/bin/env python2.7
# -*- coding: utf-8 -*-

import re

def filter_bad_words(entries):

    f = open("badwords.txt", "r")
    badwords = f.readlines()

    # remove new lines from each word
    for i in range(len(badwords)):
            badwords[i] = badwords[i].strip('\n')

    original_count = len(entries)
    for key, item in entries.items():
        quote = item['quote']
        if any(findWholeWord(x)(quote) for x in badwords):
            del entries[key]
            print "Remove: %s" % quote
    print "Removed %s items." % (original_count - len(entries))

def findWholeWord(w):
    """ removes exact word"""
    return re.compile(r'\b({0})\b'.format(w), flags=re.IGNORECASE).search

def main():
    with open("quotes.txt", "r") as f:
        quotes = f.readlines()
        entries = {}
        for key, quote in enumerate(quotes):
            entry_key = "example{}".format(key)
            entries[entry_key] = {'quote': quote}

if __name__ == "__main__":


To be, or not to be: that is the question.
Neither a borrower nor a lender be; For loan oft loses both itself and friend, and borrowing dulls the edge of husbandry.
This above all: to thine own self be true.
Though this be madness, yet there is method in 't.
That it should come to this!.
There is nothing either good or bad, but thinking makes it so.
What a piece of work is man! how noble in reason! how infinite in faculty! in form and moving how express and admirable! in action how like an angel! in apprehension how like a god! the beauty of the world, the paragon of animals! .
The lady doth protest too much, methinks.
In my mind's eye.
A little more than kin, and less than kind.
The play 's the thing wherein I'll catch the conscience of the king.
And it must follow, as the night the day, thou canst not then be false to any man.
Brevity is the soul of wit.
Doubt that the sun doth move, doubt truth to be a liar, but never doubt I love.
Rich gifts wax poor when givers prove unkind.
Do you think I am easier to be played on than a pipe?
I will speak daggers to her, but use none.
When sorrows come, they come not single spies, but in
All the world 's a stage, and all the men and women merely players. They have their exits and their entrances; And one man in his time plays many parts.
Can one desire too much of a good thing?.
I like this place and willingly could waste my time in it.
How bitter a thing it is to look into happiness through another man's eyes!
Blow, blow, thou winter wind! Thou art not so unkind as man's ingratitude.
True is it that we have seen better days.
For ever and a day.
The fool doth think he is wise, but the wise man knows
himself to be a fool.
Now is the winter of our discontent.
A horse! a horse! my kingdom for a horse!.
Conscience is but a word that cowards use, devised at first to keep the strong in awe.
So wise so young, they say, do never live long.
Off with his head!
An honest tale speeds best, being plainly told.
The king's name is a tower of strength.
The world is grown so bad, that wrens make prey where eagles dare not
O Romeo, Romeo! wherefore art thou Romeo?.
It is the east, and Juliet is the sun.
Good Night, Good night! Parting is such sweet sorrow, that I shall say good night till it be morrow.
What's in a name? That which we call a rose by any other name would smell as sweet.
Wisely and slow; they stumble that run fast.
Tempt not a desperate man.
For you and I are past our dancing days.
O! she doth teach the torches to burn bright.
It seems she hangs upon the cheek of night like a rich jewel in an Ethiope's ear.
See, how she leans her cheek upon her hand! O that I were a glove upon that hand, that I might touch that cheek!.
Not stepping o'er the bounds of modesty.



It does its job but I find it extremely slow when dealing with more than 100,000 entries, so I would appreciate suggestions to improve its performance.

(I've setup a repo to make it easier for testing.)


3 Answers 3


The current time complexity is \$O(N * M)\$ where \$N\$ is the number of quotes and \$M\$ is the number of bad words. For every single word in a quote you are iterating over all the bad words to check if there is a match.

We can do better than that. What if you would initialize bad words as a set and would just lookup if a word is there - the lookup itself would be constant time - \$O(1)\$ which would make the overall complexity \$O(N + M)\$ - we still need \$O(M)\$ to initially make a set of bad words.

Also, I would use a more appropriate and robust nltk.word_tokenize() for word tokenization.

Modified code:

from nltk import word_tokenize

def filter_bad_words(entries):
    with open("badwords.txt", "r") as f:
        badwords = set(word.strip() for word in f)

    filtered_entries = {}
    for key, item in entries.items():
        quote = item['quote']
        words = word_tokenize(quote)

        if not any(word in badwords for word in words):
            filtered_entries[key] = {'quote': quote}

    print("Removed %s items." % (len(entries) - len(filtered_entries)))
    return filtered_entries

def main():
    with open("quotes.txt", "r") as f:
        entries = {
            "example{}".format(index): {'quote': quote}
            for index, quote in enumerate(f)


if __name__ == "__main__":
  • 1
    \$\begingroup\$ I absolutely agree with your suggestion to use a better algorithm, but I disagree with the suggestion to pull in all of nltk just for word_tokenize. Splitting on \b-boundaries is perfectly fine for a simple program like this; if OP is actually doing NLP, then they will surely know about this common problem and have a standard solution at their disposal. \$\endgroup\$
    – wchargin
    Nov 5, 2017 at 22:02
  • 1
    \$\begingroup\$ Interestingly, benchmarking both functions here, shows that this approach is slower than the original code, probably due to the usage of nltk? \$\endgroup\$
    – marcanuy
    Nov 5, 2017 at 22:15
  • \$\begingroup\$ @marcanuy interesting! word_tokenize() might have a negative impact, but the sizes of the quotes and badwords inputs have a much greater impact on the output time. E.g., I've tried benchmarking with the same number of quotes but with 80000 badwords - the "sets" are winning by a very dramatic margin - ~0.8s for sets, minutes (not sure how many minutes exactly - started 10 mins ago, still waiting :)) for the old full scan approach (used repeat=3, number=20). Thanks! \$\endgroup\$
    – alecxe
    Nov 5, 2017 at 22:35
  • \$\begingroup\$ @wchargin yes, you have a point. It's just that "use the right tool for the the job" kind of internal personality wakes up from time to time :) Thanks. \$\endgroup\$
    – alecxe
    Nov 5, 2017 at 22:37
  • 1
    \$\begingroup\$ @Toby Speight: O(aN+bM) = O(M+N), provided a and b are positive. \$\endgroup\$ Nov 6, 2017 at 14:38

This code is incorrect:

re.compile(r'\b({0})\b'.format(w), flags=re.IGNORECASE)

If w contains any metacharacters, then this will do the wrong thing. You don't want a badword list containing Mr. to match MRI, do you?

Prefer instead:

re.compile(r'\b({0})\b'.format(re.escape(w)), flags=re.IGNORECASE)

Other, general comments:

  • I agree with @alexce's suggestion to use an optimal algorithm.
  • The docstring for findWholeWord is incorrect and misleading; the function does not remove anything.
  • Follow Python naming conventions; the method should be named find_whole_word.
  • Your function find_whole_word is curried, which gives you the opportunity to take advantage of staged computation and avoid the expensive recompilation of the regex. But you don’t do this, instead recomputing findWholeWord(x)(quote) for each quote. Either stage the computation or use the simpler API def contains_whole_word(needle, haystack).
  • There’s no need for coding: utf-8. Your code is all ASCII.
  • You should end your with-block as soon as possible so that the file handle can be released. Only quotes = f.readlines() needs to be contained within the block; the rest can be dedented.
  • It’s dubious for filter_bad_words to print output and not return anything, but this is potentially excusable if this is meant to be a command-line application as opposed to a library function.
  • Similarly, filter_bad_words should probably take badwords as an argument instead of doing any file I/O.
  • It is unclear why you wrap entries in {'quote': quote} instead of just using quote.
  • Your first for-loop is much better written as badwords = [word.strip('\n') for word in badwords].
  • Nit: You indent by too much in your first for-loop and in your __main__-guard.

Instead of iterating over every element of badwords for each quote, it will be much more efficient to compile a single regexp containing all the bad words. Remember that compiling a regexp results in a state machine that simply iterates over characters until there's a match or input is exhausted, so the subsequent (worst-case) runtime is O(N) where N is the total length of all the input quotes.

Constructing this regexp is simple (I've retained the case-insensitive matching; that's probably what you want in this context; you might also consider re.UNICODE and/or re.LOCALE):

badword_regexp = re.compile('\\b(' +
                            '|'.join([re.escape(w) for w in badwords])  + 

Important: this regexp should be compiled once - not for every single search as in the existing code.

badword_regexp = re.compile('\\b(' + '|'.join([re.escape(w) for w in badwords])  + ')\\b', flags=re.IGNORECASE)

original_count = len(entries)
for key, item in entries.items():
    quote = item['quote']
    if badword_regexp.search(quote):
        del entries[key]
        print "Remove: %s" % quote
print "Removed %s items." % (original_count - len(entries))
  • \$\begingroup\$ Assuming that the state machine is minimal, doesn't this give you a runtime of $\Theta(N + M)$, where $M$ is the total length of all the badwords? \$\endgroup\$
    – wchargin
    Nov 6, 2017 at 15:37
  • \$\begingroup\$ @wchargin, yes; I meant the subsequent runtime, and I've edited to fix that. Thanks for the correction! \$\endgroup\$ Nov 6, 2017 at 15:45

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