If you've used pytrends for the typical "trending this week" overview, you've probably hit the same wall I did: the default interest_over_time and trending_searches give you national-level signal, which is exactly what every other analyst already has. The interesting story is almost always at the regional level, and pytrends has a quietly underused method for that: interest_by_region(resolution="REGION") . I used it to answer a question that had been bugging me: when 30 of the most-talked-about fragrances of 2024-2026 are matched up across all 50 US states + DC, does every state pick the same #1, or do some go their own way? Result: 43 states pick the same fragrance. 8 states pick something completely different , and the outliers cluster in ways that turned out to be defensible (more on this below). The full analysis went up at perfumem.com , the raw CSVs and choropleth PNGs are on GitHub under CC BY 4.0 . This post is the technical walkthrough, with the gotchas I had to work around.…