Trends = llectTrends(username, password, terms, startDt, endDt, geo='IT', cat='0-71', gprops='news', tz='Asia/Dushanbe') For example, to get the query data for the term "pizza" in Italy, in the "Food & Drink" category, in the "news" search for Tajikistan Time, I'd type: These are all strings corresponding to the respective fields ``geo``, ``cat``, ``gprops``, and ``tz``. Just like on the Trends site, you can specify the location, category, type of search, and time zone for which you'd like to collect data. Granularity='w', sum=True, savePath="myDir/data.csv") Trends = llectTrends(username, password, terms, startDt, endDt, If left as the default ``None``, no file is saved. Default is ``False``.``savePath`` takes a string for a path to save the resultant csv. With this, the data of multiple terms can be summed together into one column. ``sum``, is an optional argument of type boolean. ``granularity`` takes a string of either ``'d'`` or ``'w'`` corresponding to daily or weekly, respectively. With the optional argument ``granularity``, the granularity can be changed from the default of daily, to weekly. the specific day within the month does not matter). The data is normalized across the entire time period and between terms such that the largest value has a float of 100.0, and all other values are scaled accordingly.ĭata is returned from [startDt, endDt), to the accuracy of the month (i.e. The dates are of type datetime, and the numbers are floats rounded to 3 decimal places.
For example, the above code snippet returns the list, ``trends``, as follows:: ``collectTrends()`` returns a 2d list of the data, of format, with an additional a header. Trends = llectTrends(username, password, terms, startDt, endDt) StartDt = datetime.datetime(year=2015, month=1, day=1)ĮndDt = datetime.datetime(year=2015, month=2, day=1)
Gtrends only contains two functions, the primary one being ``collectTrends()``. Gtrends solves this by allowing developers to extract data with either weekly or daily granularity, with the same scaling throughout, without the need to worry over scaling and data manipulation themselves. This makes it difficult, for example, to collect several files and splice them together (such as to maintain a daily granularity via files of shorter time periods), since the data are on difference scales.
Even worse, Google normalizes the data, so that the largest percent query volume in the time series is set to an integer '100,' with all other values set to smaller integer values. The data only come in daily granularity up until 3 months worth of data, after which they become weekly.
Users with Google accounts can download these data into csv files, however there are several caveats which make the data difficult to process. This volume data is represented as a fraction of the total query volume on the given day or week. `Google Trends `_ is a service offered by Google which allows access to aggregate query volume data for specific search terms, over specific periods of time. Gtrends is a Python library that eases the process of downloading Google Trend data. If there are any issues let me know in the issue tracker! Now read on. You can also specify the geographical location, timezone, category, and type of search desired. Theoretically gtrends now supports python 3.4 and 3.5, as well as 2.7.