Python Code to Find Keywords to Rank Your Website at the Top

by admink

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!