Now comes the best part, where we can see all the potential that Pytrends has and all the data it offers us.ĭepending on each project, very different data may be of interest, so we will only see how to apply only the data that can offer the most relevant information. can be used) or tz, to indicate the time offset. Other less used parameters: Parameters such as grpop can also be used, which establishes a filter based on the search (news, images, etc.We can use abbreviations like ES or even more specific areas. In the case of the example, we would extract the data of the last 12 months, but we can also use all for a complete history or specific dates. Timeframe: We indicate the date from which we want to extract the data.Later we will see how we can get the most out of this parameter. These categories are referenced with specific numbers. Cat: I think this is something really interesting, since Google Trends classifies your queries based on a series of specific or thematic categories.Words_list: It will take into account the list of words that we have associated with the previous variable.pytrends.build_payload(words_list, cat=0, timeframe="today 6-m", geo="ES") Basically, we add here what elements this tool must take into account when requesting information. The third line is made up of much more specific parameters to make the request. In the second line we establish a variable, where we can put one or more words to analyze. For example, in order to avoid SSL errors on the request. Request_args: It allows us to add other types of parameters.Backoff_factor: A delay is created between attempts.Retries: Number of attempts to connect to the server.
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