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I cannot recommend enough that you check out his slides on Python and his extremely useful and easily accessible guide on using Jupyter Notebooks – all contained in this handy Dropbox.

For me, Python was something that always seemed difficult to comprehend – I didn’t know where the scripts I was trying to use were going, what was working, what wasn’t and what output I should expect.

If you’re in that situation, read Lord’s guide. It will help you realize that it doesn’t need to be that way and that working with Python in a Jupyter Notebook is actually more straightforward than you might think.

It will also put each technique referenced in this article easily within reach, and give you a platform to conduct your own research and set up some powerful Python automation of your own.

Getting Your SERP Data

As an employee, I’m lucky to have access to Conductor where we can run SERP reports, which use an external API to pull SERP-displayed metadata for a set of keywords.

This is a straightforward way of getting the data we need in a nice clean format we can work with.

It looks like this:

Conductor SERP report

Another way to get this information at scale is to use a custom extraction on the SERPs with a tool like Screaming Frog or DeepCrawl.

I have written about how to do this, but be warned: it is maybe just a tiny little insignificant bit in violation of Google’s terms of service, so do it at your own peril (but remember, proxies are the perfect antidote to this peril).