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"Description": "'Google Trends' provides cross-sectional and time-series\ndata on searches, but lacks readily available longitudinal\ndata. Researchers, who want to create longitudinal 'Google\nTrends' on their own, face practical challenges, such as\nnormalized counts that make it difficult to combine\ncross-sectional and time-series data and limitations in data\nformats and timelines that limit data granularity over extended\ntime periods. This package addresses these issues and enables\nresearchers to generate longitudinal 'Google Trends' data. This\npackage is built on 'pytrends', a Python library that acts as\nthe unofficial 'Google Trends API' to collect 'Google Trends'\ndata. As long as the 'Google Trends API', 'pytrends' and all\ntheir dependencies are working, this package will work. During\ntesting, we noticed that for the same input (keyword, topic,\ndata_format, timeline), the output index can vary from time to\ntime. Besides, if the keyword is not very popular, then the\nresulting dataset will contain a lot of zeros, which will\ngreatly affect the final result. While this package has no\ncontrol over the accuracy or quality of 'Google Trends' data,\nonce the data is created, this package coverts it to\nlongitudinal data. In addition, the user may encounter a 429\nToo Many Requests error when using cross_section() and\ntime_series() to collect 'Google Trends' data. This error\nindicates that the user has exceeded the rate limits set by the\n'Google Trends API'. For more information about the 'Google\nTrends API' - 'pytrends', visit\n.",
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