Filtering Stopwords With NLTK
Drop the noise from a token list.
Filtering Stopwords With NLTK is a free NLP Academy lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the NLP Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Let NLTK Do the Heavy Lifting
Building your own stopword list is fine, but NLTK already ships a curated one for many languages. Let us put it to work on a token list.
Grab the Data First
NLTK keeps word lists as downloadable data. You fetch the stopwords package once, then it stays on your machine.
import nltk
nltk.download("stopwords")Load the English List
Now import the corpus and ask for English. You get back a plain list of words you can inspect or filter against.
from nltk.corpus import stopwords
stops = stopwords.words("english")
print(len(stops))Convert It to a Set
The list works, but a set makes membership checks much faster. Wrap it once and reuse it for every token.
stops = set(stopwords.words("english"))Filter With a Comprehension
A list comprehension keeps only the words that are not stopwords. This single line is the heart of stopword removal.
tokens = ["the", "quick", "brown", "fox"]
clean = [w for w in tokens if w not in stops]
print(clean)Mind the Case
The list is lowercase, so The will not match the. Lowercase your tokens first, or you will leave capitalized stopwords behind.
clean = [w for w in tokens if w.lower() not in stops]See the Difference
Before filtering you might have ten tokens; after, only the meaningful four remain. That shrink is the noise you just dropped.
Other Languages Too
NLTK is not English-only. Swap the argument to pull a stopword list for Spanish, German, French, and many more.
spanish = set(stopwords.words("spanish"))Customize the List
The list is just a set, so you can add your own domain noise to it with normal set operations before filtering.
stops.add("subject")
stops.update(["http", "www"])Or Keep a Few Back
Want to protect a word like not? Just remove it from the set so filtering never strips it out.
stops.discard("not")Filter Once, Reuse Often
Build your stops set a single time at startup, not inside a loop. Rebuilding it for every document wastes real time.
Quick Check
One detail trips up almost everyone the first time.
Recap
You can now filter tokens against NLTK stopwords: download once, build a lowercase set, and keep only words not in it. Mind the case.
Frequently asked questions
Is the “Filtering Stopwords With NLTK” lesson free?
Yes — the full text of “Filtering Stopwords With NLTK” is free to read here on the web, and the NLP Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the NLP Academy course, upgrade to CoddyKit PRO.
What will I learn in “Filtering Stopwords With NLTK”?
Drop the noise from a token list. You practise NLP Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start NLP Academy?
No prior experience is required. NLP Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Filtering Stopwords With NLTK” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this NLP Academy lesson?
Yes. Every NLP Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- What Are Stopwords?
- Filtering Stopwords With NLTK
- Stripping Punctuation and Symbols
- Building a Reusable Clean-Text Function