![]() Keep calm, learn Python & do it yourself (not easy, but definitely possible).Getting a Python developer to do this for you (if you know one/can afford one).When you really want this information and the straight-forward GDS connector is not an option, a quick Google search would lead you to the understanding that the only way to get this information is by requesting this information directly from the Google Trends API, and the only way to do that is by (Python) coding. The Idea: Using the Google Trends API with Python You can download this information you get per search, but at the end of the day, you’ll probably be on this for a while exporting excels, one per keyword, joining them together, creating a visual or report. Unfortunately, there’s no Google Data Studio connector for Google Trends, so if you’re constantly on the lookout for new breaking content opportunities like me, you constantly reach this little box at the bottom of GTrends after you’ve searched for one keyword at a time per geo, per timeframe. Writer = pd.8 But wait… there’s more! Get my repo here The Challenge: Making the most out of the ‘Related Queries’ box on Google Trends ![]() #exports the three dataframes into excel and formats them. (notice how a 50 on the 0-100 scale is actually just the average weekly search volume)
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