Analysing Search Console Data in Pandas

Every SEO has pushed Excel beyond it’s limits at some point. Pandas (‘Python Data Analysis Library’) is a widely used Python library that can handle far more data than Excel/Google Sheets.

As an example, here is a Jupyter Notebook with Python / Pandas code that:

  • Upload a .CSV export from Search Console > Performance > Queries
  • Adds some data features
  • Graphs the correlations

Example data from recruitin.net:

Graphed, the correlations look like this:

Correlation v Clicks

The notable insight from the recruitin.net data is that due to very strong rankings and a relatively unknown brand, generic terms drive more clicks than brand-terms. In this particular niche the specificity (query length & number of tokens) have a very week impact on clicks.

Feel free to download the workbook from GitHub and use on your own data as you wish.

Further reading

Automatically check Google Sitelinks

When Google regularly swaps the organic sitelinks under your brand, it can be a pain checking multiple sites / markets to make sure it’s the way you want it.

An example of sitelinks, for eBay on Google UK.
An example of sitelinks, for eBay on Google UK.

To solve this problem (for me), I’ve written a simple PHP script which when run daily will check this out, and email you if there any changes.

Example email when sitelinks change

Here’s the link to the GitHub repo: https://github.com/ChrisCB/sitelinks-watch, and the direct download.

Feel free to take the code and do what you like with it, obviously no warranty, and completely as-is, imperfections and all.

I’ll be improving this in the future, if you have any requests, or pointers for improvement, let me know!