![]() October through December sees an increase in searches with a decline starting in January. Those show you the seasonal trends of the searches. You might be wondering what the dips and increases mean. But for the most part, Google Trends shows that men’s fashion is a pretty stable niche. Over the span of several years you’ll see some slight dips or increases, which is normal. However, for the most part, the search volume for this niche is pretty stable. You can clearly see in the graph that there are slight dips. Here’s an example of a stable niche in Google Trends: men’s fashion. So this trending product would need to be monitored for a while longer. However, that doesn’t mean you can’t still capitalize on sales. In January we saw a sudden peak with a slight dip in February. ![]() You can see quite clearly that over the past several months there’s been skyrocketing growth. Here’s an example of a skyrocketing product in Google Trends: posture corrector. But it also allows you to see seasonal trends in one clear-cut shot. Whenever looking for a new niche, you’ll want to make sure you change your range from “Past 12 months” to “2004-present.” Doing this helps you see clearly whether the search volume is increasing or declining. Google Trends is a great tool to find a skyrocketing niche. How to Use Google Trends: 10 Features for Entrepreneurs 1. You can also find demographic insights, related topics, and related queries to help you better understand the Google trends. You can view whether a trend is on the rise or declining. To provide a high-level interface for drawing attractive and informative statistical graphics.Google Trends is trends search feature that shows the popularity of a search term in Google. To provide high-performance, easy-to-use data structures and data analysis tools. It also opens figures on your screen and acts as the figure GUI manager. It provides an implicit, MATLAB-like, way of plotting. To provide a state-based interface to matplotlib. To convert extracted data to a JSON object. To scrape and parse Google results using SerpApi web scraping library. Import libraries: from serpapi import GoogleSearch Google-search-results is a SerpApi API package. Install library: pip install google-search-results matplotlib pandas seaborn ![]() Plot_interest_over_time(google_trends_result) Print(json.dumps(google_trends_result, indent=2, ensure_ascii=False)) Plt.legend(bbox_to_anchor=(1.01, 1), loc='upper left', borderaxespad=0) Palette = sns.color_palette('mako_r', 3) # 3 is number of colors Related_queries = scrape_google_trends('RELATED_QUERIES', 'related_queries', 'Mercedes')ĭata = related_queriesįor result in data:Įxtracted_value = value Related_topics = scrape_google_trends('RELATED_TOPICS', 'related_topics', 'Mercedes') Interest_by_region = scrape_google_trends('GEO_MAP_0', 'interest_by_region', 'Mercedes')ĭata = interest_by_region Interest_over_time = scrape_google_trends('TIMESERIES', 'interest_over_time', 'Mercedes,BMW,Audi')ĭata = interest_over_timeĬompared_breakdown_by_region = scrape_google_trends('GEO_MAP', 'compared_breakdown_by_region', 'Mercedes,BMW,Audi')ĭata = compared_breakdown_by_region Return results if not results else results Results = search.get_dict() # JSON -> Python dict Search = GoogleSearch(params) # where data extraction happens on the SerpApi backend # 'q': '', # query (defined in the function)ĭef scrape_google_trends(data_type: str, key: str, query: str): # 'data_type': '', # type of search (defined in the function) # 'gprop': 'images', # by default Web Search 'date': 'today 12-m', # by default Past 12 months 'engine': 'google_trends', # SerpApi search engine If you don't need explanation, have a look at full code example in the online IDE. Response times and status rates are shown under SerpApi Status page. SerpApi handles everything on the backend with fast response times under ~2.5 seconds (~1.2 seconds with Ludicrous speed) per request and without browser automation, which becomes much faster.
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