Stripping Punctuation and Symbols
Clean out characters that confuse models.
Stripping Punctuation and Symbols is a free NLP Academy lesson on CoddyKit — lesson 3 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.
Punctuation Is Noise Too
After stopwords, the next clutter is symbols. Commas, dollar signs, and emoji can confuse a model, so we often strip punctuation away.
Why It Matters
Without cleanup, your model sees cat, and cat as two different tokens. That trailing comma splits one word into two features.
Python Knows the Marks
The string module hands you every common mark in one constant called punctuation, so you never type them out by hand.
import string
print(string.punctuation)The translate Trick
The fastest way to delete characters is str.translate with a table that maps each mark to nothing at all.
table = str.maketrans("", "", string.punctuation)
clean = "hello, world!".translate(table)
print(clean)Per-Token Cleaning
You can also strip marks token by token. Apply translate inside a comprehension, then drop any token that became empty.
clean = [t.translate(table) for t in tokens]
clean = [t for t in clean if t]Regex for Symbols
Need more control than the fixed list? A regex can wipe anything that is not a letter, number, or space.
import re
clean = re.sub(r"[^a-zA-Z0-9 ]", "", text)Beware of Useful Marks
Some symbols carry meaning. Stripping every dot turns u.s.a into usa, and that may not be what you want for your task.
Numbers Are a Choice
Digits are not punctuation, but they are often noise. Decide on purpose whether to keep or drop numbers for your data.
no_digits = re.sub(r"\d+", "", text)Watch Unicode Symbols
The ASCII punctuation set misses curly quotes and emoji. A Unicode-aware regex catches the symbols a fixed list leaves behind.
clean = re.sub(r"[^\w\s]", "", text)Collapse Extra Spaces
Removing marks can leave gaps and double spaces. A quick whitespace squeeze tidies the result back into clean words.
clean = re.sub(r"\s+", " ", clean).strip()Order of Operations
Sequence matters. Strip punctuation before you split or compare, so trailing marks never sneak into your final tokens.
Quick Check
Pick the cleanest way to delete punctuation from a string.
Recap
You can now strip punctuation with translate or regex, handle Unicode and numbers on purpose, and squeeze leftover spaces for clean tokens.
Frequently asked questions
Is the “Stripping Punctuation and Symbols” lesson free?
Yes — the full text of “Stripping Punctuation and Symbols” 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 “Stripping Punctuation and Symbols”?
Clean out characters that confuse models. 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 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Stripping Punctuation and Symbols” 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