How Python and Google Colab Can Make Your SEO Life Easier

10 min read

If you’re an SEO professional and you haven’t started using Python yet, you’re missing out on a serious productivity boost.

But here’s the good news: thanks to LLMs like ChatGPT, Claude, or Gemini, you don’t need to be a coding expert anymore. These AI assistants can write Python code for you. All you need to know is what’s possible and how to run it.

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That’s exactly what this article is about. I won’t dive deep into code syntax—any LLM can handle that for you. Instead, I’ll show you the possibilities that Python + Google Colab unlock for SEO, and help you take those first steps that often feel like the hardest part.

But before we get into all the cool stuff, let’s start with the basics: what exactly are Python and Google Colab?

Python and Google Colab: What Are They?

What Is Python?

Python is a programming language. Think of it as a way to give precise instructions to your computer: “take this file, filter these rows, export the result.” That’s it.

What makes it relevant for SEO is that thousands of ready-made tools (called “libraries”) already exist. Need to scrape a site? There’s a library. Analyze 100,000 URLs? There’s a library. Generate PDF reports? Library. You don’t reinvent the wheel—you just assemble existing pieces.

What Is Google Colab?

Google Colab (short for Colaboratory) is a free, cloud-based platform that lets you write and run Python code directly in your browser. No installation required. No configuration headaches. Just open it and start coding.

It’s built on Jupyter Notebooks, which is a format that combines code, text, and visualizations in a single document. The file format is .ipynb (which stands for IPython Notebook). Think of it as a Word document, but for code—you can write explanations, run code blocks, and see results all in one place.

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The key difference from running Python locally: With Google Colab, everything runs on Google’s servers. You don’t need a powerful computer, and you get access to free GPU/TPU resources for heavy computations. Plus, your notebooks are automatically saved to Google Drive.

Why Google Colab Over Other Solutions?

There are other ways to run Python: installing it locally, using VS Code, or other cloud platforms like Kaggle or Deepnote. But for SEO work, Google Colab hits the sweet spot:

Zero setup – Just open your browser and go
Free – Including access to GPUs for AI tasks
Google Drive integration – Easy file management and sharing
Collaboration – Share notebooks like Google Docs
Gemini built-in – Google’s AI assistant is integrated directly to help you code and debug

LEFT SIDE labeled "WITHOUT PYTHON" in red/orange (#E57373): show icons of Excel spreadsheet, clock showing long time, frustrated person, multiple manual steps with arrows. RIGHT SIDE labeled "WITH PYTHON + GOOGLE COLAB" in purple (#9C89B8): show a simple 3-step flow (Prompt → Code → Results), happy person, lightning bolt for spee

Your First Script in 5 Minutes

Enough theory. Let’s get your hands dirty with a real example that works every time.

The goal: Create a simple script that checks if your meta titles are too long (over 60 characters) or too short (under 30 characters).

Step 1: Open Google Colab

Go to colab.research.google.com and click “New notebook”. That’s it—you now have a blank canvas ready to run Python code.

Step 2: Ask an LLM to Write the Code

Open ChatGPT, Claude, or any LLM you prefer. Copy-paste this prompt:

Meta Title Length Checker

BEGINNER FRIENDLY

Write me a simple Python script for Google Colab that: 1. Lets me paste a list of meta titles (one per line) 2. Checks each title’s character count 3. Flags titles that are too long (>60 chars) or too short (<30 chars) 4. Displays the results in a clear table Keep it simple, no external files needed. The user should just be able to paste their titles directly in the code.

Step 3: Copy the Code to Colab

The LLM will give you a block of code. Copy it entirely and paste it into the empty cell in your Google Colab notebook.

Step 4: Run It

Click the “Play” button on the left of the cell (or press Ctrl+Enter). The script runs, and you see the results right below.

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Congratulations! You just ran your first Python script. The whole process took less than 5 minutes, you didn’t write a single line of code yourself, and you now have a reusable tool for checking meta titles. This is exactly how I work every day.

From here, you can ask the LLM to modify the script: “Add a column showing how many characters to remove” or “Export the results to a CSV file.” The possibilities grow as you experiment.

How I Use Python and Google Colab (My Workflow)

Let me share my personal workflow, because it took me a while to figure out the most efficient way to work with these tools.

Step 1: Ask an LLM to generate a complete .ipynb file

Instead of asking ChatGPT or Claude for code snippets that I’d have to copy-paste, I ask them to generate a complete .ipynb file with well-structured cells. This way, when I import it into Google Colab, I get a nicely organized notebook with clear sections rather than one giant block of code.

Step 2: Import and run

Once I have my notebook file, I upload it to Google Colab and click “Run all” to execute all cells sequentially. That’s it—the script does its magic.

Step 3: Debug if needed

Sometimes things don’t work on the first try. That’s normal. Having multiple cells makes debugging much easier—you can identify exactly which part failed and fix it without re-running everything.

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Why multiple cells matter: Before running your main script, you often need to install dependencies first. Splitting your code into cells lets you install packages in one cell, import libraries in another, and run your actual code in the next. It also makes your notebook way easier to navigate and maintain.

Cool Things You Can Do in SEO with Python and Google Colab

Now for the fun part. Here’s a (non-exhaustive) list of what you can accomplish with Python for your SEO work.

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Web Scraping

Beautiful Soup + requests

Extract data from websites: competitor analysis, content audits, price monitoring. Fetch HTML and parse specific elements like H1 tags, prices, or any CSS selector.

response = requests.get(‘url’)
soup = BeautifulSoup(response.text, ‘html.parser’)
soup.find_all(‘h1’)

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Analyzing Large Datasets

Pandas

Handle 500,000+ row CSVs with ease. Merge data from GSC, GA4, Ahrefs, and Screaming Frog into one unified table. All your SEO data in one place.

import pandas as pd
df = pd.read_csv(‘data.csv’)
merged = pd.merge(gsc_df, ahrefs_df, on=’url’)

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Forecasting SEO Performance

Facebook Prophet

Predict future traffic patterns from historical Search Console data. Automatically captures trends, seasonality, and exceptional events. Perfect for setting realistic goals.

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Generating PDFs & PowerPoints

reportlab, fpdf2, python-pptx

Automate report generation. Create PDFs from scratch or convert HTML. Generate PowerPoint presentations with text, images, and charts. Monthly client reports on autopilot!

prs = Presentation()
slide = prs.slides.add_slide(layout)

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Building Apps with Streamlit

Streamlit

Create web applications with a user interface—without being a web developer. Build dashboards, internal tools, or client-facing apps using pure Python.

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Browser Automation

Selenium

Automate browser actions: clicking buttons, filling forms, taking screenshots. Essential for testing, repetitive tasks, or scraping JavaScript-heavy sites.

driver = webdriver.Chrome()
driver.get(‘https://site.com’)
driver.find_element(By.ID, ‘btn’).click()

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AI Models (Free!)

Hugging Face Transformers

Free, unlimited access to thousands of pre-trained AI models. Content classification, sentiment analysis, entity extraction, automatic Q&A—no paid APIs needed.

pip install transformers
from transformers import pipeline
classifier = pipeline(‘sentiment-analysis’)

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Image Processing

Pillow

Format conversions (JPG, PNG, WebP), resizing, cropping, compression, filters, watermarks. Batch-convert 500 images to WebP for better Core Web Vitals.

from PIL import Image
img = Image.open(‘photo.jpg’)
img.save(‘photo.webp’, ‘WEBP’, quality=80)

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Connecting to APIs

requests + any API

Connect to LLM APIs (OpenAI, Anthropic), DataForSEO, SERP API, Thot SEO, Search Console API, and many more. Automate anything that has an API.

import requests
response = requests.post(api_url, headers=headers, json=data)

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Coding with Gemini

Built into Google Colab

Gemini is directly integrated into Google Colab. Hit an error? Ask Gemini to explain it and suggest fixes. It’s particularly good at debugging—trust me.

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Analyzing Files

ydata-profiling

Upload files through the browser or generate an “Import files” button in your code. ydata-profiling automatically generates comprehensive EDA reports from any CSV.

from ydata_profiling import ProfileReport
profile = ProfileReport(df)
profile.to_notebook_iframe()

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Downloading Files

google.colab.files

Once your script has processed data and created an output file, download it directly to your computer with one line of code.

from google.colab import files
files.download(‘your_file.csv’)

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Previewing Websites

requests + IPython.display

Fetch a webpage’s HTML and display it directly in your notebook. Quick way to check a page without leaving Colab (JS-heavy sites may not render correctly).

response = requests.get(url)
display(HTML(response.text))

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Embedding HTML/CSS/JS

IPython.display

Enrich your notebooks with custom HTML, CSS, and JavaScript. Create custom visualizations, interactive dashboards, or animations from your Python data.

from IPython.display import HTML, display
display(HTML(f”””<div>…</div>”””))

Extra Tips for Google Colab

Keyboard Shortcuts (Windows)

These shortcuts can save you a lot of time once you get used to them:

Import Notebooks from GitHub

Found a great notebook on GitHub? You can import it directly into Google Colab with one click. Go to “File” > “Open notebook” > “GitHub” tab, then enter either the creator’s username or the repository URL containing the notebook(s).

[IMAGE PLACEHOLDER: Screenshot of Google Colab’s “Open notebook” dialog showing the GitHub import interface]

Structure Your Notebook with Headings

Just like you structure an article with headings for SEO, you can do the same in Google Colab. These headings create collapsible sections, making your notebook much easier to navigate.

To add headings, create a text cell and use hashtags:

# = H1
## = H2
### = H3
And so on…

Using R Instead of Python

If you’re more comfortable with R, Google Colab supports it too! By default, execution is set to Python, but you can change it in the runtime settings (top-right corner of the interface) and select “R” as the runtime type.

how to use r programming langage in collab

What Should You Automate First?

Not sure where to start? Take this quick quiz to find your first Python project.

What takes the most time in your daily SEO work?

📊 Manipulating data in spreadsheets

📄 Creating reports for clients

🔍 Collecting information from websites

🔁 Repetitive manual tasks

How comfortable are you with technical tools?

🌱 Complete beginner — I’ve never coded anything

📈 Intermediate — I use formulas in Excel/Sheets

⚡ Advanced — I’ve tried some coding before

Your first project: Data Analysis with Pandas

Start by merging your GSC and Screaming Frog exports into one unified file. Ask an LLM for this prompt:

“Write a Python script for Google Colab that merges two CSV files (one from Google Search Console, one from Screaming Frog) based on the URL column, and exports the result.”

Your first project: Automated PDF Reports

Create a script that generates a PDF summary from your SEO data. Ask an LLM for this prompt:

“Write a Python script for Google Colab that takes a CSV file with URL, clicks, and impressions columns, and generates a simple PDF report with a summary table and basic stats.”

Your first project: Simple Web Scraping

Build a scraper to extract H1 tags and meta descriptions from a list of URLs. Ask an LLM for this prompt:

“Write a Python script for Google Colab that takes a list of URLs and extracts the H1 tag and meta description from each page, then exports the results to CSV.”

Your first project: Browser Automation

Automate a task you do repeatedly in your browser. Start simple—think about what you click on every day. Ask an LLM for this prompt:

“Write a Python script using Selenium for Google Colab that opens a list of URLs one by one and takes a screenshot of each page.”

Final Thoughts

I hope this article has inspired you to give Python and Google Colab a try. There are probably dozens more use cases I haven’t mentioned here—the possibilities really are endless.

The barrier to entry has never been lower. With LLMs writing code for you and Google Colab handling all the technical setup, there’s nothing stopping you from automating tedious tasks, analyzing data at scale, and building tools that make your SEO work faster and smarter.

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The hardest part is taking that first step. Open Google Colab, ask an LLM to generate a simple script for something you do manually today, and run it. Once you see the magic happen, you’ll never look back.

If you have use cases you’d like me to add to this article, feel free to reach out!

About the author:

Ian Sorin is an SEO consultant at Empirik, a digital marketing agency based in Lyon, France. He uses Python and Google Colab daily for his SEO analyses—from processing large datasets and automating reports to building custom tools that make his workflow faster and more efficient.

How Python and Google Colab Can Make Your SEO Life Easier