I’ve been telling myself I should learn Python properly for a while.
Like many people working in SEO, I’ve seen the examples.
Automate keyword research.
Analyse thousands of URLs.
Pull data from APIs.
Process log files.
Build reports.
Automate repetitive tasks.
It all sounds useful.
But “Python is useful for SEO” was never quite enough motivation for me to stick with it.
What’s changed is that I’m starting to see Python as something bigger than an SEO automation tool.
I’m learning cybersecurity. I’m becoming more interested in data. AI is changing how I work. And I’m constantly looking at repetitive SEO tasks and wondering whether there’s a better way to do them.
Python seems to sit somewhere in the middle of all of that.
So this time, I want to learn it properly.
I’m not trying to become a software developer
This is probably worth saying upfront.
My goal isn’t to change careers and become a Python developer.
I want to be an SEO who can code.
There’s a difference.
I don’t need to know everything about software engineering to get value from programming.
What I want is enough knowledge to recognise when a problem could be solved with code — and enough ability to actually build the solution.
That could be something incredibly simple.
If a task takes two hours every week and a small script can reduce it to five minutes, that’s useful to me.
SEO involves more repetitive work than we admit
There are parts of SEO that require creativity, judgement and experience.
Then there are parts that involve doing the same thing 500 times.
Checking URLs.
Cleaning keyword data.
Finding duplicates.
Comparing title tags.
Looking for redirect chains.
Grouping pages.
Extracting information.
Combining exports from different tools.
Analysing large spreadsheets.
Anyone who has worked in SEO for long enough has probably stared at a spreadsheet and thought:
There has to be a better way to do this.
Sometimes there is.
That’s one of the reasons Python interests me.
I want to work with larger amounts of data
Excel and Google Sheets are incredibly useful.
I’m not abandoning either.
But SEO datasets can get big very quickly.
A small website might have a few hundred URLs.
A large website can have hundreds of thousands.
Add keyword data, crawl information, backlinks, Search Console exports and analytics data and suddenly you’re dealing with a lot of information.
I want to become more comfortable processing that data programmatically.
Not because spreadsheets are bad.
Because I don’t want the size of a spreadsheet to determine what questions I’m able to ask.
APIs are another big reason
SEO tools contain huge amounts of useful information.
So do Google Search Console and other platforms we work with.
Most of the time, we access that information through an interface.
Click.
Filter.
Export.
Open spreadsheet.
Repeat.
Learning Python opens another door.
Instead of always working through the interface, I can start learning how to request data directly, process it and turn it into something useful.
That’s where programming starts becoming genuinely interesting to me.
You’re no longer limited to using tools exactly how somebody else designed them.
You can begin connecting things yourself.
I want to automate the boring parts, not the thinking
Whenever automation comes up in SEO, there’s a temptation to automate everything.
I’m not interested in that.
Some things should require human judgement.
Which page should we prioritise?
Does this content actually help someone?
Why did performance change?
What does the client need?
Is this recommendation realistic?
Python shouldn’t make those decisions for me.
What it can potentially do is remove some of the repetitive work surrounding those decisions.
Instead of spending an hour preparing data, maybe I spend five minutes.
Then I can spend the remaining time thinking about what the data actually means.
That’s the kind of automation I want.
AI has made learning Python more interesting
There’s also an obvious question:
Why learn programming when AI can already write code?
I’ve thought about that.
I can ask an AI tool to write a Python script today.
The problem is that if I understand absolutely nothing about Python, I’m trusting something I can’t properly evaluate.
When the script breaks, what do I do?
When the output looks strange, how do I know whether the problem is the data, the logic or the code?
When I need to modify something, do I understand what I’m changing?
AI actually makes me want to understand programming more, not less.
I don’t necessarily need to memorise every piece of syntax.
But I want to understand the logic.
Cybersecurity gave me another reason to learn it
My cybersecurity learning has added another dimension.
Python appears there too.
Automation.
Log analysis.
Working with files.
Processing information.
Investigating data.
Suddenly, Python isn’t something that belongs to my “SEO learning” folder.
It connects several areas I’m interested in.
That’s useful because I’m much more likely to stick with something when I can apply it to real problems.
Instead of learning Python for the sake of learning Python, I can ask:
Can I use this for SEO?
Can I use this for cybersecurity?
Can I automate something I currently do manually?
Those questions make learning much more practical.
I want to start with small SEO projects
I’m deliberately keeping my ambitions realistic.
I’m not going to start by building some enormous AI-powered SEO platform.
I’d rather build small scripts that solve actual problems.
Things like cleaning keyword exports, checking lists of URLs, analysing title tags, processing crawl data, working with Search Console exports or identifying patterns across pages.
Then I’ll move into APIs.
Then larger datasets.
Then perhaps more interesting combinations of SEO, Python and AI.
Some scripts will probably be terrible.
That’s fine.
The point is to build them.
I also want to understand what’s happening
One thing I’m trying to avoid is becoming dependent on copying code I don’t understand.
It’s very easy now.
Ask AI for a script.
Copy it.
Run it.
Celebrate when it works.
But I want to be able to look at that script and roughly explain what each part is doing.
Variables.
Loops.
Functions.
Conditions.
Libraries.
Inputs.
Outputs.
Errors.
I’m still learning these things.
Sometimes slowly.
But understanding the foundations means AI becomes something that helps me code rather than something that codes while I watch.
This isn’t about adding “Python” to my LinkedIn skills
I want something more useful than that.
I want to reach the point where I’m doing an SEO task and naturally think:
“I could automate this.”
Then open Python and try.
Maybe the first attempt doesn’t work.
Maybe I spend longer writing the script than it would have taken to complete the task manually.
That’s part of learning.
The second time will be faster.
The tenth time might save hours.
I think the role of an SEO is becoming more technical
Not every SEO needs to code.
I still believe that.
SEO has room for brilliant content strategists, digital PR specialists, local SEO experts, technical SEOs, analysts and people with completely different strengths.
But search itself is becoming more complicated.
We’re dealing with traditional search engines, AI systems, APIs, structured data, JavaScript-heavy websites, increasingly large datasets and more automation.
For the kind of SEO professional I want to become, understanding code feels increasingly valuable.
I want Python to expand what I’m capable of
That’s really the goal.
I’m not learning Python because I think every SEO needs to.
I’m learning it because I keep finding problems where I can see how programming might help.
SEO gave me the first reason.
Cybersecurity gave me another.
AI made it easier to experiment.
Curiosity is doing the rest.
I’m still very much learning.
I’ll forget syntax.
I’ll write inefficient scripts.
I’ll probably spend an embarrassing amount of time debugging something caused by one tiny mistake.
But eventually, I want to move from:
“I wonder if Python could do this?”
to:
“Give me a few minutes. I’ll build something.”
That’s the skill I’m really trying to learn.
