Skip to content 
Keywords have ceased to be a search phrase in the new online world but are the mainstay of search engine optimization (SEO), pay-per-click (PPC) internet advertisements, and content strategy. When you have tried to run a campaign with Google Ads or optimized a blog to be found on a search engine, you must have noticed how crucial it is to group the keywords into coherent sets. This is known as keyword grouping, and Python is one of the most effective tools you can use to make it faster, smarter and more accurate.
This tutorial will take you through all the information you require as a beginner to understand about keyword grouping with Python in 2025: why you want to know about it, how it works, the libraries you want to use, and a few bits of code.
What Is Keyword Grouping?
The grouping of a list of keywords into groups is what is known as keyword grouping. Thousands of keywords are easier to deal with by grouping them into meaningful sets rather than treating them individually as marketers do.
As an example, the following list of keywords is considered:
- buy running shoes online
- best jogging sneakers
- cheap marathon shoes
- luxury handbags for women
- designer purses online
In this case, the keywords 1-3 obviously fall in the category of a cluster of running shoes and the keywords 4-5 fall in the category of handbags.
Keyword grouping helps with:
- SEO Strategy: Divide the keywords into topic-based groups in order to create more focused blog posts or landing pages.
- PPC Advertising: Group the keywords into closely themed ad groups to achieve better Quality Scores.
- Content Planning: Find content gaps by viewing which groups of keywords are not developed.
Why Use Python to Group Keywords in 2025?
Although paid tools exist that process keyword clustering, Python provides you with:
- Automation: keywords Process thousands of keywords without the need for human effort.
- Flexibility: Select an alternative to clustering (e.g., semantic similarity, machine learning or rule-based).
- Scalability: Scale large data that is not feasible through the Internet.
- Savings on costs: Python is an open-source system- no costly SaaS licenses are required.
Python packages such as scikit-learn, spaCy, NLTK, and transformers (Hugging Face) will allow novices in 2025 to do complex keyword clusterings with only a handful of lines of code.
Step 1: Prepare your keywords.
You require an uncontaminated list of keywords prior to grouping. We are going to assume that we have a CSV file named keywords.csv with a column of keywords.
import pandas as pd
# Load the keywords dataset
df = pd.read_csv(“keywords.csv”)
keywords = df[“keyword”].tolist()
print(“Sample keywords:”, keywords[:10])
Cleaning is essential. Turn the keywords into lowercase, delete punctuation marks, and delete the stop words such as “the,” “and,” or “for.”
import re
stopwords which are imported as an nltk.corpus.
stopwords.words(english) is a set of stop words in English.
def clean_text(text):
text = text.lower()
text = re.sub(r”[^a-zA-Z0-9\s]”, “”, text)
return word.join(text.split(word in the stop-words))
cleaned key words: cleaned keywords = [clean_text(k) of keywords].
Step 2: mapping keywords to vectors.
A computer does not comprehend words; it comprehends numbers. Then we should convert each keyword into a list (vector) of numbers which contain the meaning.
The most popular in 2025 are:
- Word Embedding (Word 2 Vec, GloVe): train meaning.
- Transformers (BERT, Sentence Transformers): State of the art.
- model.embed(keywords-cleaned): embeddings
Example using TF-IDF (super easy to learn):
Imported as TfidfVectorizer from sklearn.feature_extraction.text.
Vectorizer = TfidfVectorizer.
X = vectorizer.fit_transform(keywords_cleaned)
Step 3: Clustering of Keywords.
We can now cluster numeric representations, since we have them. Popular clustering methods:
- K-Means Clustering: assumes that the number of clusters is specified.
- Agglomerative Hierarchical Clustering: constructs a cluster tree.
- DBSCAN: uses density to automatically identify groups.
Here’s an example with K-Means:
Import KMeans, a part of the sklearn. Cluster.
# Specify number of clusters (adaptable)
num_clusters = 5
km = KMeans(n clusters=num clusters, random_state=42)
km.fit(X)
clusters = km.labels_
## Tie clusters back to keywords.
df[“cluster”] = clusters
print(df.head(20))
Your keywords have been formed into 5 clusters now.
Step 4: Interpretation of the Results.
The result could be as shown below:
Keyword Cluster
buy running shoes online 0
best jogging sneakers 0
cheap marathon shoes 0
luxury handbags for women 1
designer purses online 1
Cluster 0 = Running Shoes
Cluster 1 = HandbagsYou can also name keywords in each cluster using useful terms, like “Athletic Footwear” or “Luxury Fashion,” etc.
Step 5: Semantic Model Advanced Grouping.
Transformer-based models are the go-to keyword grouping model in 2025 since they process the context rather than words.
As an example, consider Sentence Transformer:
sentence_transformers SentenceTransformer from sentence-transformers import SentenceTransformer
imported from sklearn.cluster: KMeans.
Load pretrained transformer model
sentence transformer = SentenceTransformer(model=SentenceTransformer, all-MiniLM-L6-v2)
convert keywords to embeddings
import fig as fig.matplotlib.pyplot.
# Cluster with K-Means
km = KMeans(n_clusters=5, random_state=42)
km.fit(embeddings)
df[“cluster”] = km.labels_
print(df.head(20))
The method is able to cluster semantically similar words even when they do not have direct word matches. For example:
Played sneakers men’s cheap and cheap running shoes would be in the same category.
Step 6: Seeing Keyword Groups.
A thousand words speak louder than pictures. You can plot your clusters in a scatter plot:
plt.scatter(reduced[0], reduced[1], c=df[cluster], cmap=rainbow)
import PCA, sklearn.decomposition
pca = PCA(n_components=2)
pca.fit_transform(embeddings) = reduced.
Scale Up Later: Transformer embeddings are used when you have larger datasets.
word in enumerate(keywords_cleaned):
plt.annotate(word, (reduced[i,0], reduced[i,1]))
plt.show()
In this manner, you can actually visualize keyword clusters in a 2D space.
The Keyword Grouping in 2025 Best Practices.
- Start Simple: TF-IDF + KMeans is a good place to start if you are a beginner.
- Label Your Clusters: You can label clusters manually so that they are easy to view when grouped.
- Hybridize with Search Intent: Hybridize not only with a word but also with user intent (informational vs transactional vs navigational).
- Keep It Iterative: You will not get the best results the first time around–adjust cluster numbers.
- E-Commerce Stores: Group keywords of things in a classification.
Real-World Applications
- SEO Agencies: Automate the process of searching keywords on the websites of clients.
- E-Commerce Stores: Cluster keywords concerning products in a category.
- Content Teams: Build blog posts around keyword groups, rather than single words.
- PPC Managers: Create highly themed Google Ads ad groups.
Conclusion
One of the most efficient methods of organizing the mess of thousands of search terms is to group keywords. Python will allow even non-experts in 2025 to automate this process and make smarter decisions about SEO, PPC, and content planning.
TF-IDF and KMeans are best to start with when you are new to the space, but you can then proceed with transformer-based embeddings to achieve more real-world semantic groupings. Never forget that the end game is not technical clustering but actionable insights and the development of content, campaigns and strategies based on the way real people search.
FAQs
Q. What does "keyword grouping" mean in SEO?
The grouping of related keywords or grouping of keywords is a process of grouping related keywords together. This aids marketers in categorizing groups of search terms into particular pages, advertisements or content.
Q. Why do I have to use Python to group keywords?
Python lets you automate the clustering of keywords, process big data, and apply more sophisticated algorithms such as natural language processing and machine learning at a free open-source cost.
Q. What Python libraries are most successful at keyword grouping?
According to the popular libraries, scikit-learn (clustering similar to KMeans), NLTK or spaCy (text cleaning and processing) and sentence-transformers (semantic embedding) are mentioned.
Q. What is the number of clusters in which I will group the keywords?
There’s no fixed number. It depends on your dataset. You may also try with various values or apply such techniques as the so-called elbow method to determine what size cluster to use.
Q. Is all that keyword grouping to do with SEO?
No. It can be used in SEO, PPC campaigns, content marketing, the categorization of e-commerce products and even in market research.
