Interest by Region: returns data for where the keyword is most searched as shown on Google Trends' Interest by Region section. It seems like this would be the only way to get historical, hourly data. It sends multiple requests to Google, each retrieving one week of hourly data. Historical Hourly Interest: returns historical, indexed, hourly data for when the keyword was searched most as shown on Google Trends' Interest Over Time section. Multirange Interest Over Time: returns historical, indexed data similar to interest over time, but across multiple time date ranges. Interest Over Time: returns historical, indexed data for when the keyword was searched most as shown on Google Trends' Interest Over Time section. Pytrends.build_payload(kw_list, cat=0, timeframe='today 5-y', geo='', gprop='') Note: only https proxies will work, and you need to add the port number after the proxy ip address Build Payload kw_list = Note: the parameter hl specifies host language for accessing Google Trends. A dict with additional parameters to pass along to the underlying requests library, for example verify=False to ignore SSL errors.By default, backoff is disabled (set to 0). It will never be longer than Retry.BACKOFF_MAX. If the backoff_factor is 0.1, then sleep() will sleep for between retries. Pytrends = TrendReq(hl='en-US', tz=360, timeout=(10,25), proxies=, retries=2, backoff_factor=0.1, requests_args= - 1)) seconds. Or if you want to use proxies as you are blocked due to Google rate limit: from pytrends.request import TrendReq Table of Contentsīack to top API Connect to Google from pytrends.request import TrendReq Looking for maintainers! Please open an issue with a method of contacting you if you're interested. When that happens feel free to contribute! Only good until Google changes their backend again :-P. Allows simple interface for automating downloading of reports from Google Trends.
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