Implementation:Scikit learn Scikit learn StopWords
| Knowledge Sources | |
|---|---|
| Domains | Natural Language Processing, Text Preprocessing |
| Last Updated | 2026-02-08 15:00 GMT |
Overview
Concrete tool providing a predefined set of English stop words for text feature extraction provided by scikit-learn.
Description
The _stop_words module defines ENGLISH_STOP_WORDS, a frozenset of common English stop words sourced from the Glasgow Information Retrieval Group. Stop words are common words (such as "the", "a", "is", "in") that are typically filtered out during text preprocessing because they carry little meaningful information for text analysis tasks. This set is used internally by scikit-learn text vectorizers when stop_words='english' is specified.
Usage
Use the ENGLISH_STOP_WORDS set when you need a standard list of English stop words for text preprocessing, or when configuring CountVectorizer or TfidfVectorizer with the stop_words='english' parameter. It can also be extended or customized as needed for domain-specific applications.
Code Reference
Source Location
- Repository: scikit-learn
- File: sklearn/feature_extraction/_stop_words.py
Signature
ENGLISH_STOP_WORDS = frozenset([
"a", "about", "above", "across", "after", "afterwards", "again", ...
])
Import
from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
I/O Contract
Inputs
| Name | Type | Required | Description |
|---|---|---|---|
| (none) | N/A | N/A | ENGLISH_STOP_WORDS is a constant frozenset; it takes no inputs. |
Outputs
| Name | Type | Description |
|---|---|---|
| ENGLISH_STOP_WORDS | frozenset of str | A set of 318 common English stop words from the Glasgow Information Retrieval Group. |
Usage Examples
Basic Usage
from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
# Check if a word is a stop word
print("the" in ENGLISH_STOP_WORDS) # True
print("python" in ENGLISH_STOP_WORDS) # False
# Use with CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(stop_words='english')
X = vectorizer.fit_transform(["This is a sample document about python"])
print(vectorizer.get_feature_names_out())
# Words like 'this', 'is', 'a' will be filtered out