Python Code to Find Keywords to Rank Your Website at the Top
Finding the correct keywords in today’s competitive digital market can mean the difference between success and failure for your website. The appropriate keywords help your content coincide with what potential buyers are looking for, enhancing its visibility and rating on search engines. Fortunately, Python has powerful libraries that make keyword research simple. We’ll walk you through a Python code sample for finding useful keywords for your website.
Step 1: Install Necessary Libraries
Before diving into code, install the necessary libraries. For this task, we’ll use:
- requests for handling API requests
- beautifulsoup4 for parsing HTML data
- nltk for text processing
- pandas for handling data tables
- seotools for keyword analysis (if available)
Run this in your command line to install them:
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pip install requests beautifulsoup4 nltk pandas
Step 2: Import the Libraries
In Python, start by importing the necessary libraries.
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import requests
from bs4 import BeautifulSoup
import pandas as pd
import nltk
from nltk.corpus import stopwords
from collections import Counter
(Optional) Download NLTK Stopwords
If you’re running this for the first time, download the stopword corpus:
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nltk.download(‘stopwords’)
Step 3: Set Up URL and Extract Page Content
Choose a webpage that ranks high for keywords in your industry. For example, if you’re in digital marketing, select popular websites within that niche. Use BeautifulSoup to extract the content of the chosen page.
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url = ‘https://example.com’
response = requests.get(url)
soup = BeautifulSoup(response.text, ‘html.parser’)
# Extract visible text
text = ‘ ‘.join([p.text for p in soup.find_all(‘p’)])
Step 4: Process the Text
To make the extracted text keyword-friendly, we need to clean it up. This involves removing stopwords, punctuation, and other non-essential parts.
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stop_words = set(stopwords.words(‘english’))
words = text.split()
# Filter words to remove stopwords and non-alphabetic characters
filtered_words = [word.lower() for word in words if word.isalpha() and word.lower() not in stop_words]
Step 5: Count Keyword Frequency
Using the Counter module, calculate the frequency of each word in the text. This will give an indication of potential keywords.
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# Count word frequency
word_counts = Counter(filtered_words)
# Display the top 10 most common words
print(word_counts.most_common(10))
Step 6: Analyze Keyword Data with SEO Tools (Optional)
For a more in-depth analysis, consider using SEO tools with APIs (e.g., Google Keyword Planner or Ahrefs API) to get keyword volume, difficulty, and other metrics. Here’s an example using an API (replace YOUR_API_KEY with your actual key):
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api_key = ‘YOUR_API_KEY’
endpoint = f’https://api.keywordtool.io/v2/search/keywords?apikey={api_key}&keyword=python&metrics_location=2840&metrics_language=en’
# Fetch data
response = requests. get(endpoint)
keyword_data = response.json()
# Display keyword data in a data frame
df = pd.DataFrame(keyword_data[‘results’])
print(df.head())
Step 7: Filter Keywords Based on Relevance and Volume
After gathering keyword data, filter keywords based on relevance to your website, search volume, and keyword difficulty.
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# Example filtering
relevant_keywords = df[(df[‘search_volume’] > 1000) & (df[‘keyword_difficulty’] < 30)]
print(relevant_keywords)
Step 8: Generate Final Keyword List
Finally, export the keywords for further analysis or integration into your website.
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# Export to CSV for easy access
relevant_keywords.to_csv(‘final_keywords.csv’, index=False)
Conclusion
This Python script will help you uncover high-potential keywords for your website, providing you an advantage in your SEO efforts. This article covers all of the necessary processes for conducting keyword research, from scraping website information to analyzing keyword data. Use these tips to optimize your website and achieve first-page rankings!