Graphed, the correlations look like this:

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:

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

• Getting started with Jupyter
• Getting started with Pandas
• This excellent article on Machine learning for SEO which provided inspiration for this post