You can also plot the top 10 if you wish, using ibr.sort_values(ascending=False).plot.bar(). Let's sort the countries by interest in Python: # sort the countries by interest We set inc_low_vol to True so we include the low search volume countries, we also set inc_geo_code to True to include the geocode of each country. Other possible values are 'CITY' for city-level data, 'DMA' for Metro-level data, and 'REGION' for region-level data. We pass "COUNTRY" to the interest_by_region() method to get the interest by country. Ibr = pt.interest_by_region("COUNTRY", inc_low_vol=True, inc_geo_code=True) Let's get the interest of a specific keyword by region: # the keyword to extract data Note that this method can cause Google to block your IP, as it grabs a lot of data if you specify an extended timeframe, so keep that in mind. If there's something quickly emerging, this method will definitely be helpful. Here is the output: data science isPartial You can also pass cat and geo as mentioned earlier. We set the starting and ending date and time and retrieve the results. It's suitable for short periods: # get hourly historical interest However, that's not useful if you're seeking long-term trends. Let's plot the relative search difference between Python and Java over time: # plot itĪlternatively, we can use the get_historical_interest() method which grabs hourly data. The default of this parameter is 'today 5-y' meaning the last five years. timeframe: It is the time range of the data we want to extract, 'all' means all the data that is available on Google since the beginning, you can pass specific datetimes, or the minus patterns such as 'today 6-m' will return the latest six months data, 'today 3-d' will return the latest three days, and so on.You can also get data for provinces by specifying additional abbreviations such as 'GB-ENG' or 'US-AL'. geo: The two-letter country abbreviation to get searches of a specific country, such as US, FR, ES, DZ, etc.You can check this page for a list of category IDs or simply call pytrends.categories() method to retrieve them. cat: You can specify the category ID if a search query can mean more than one meaning, setting the category will remove the confusion.The build_payload() method accepts several parameters besides the keyword list: The values range from 0 (few or no searches) to 100 (maximum possible searches). To get the relative number of searches of a list of keywords, we can use the interest_over_time() method after building the payload: # set the keyword & timeframe There are other parameters such as retries indicating the number of retrials if the request fails or using proxies by passing a list to proxies parameter. The hl parameter is the host language for accessing Google Trends, and tz is the timezone offset. To begin with pytrends, you have to create a TrendReq object: # initialize a new Google Trends Request Object We'll use Seaborn just for beautiful plots, nothing else: from pytrends.request import TrendReq To get started, let's install the required dependencies: $ pip install pytrends seaborn In this tutorial, you will learn how to extract Google Trends data using Pytrends, an unofficial library in Python, to extract almost everything available on the Google Trends website. Google Trends is a website created by Google that analyzes the popularity of search queries on Google Search across almost every region, language, and category. Confused by complex code? Let our AI-powered Code Explainer demystify it for you.
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