Google Trends is a free tool from Google that shows the popularity of search queries on different Google properties across various regions and languages. What’s Google Trends and how does it work? Is there a way to get Google Trends data with API? How to use DataForSEO Google Trends API for search analytics? What’s Google Trends and how does it work? Besides that, we will describe a few use cases on how different businesses can take advantage of Google Trends data. In this article, we will show you what solutions to this problem exist, and what’s the most reliable way to pull vast volumes of data from Google Trends. Yet, if you’re working on a large project or planning to build proprietary software on top of Google Trends data, accessing it at scale may be challenging as there is no official API. To provide a high-level interface for drawing attractive and informative statistical graphics.Google Trends is a valuable source of data on people’s needs and interests around the globe. 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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