You can use that and export your keywords

[rabhasan018542]

this, especially with your Google Search Console data, is you can easily extract and explore keywords that have a high click-through rate and a poor rank in search. It's one of my favorite ways to explore keyword opportunities for clients, and it couldn't be easier. So, play around with the filterable view. If you're doing keyword research, you're trying to bucket keywords, you're trying to organize topics, etc.


but you can more easily organize your keywords with Pandas. Here's how to create tanzania business email list a new column that states whether or not a keyword is 'Branded'. So to walk you through this, "df["Branded"]" creates a new column called "Branded". Then "df.Query.str.contains("moz|rand|ose")" uses regex that labels any query with those keywords as Branded = True. So now that makes filtering and exploring that so much faster! You can even do this in ways where you can create an entirely different data frame table (examples of that are also in this notebook).


into buckets like that, and there's no stall time. Things don't freeze up like Excel. You can account for misspellings and all sorts of good stuff more easily with regular expressions. It's super cool. Conclusion Again, this is just tip of the iceberg, my friends. I am most excited to plant this seed within all of you so that you guys can come back and teach me what you've been able to accomplish. I think we have so much more to explore in this space.