Managing and optimizing URLs is a crucial task for SEO professionals. Over time, websites accumulate messy, non-standard URLs with tracking parameters, redundant subdomains, and incorrect encodings. These issues can impact crawlability, indexing, and user experience. For over two decades, I’ve been at the forefront of the tech industry, championing innovation, delivering scalable solutions, and steering organisations toward transformative success. While working on NextStruggle, I optimised my website URLs for search engine performance—creating a trusted blueprint for businesses looking to redefine their search visibility and dominance.

In this tech concept, we will explore how to decode, clean, and standardize URLs at scale using Python. With powerful libraries like urllib, tldextract, and regex, SEO professionals can automate URL cleaning and ensure better search engine performance.

Why Cleaning URLs is Essential for SEO

Before diving into the technical implementation, let’s look at some common SEO problems caused by messy URLs:

1. Tracking Parameters – URLs often contain unnecessary query strings like ?utm_source=google or ?ref=1234, leading to duplicate content issues.
   • Example: https://example.com/page?utm_source=google&utm_campaign=summer should be https://example.com/page.
2. Incorrect Encoding – Special characters in URLs may be incorrectly encoded, affecting search engine readability.
   • Example: https://example.com/search?q=caf%C3%A9 should be properly decoded as https://example.com/search?q=café.
3. Non-Standard Subdomains – Some websites use unnecessary subdomains that create duplicate versions of the same content.
   • Example: https://www.example.com and https://blog.example.com may serve similar content, creating confusion for search engines.
4. Broken or Redirecting URLs – Old URLs that lead to unnecessary redirects slow down crawling and dilute page authority.

Step 1: Decoding and Normalizing URLs

The first step in URL cleaning is decoding special characters and removing unnecessary parts using Python’s urlliband requests libraries.

Using urllib to Decode URLs

from urllib.parse import unquote

# Example URL with encoded characters
url = "https://example.com/search?q=caf%C3%A9"

# Decode the URL
decoded_url = unquote(url)
print(decoded_url)  # Output: https://example.com/search?q=café

This method ensures that all encoded characters like %20 (space) or %C3%A9 (é) are converted into human-readable format.

Step 2: Removing Tracking Parameters

Many URLs include tracking parameters that are not needed for indexing. We can remove them using urllib.parse.

Stripping Unnecessary Query Parameters

from urllib.parse import urlparse, urlunparse

def remove_tracking_params(url):
    parsed_url = urlparse(url)
    clean_url = urlunparse((parsed_url.scheme, parsed_url.netloc, parsed_url.path, '', '', ''))
    return clean_url

url = "https://example.com/product?utm_source=google&utm_medium=email"
print(remove_tracking_params(url))  # Output: https://example.com/product

This function removes all query parameters while preserving the main page path.

Step 3: Extracting Root Domains

Sometimes, SEO professionals need to extract the main domain from long URLs. This is useful for analyzing backlinks, identifying duplicate content, or grouping websites by domain.

Using tldextract to Extract Domains

import tldextract

def get_root_domain(url):
    extracted = tldextract.extract(url)
    return f"{extracted.domain}.{extracted.suffix}"

url = "https://blog.nextstruggle.com/path/page.html"
print(get_root_domain(url))  # Output: nextstruggle.com

This approach ensures we extract only the root domain, ignoring subdomains or unnecessary prefixes.

Step 4: Identifying and Fixing Redirects

Handling redirects is essential for ensuring clean URL structures. Python’s requests library helps in identifying redirected URLs.

Checking for Redirects

import requests

def check_redirects(url):
    response = requests.head(url, allow_redirects=True)
    return response.url if response.history else url

url = "http://example.com/redirect"
print(check_redirects(url))  # Output: Final resolved URL

If a URL redirects, this function will return the final destination URL, allowing SEO professionals to update internal links accordingly.

Step 5: Automating URL Cleaning for Large Datasets

For large-scale SEO audits, we can apply these functions to entire datasets of URLs.

Processing a List of URLs

import pandas as pd

def clean_urls(file_path):
    df = pd.read_csv(file_path)
    df['Cleaned URL'] = df['URL'].apply(lambda x: remove_tracking_params(check_redirects(unquote(x))))
    df.to_csv("cleaned_urls.csv", index=False)
    print("Processed URLs saved to cleaned_urls.csv")

# Run the script on a CSV file
clean_urls("urls.csv")

This script takes a CSV file with a list of URLs, cleans each URL, and saves the results for further analysis.

My Tech Advice: Cleaning and standardizing URLs is a critical SEO task that improves crawlability, indexing, and ranking performance. By automating URL cleaning with Python, SEO professionals can: Remove unnecessary tracking parameters, Decode and standardise URLs for better readability, Extract root domains for backlink analysis, Detect and fix redirects to optimise link structure, Scale URL cleaning across large datasets. Implement these concepts and track your SEO visibility across search engines—just as I have done with NextStruggle.

#AskDushyant

Note: The example and pseudo code is for illustration only. You must modify and experiment with the concept to meet your specific needs.

#TechConcept #TechAdvice #Python #SEO  #URL #Processing