I’ve been asking myself whether Python is easy to learn.
The answer I’ve landed on so far is slightly annoying:
Yes. And no.
Python is probably one of the more approachable programming languages I’ve tried to understand. I can write a small script, run it and see something useful happen surprisingly quickly.
But “easy to start” and “easy to learn” are two very different things.
I’m learning Python as someone who doesn’t want to become a software developer just “yet“. I work in SEO and spend a lot of time around websites and data, and I’m also learning more about cybersecurity.
So I’m not approaching Python because I want a new job title.
I’m learning it because I keep encountering problems where knowing how to code would be useful.
And that has made the experience very different from what I expected.
Python Is Easier When You Have a Reason to Learn It
I think one of my previous mistakes with programming was trying to learn it without having a problem to solve.
You learn about variables.
Then lists.
Then loops.
Then functions.
Then somebody asks you to write a program that calculates the area of a triangle.
You complete the exercise.
And then you wonder what any of this has to do with your actual life.
This time, I have reasons.
I work with large lists of URLs.
I clean keyword data.
I compare spreadsheets.
I analyse Search Console exports.
I check metadata.
I investigate redirects.
I work with APIs.
I deal with repetitive tasks that sometimes make me think:
“Surely I shouldn’t be doing this manually.”
Suddenly, a loop isn’t just something from a programming tutorial.
It’s a way to repeat the same action across 5,000 URLs without doing it 5,000 times myself.
That makes Python much easier to learn.
The Basic Syntax Isn’t the Hardest Part
Python has a reputation for being beginner-friendly, and I can see why.
You can do useful things without writing huge amounts of code.
Even something as simple as:
for url in urls:
print(url)is relatively easy to look at and understand.
Take every URL in my list and print it.
I’m simplifying, obviously, but there’s something satisfying about being able to read code and roughly understand what it’s trying to do.
The harder part comes later.
It’s understanding how everything connects.
What type of data am I working with?
Why is this function returning something I didn’t expect?
Why did the script work perfectly on nine URLs and completely fall apart on number ten?
Why is this value a string when I thought it was a number?
Why does adding one small feature break something that worked five minutes ago?
That’s when “Python is easy” starts needing an asterisk.
The First Time Something Works Is Dangerous
There is a very specific feeling when your first useful Python script works.
You run it.
Nothing explodes.
A file appears.
You open it.
The data is actually there.
And for approximately thirty seconds, you think:
I am basically a software engineer now.
Then you try to add one more feature.
Error.
Fix that error.
Different error.
Google the error.
Change something.
Now the original feature doesn’t work.
Welcome back to being a beginner.
I’ve started to enjoy that cycle, though.
Every time something breaks, I’m forced to understand a little more about what the code is actually doing.
That’s probably where most of the learning happens.
AI Makes Python Feel Much Easier
I can’t talk about learning Python in 2026 without talking about AI.
The experience of learning programming today is completely different from learning it ten or fifteen years ago.
If I get an error, I can paste it into an AI tool and ask what happened.
If I don’t understand a line of code, I can ask for an explanation.
If I have an idea, I can describe it:
I have a CSV containing URLs. I want to check the status code of every URL and export the results.
And within seconds, I can have working – or at least almost working – code.
That’s incredible.
But there’s a trap.
Getting the Code Isn’t the Same as Learning Python
This is something I’m becoming increasingly conscious of.
AI can make me look much better at Python than I actually am.
I can ask it to build a script containing concepts I don’t understand.
Run the script.
Get the output.
And technically I’ve “built” something.
But what happens when it breaks?
What happens when the data is wrong?
What happens when I need to change the script?
What happens when AI has made an incorrect assumption that I don’t know enough to notice?
That’s why I’m trying not to measure my Python progress by the complexity of the scripts I can generate.
I’m more interested in whether I can understand them.
Can I explain what the loop is doing?
Can I change the condition?
Can I add another field?
Can I understand the error?
Can I recognise when the output doesn’t make sense?
AI can write code for me. I still want to know what the code is doing.
Being a Non-Developer Is Actually Helping
This surprised me.
I thought not being a developer would be purely a disadvantage.
In some ways, it probably is.
There are fundamental computer science concepts that I don’t know (funny, I did computer science in college lol). Developers will look at problems differently because they’ve spent years building software.
But I also have something useful:
Problems.
Real ones.
I don’t need to invent reasons to practise Python.
SEO gives me plenty.
Take a simple URL checker.
There are already excellent SEO crawlers that can check thousands of URLs far better than anything I’m going to build as a beginner.
But if I build my own basic version, suddenly I’m learning about:
- HTTP requests
- status codes
- redirects
- HTML
- parsing
- loops
- conditions
- error handling
- dataframes
- exporting data
I’m not building it because the SEO industry desperately needs another crawler.
I’m building it because one small SEO problem can teach me ten programming concepts.
That’s much more motivating than another abstract exercise.
Python Is Also Changing How I Think About SEO
This might be the most interesting part of learning it.
I’m starting to look at repetitive SEO tasks differently.
Instead of automatically opening a spreadsheet, I’m asking:
Could Python do this?
Instead of assuming two datasets need to be manually combined, I’m wondering whether I can write something that combines them.
Instead of manually checking a group of pages, I’m wondering whether I can build a script that identifies the unusual ones.
That doesn’t mean Python is always the answer.
Sometimes Excel is faster.
Sometimes Screaming Frog already does exactly what I need.
Sometimes writing a script to save ten minutes would take me three hours.
And sometimes the best automation is simply not automating something.
But learning Python is adding another option to the way I solve problems.
That’s valuable even if I never become particularly advanced.
Cybersecurity Is Giving Me Another Reason to Keep Going
SEO was my original reason for becoming interested in Python.
Cybersecurity has given me another one.
As I’ve started learning more about security, I’ve seen Python appear repeatedly.
Working with files.
Processing logs.
Analysing data.
Interacting with systems.
Automating repetitive tasks.
Investigating information.
That overlap makes Python particularly interesting to me because it isn’t tied to one part of my career.
It’s a general technical skill.
SEO gives me one environment to practise it.
Cybersecurity gives me another.
AI gives me another.
Data gives me another.
That’s when learning starts feeling less like “I should learn Python” and more like building a capability I can carry between different interests.
You Don’t Need to Memorise Everything
This has been another useful lesson.
At first, programming can make you feel like you’re supposed to remember everything.
Syntax.
Methods.
Functions.
Libraries.
Commands.
I don’t.
I look things up constantly.
And I’m becoming increasingly comfortable with that.
The important part, at least at my level, is understanding the concepts.
I need to know that I can loop through a list.
I don’t necessarily need to remember the exact syntax every time.
I need to understand what a function does.
I can look up how to write one.
I need to understand that error handling exists and why I need it.
I don’t need every possible exception memorised.
The more I use these things, the more naturally some of the syntax sticks anyway.
So, Is Python Easy to Learn?
I think Python is easy to start learning.
That’s an important distinction.
You can go from knowing very little to writing something that works relatively quickly.
Becoming genuinely good at Python is another matter.
There’s an enormous difference between writing a small automation script and building reliable software.
I’m nowhere near the second one.
And that’s fine.
I’m not trying to speedrun my way into calling myself a Python developer.
I want to become capable enough that when I encounter a problem in SEO, cybersecurity, data or something else, code becomes one of the tools I can consider using.
That’s my measure of progress.
Not:
“Do I know Python?”
But:
“Can I do something today that I couldn’t do before I started learning it?”
Right now, the answer is yes.
So maybe Python isn’t easy.
Maybe it’s just accessible enough to keep you curious.
For me, that’s more important.
