Python can really feel intimidating when you’re not a developer. You see scripts flying round Twitter, hear individuals speaking about automation and APIs, and marvel if it’s value studying—and even potential—with out a pc science diploma.
However right here’s the reality: Search engine marketing is full of repetitive, time-consuming duties that Python can automate in minutes. Issues like checking for damaged hyperlinks, scraping metadata, analyzing rankings, and auditing on-page Search engine marketing are all doable with just a few traces of code. And because of instruments like ChatGPT and Google Colab, it’s by no means been simpler to get began.
On this information, I’ll present you learn how to begin studying.
Search engine marketing is filled with repetitive, handbook work. Python helps you automate repetitive duties, extract insights from huge datasets (like tens of hundreds of key phrases or URLs), and construct technical expertise that aid you sort out just about any Search engine marketing downside: debugging JavaScript points, parsing advanced sitemaps, or utilizing APIs.
Past that, studying Python helps you:
- Perceive how web sites and net knowledge work (consider it or not, the web is not tubes).
- Collaborate with builders extra successfully (how else are you planning to generate hundreds of location-specific pages for that programmatic Search engine marketing marketing campaign?)
- Be taught programming logic that interprets to different languages and instruments, like constructing Google Apps Scripts to automate reporting in Google Sheets, or writing Liquid templates for dynamic web page creation in headless CMSs.
And in 2025, you’re not studying Python alone. LLMs can clarify error messages. Google Colab enables you to run notebooks with out setup. It’s by no means been simpler.

LLMs can sort out most error messages with ease—irrespective of how dumb they could be.
You don’t have to be an skilled or set up a posh native setup. You simply want a browser, some curiosity, and a willingness to interrupt issues.
I like to recommend beginning with a hands-on, beginner-friendly course. I used Replit’s 100 Days of Python and extremely suggest it.
Right here’s what you’ll want to grasp:
1. Instruments to write down and run Python
Earlier than you’ll be able to write any Python code, you want a spot to do it — that’s what we name an “atmosphere.” Consider it like a workspace the place you’ll be able to kind, check, and run your scripts.
Selecting the best atmosphere is essential as a result of it impacts how simply you will get began and whether or not you run into technical points that decelerate your studying.
Listed here are three nice choices relying in your preferences and expertise degree:
- Replit: A browser-based IDE (Built-in Improvement Atmosphere), which suggests it offers you a spot to write down, run, and debug your Python code — all out of your net browser. You don’t want to put in something — simply join, open a brand new undertaking, and begin coding. It even contains AI options that will help you write and debug Python scripts in actual time. Go to Replit.
- Google Colab: A free software from Google that permits you to run Python notebooks within the cloud. It’s nice for Search engine marketing duties involving knowledge evaluation, scraping, or machine studying. You may also share notebooks like Google Docs, which is ideal for collaboration. Go to Google Colab.
- VS Code + Python interpreter: In case you desire to work regionally or need extra management over your setup, set up Visible Studio Code and the Python extension. This offers you full flexibility, entry to your file system, and help for superior workflows like Git versioning or utilizing digital environments. Go to the VS Code web site.


My weblog reporting program, inbuilt heavy conjunction with ChatGPT.
You don’t want to begin right here—however long-term, getting comfy with native growth gives you extra energy and adaptability as your initiatives develop extra advanced.
In case you’re uncertain the place to begin, go together with Replit or Colab. They eradicate setup friction so you’ll be able to deal with studying and experimenting with Search engine marketing scripts proper away.
2. Key ideas to be taught early
You don’t must grasp Python to begin utilizing it for Search engine marketing, however it’s best to perceive just a few foundational ideas. These are the constructing blocks of practically each Python script you’ll write.
- Variables, loops, and capabilities: Variables retailer knowledge like a listing of URLs. Loops allow you to repeat an motion (like checking HTTP standing codes for each web page). Capabilities allow you to bundle actions into reusable blocks. These three concepts will energy 90% of your automation. You’ll be able to be taught extra about these ideas by means of newbie tutorials like Python for Inexperienced persons – Be taught Python Programming or W3Schools Python Tutorial.
- Lists, dictionaries, and conditionals: Lists aid you work with collections (like all of your website’s pages). Dictionaries retailer knowledge in pairs (like URL + title). Conditionals (like if, else) aid you resolve what to do relying on what the script finds. These are particularly helpful for branching logic or filtering outcomes. You’ll be able to discover these subjects additional with the W3Schools Python Knowledge Constructions information and LearnPython.org’s management circulate tutorial.
- Importing and utilizing libraries: Python has hundreds of libraries: pre-written packages that do heavy lifting for you. For instance, requests enables you to ship HTTP requests, beautifulsoup4 parses HTML, and pandas handles spreadsheets and knowledge evaluation. You’ll use these in virtually each Search engine marketing process. Take a look at The Python Requests Module by Actual Python, Stunning Soup: Net Scraping with Python for parsing HTML, and Python Pandas Tutorial from DataCamp for working with knowledge in Search engine marketing audits.


These are my precise notes from working by means of Replit’s 100 Days of Python course.
These ideas might sound summary now, however they arrive to life when you begin utilizing them. And the excellent news? Most Search engine marketing scripts reuse the identical patterns repeatedly. Be taught these fundamentals as soon as and you may apply them all over the place.
3. Core Search engine marketing-related Python expertise
These are the bread-and-butter expertise you’ll use in practically each Search engine marketing script. They’re not advanced individually, however when mixed, they allow you to audit websites, scrape knowledge, construct experiences, and automate repetitive work.
- Making HTTP requests: That is how Python masses a webpage behind the scenes. Utilizing the requests library, you’ll be able to examine a web page’s standing code (like 200 or 404), fetch HTML content material, or simulate a crawl. Be taught extra from Actual Python’s information to the Requests module.
- Parsing HTML: After fetching a web page, you’ll typically wish to extract particular components, just like the title tag, meta description, or all picture alt attributes. That’s the place beautifulsoup4 is available in. It helps you navigate and search HTML like a professional. This Actual Python tutorial explains precisely the way it works.
- Studying and writing CSVs: Search engine marketing knowledge lives in spreadsheets: rankings, URLs, metadata, and many others. Python can learn and write CSVs utilizing the built-in csv module or the extra highly effective pandas library. Find out how with this pandas tutorial from DataCamp.
- Utilizing APIs: Many Search engine marketing instruments (like Ahrefs, Google Search Console, or Screaming Frog) provide APIs — interfaces that allow you to fetch knowledge in structured codecs like JSON. With Python’s requests and json libraries, you’ll be able to pull that knowledge into your personal experiences or dashboards. Right here’s a primary overview of APIs with Python.


The Pandas library is unbelievably helpful for knowledge evaluation, reporting, cleansing knowledge, and 100 different issues.
As soon as you understand these 4 expertise, you’ll be able to construct instruments that crawl, extract, clear, and analyze Search engine marketing knowledge. Fairly cool.
These initiatives are easy, sensible, and might be constructed with fewer than 20 traces of code.
1. Verify if pages are utilizing HTTPS
One of many easiest but most helpful checks you’ll be able to automate with Python is verifying whether or not a set of URLs is utilizing HTTPS. In case you’re auditing a shopper’s website or reviewing competitor URLs, it helps to know which pages are nonetheless utilizing insecure HTTP.
This script reads a listing of URLs from a CSV file, makes an HTTP request to every one, and prints the standing code. A standing code of 200 means the web page is accessible. If the request fails (e.g., the positioning is down or the protocol is flawed), it should inform you that too.
import csv import requests with open('urls.csv', 'r') as file: reader = csv.reader(file) for row in reader: url = row[0] strive: r = requests.get(url) print(f"{url}: {r.status_code}") besides: print(f"{url}: Failed to attach")
2. Verify for lacking picture alt attributes
Lacking alt textual content is a standard on-page difficulty, particularly on older pages or giant websites. Slightly than checking each web page manually, you should use Python to scan any web page and flag photographs lacking an alt attribute. This script fetches the web page HTML, identifies all tags, and prints out the src of any picture lacking descriptive alt textual content.
import requests from bs4 import BeautifulSoup url="https://instance.com" r = requests.get(url) soup = BeautifulSoup(r.textual content, 'html.parser') photographs = soup.find_all('img') for img in photographs: if not img.get('alt'): print(img.get('src'))
3. Scrape title and meta description tags
With this script, you’ll be able to enter a listing of URLs, extract every web page’s
import requests from bs4 import BeautifulSoup import csv urls = ['https://example.com', 'https://example.com/about'] with open('meta_data.csv', 'w', newline="") as f: author = csv.author(f) author.writerow(['URL', 'Title', 'Meta Description']) for url in urls: r = requests.get(url) soup = BeautifulSoup(r.textual content, 'html.parser') title = soup.title.string if soup.title else 'No title' desc_tag = soup.discover('meta', attrs={'title': 'description'}) desc = desc_tag['content'] if desc_tag else 'No description' author.writerow([url, title, desc])
4. Utilizing Python with the Ahrefs API
In case you’re an Ahrefs buyer with API entry, you should use Python to faucet immediately into our knowledge, fetching backlinks, key phrases, rankings, and extra. This opens the door to large-scale Search engine marketing workflows: auditing hundreds of pages, analyzing competitor hyperlink profiles, or automating content material reporting.
For instance, you may:
- Monitor new backlinks to your website each day and log them to a Google Sheet
- Mechanically pull your prime natural pages each month for content material reporting
- Observe key phrase rankings throughout a number of websites and spot tendencies quicker than utilizing the UI alone
Right here’s a easy instance to fetch backlink knowledge:
import requests url = "https://apiv2.ahrefs.com?from=backlinks&goal=ahrefs.com&mode=area&output=json&token=YOUR_API_TOKEN" r = requests.get(url) knowledge = r.json() print(knowledge)
You’ll want an Ahrefs API subscription and entry token to run these scripts. Full documentation and endpoint particulars can be found within the Ahrefs API docs.
Patrick Stox, aka Mr Technical Search engine marketing, is all the time tinkering with Python, and he’s made tons of free instruments and scripts freely accessible in Google Colab. Listed here are just a few of my private favorites:
- Redirect matching script: This script automates 1:1 redirect mapping by matching previous and new URLs by way of full-text similarity. Add your before-and-after URLs, run the pocket book, and let it counsel redirects for you. It’s extremely useful throughout migrations. Run the script right here.
- Web page title similarity report: Google typically rewrites web page titles in search outcomes. This software compares your submitted titles (by way of Ahrefs knowledge) with what Google truly shows, utilizing a BERT mannequin to measure semantic similarity. Preferrred for large-scale title audits. Run the script right here.
- Site visitors forecasting script: Featured in our Search engine marketing Forecasting information, this script makes use of historic visitors knowledge to foretell future efficiency. Nice for setting expectations with purchasers or making the case for continued funding. Run the script right here.


One in every of Patrick’s scripts in Colab.
Be taught extra about this forecasting script in Patrick’s information to Search engine marketing forecasting.
Ultimate ideas
Python is likely one of the most impactful expertise you’ll be able to be taught as an Search engine marketing. Even just a few primary scripts can save hours of labor and uncover insights you’d miss in any other case.
Begin small. Run your first script. Fork considered one of Patrick’s instruments. Or spend half-hour with Replit’s Python course. It received’t take lengthy earlier than you’re pondering: why didn’t I do that sooner?
Received questions? Ping me on Twitter.