Google Trends is a public platform to examine the popularity of top Google Search queries across multiple locations and languages, as well as interest in web searches over time for a certain topic, search phrase, or even organization. If you want to create your own custom reports or analyze search keywords, you should utilize Pytrends, an unofficial Google Trends API that provides many API methods for downloading reports of trending searches from Google Trends. In this article, we will discuss what you can do with the Google Trends API and how to use the Google Trends API Python.
Table of Contents
What Is The Google Trends API Used For?
Google Trends is available as both a Google Trends website and API. There isn’t any issue with simply utilizing the web interface; but, when working on a big-scale project that involves the creation of a huge dataset, this may become quite time-consuming.
Manually investigating and extracting data from Google Trends is a time-consuming and labor-intensive operation. This time and energy are significantly reduced when using an API.
Google Trends adjusts search data to facilitate term comparisons easier. The numbers are computed on a scale of 0 to 100, with 100 being the place with the most popularity as a percentage of all searches in that location and 50 indicating a location that would be about half as popular. A value of 0 means that there was insufficient data for this phrase in this location. This relative popularity is calculated by dividing each data point by the total number of searches for the location and time period it represents.
Additionally, with regard to abstract search volume, various locations with the same level of search interest in a phrase do not always have the same overall search volume. Overall, it is reasonable to compare search phrases only within the same time period and geography.
What Are The Benefits Of Using Google Trends?
Google searches offer a plethora of data on the most recent search trends for every country. Advertisers may utilize this information to develop content and frame plans as trendy topics. Pretty much every single organization is following the current trend in order to gain more consumers and customers. The Google Trends tool will assist you in gaining an understanding of the most popular news and information from across the world.
Google Trend helps in tracking the areas and regions that are most engaged and interested in the phrases you’ve looked for. This allows the user to tailor their material to the individuals in that precise place, increasing consumer engagement.
Analyzing and tracking the behaviors of your competitors may be a helpful step toward the advancement of your brand or company. One of Google Trends’ tools displays your market competition. While this isn’t the most comprehensive analyzer, it does give you some of the top competitors for your searched phrase.
All content writers want to enhance visitors to their site. You can uncover the leading and trending keywords for your content using Google Trends.
What Are Pytrends?
Pytrends is an unofficial Google Trends API that offers many techniques for downloading trending information from Google Trends. The Python library could be used to automate a variety of operations, such as swiftly retrieving Google Trends data for further analysis.
Pytrends can retrieve information related to a certain term you supply to the API. This information includes interest by region, popular searches, suggestions, hourly interest, interest over time, and related topics.
How To Use Google Trends API With Pytrends?
To get useful information from search word searches, we will be using Pytrends, the Python API for Google Trends.
Install Pytrends And Some Dependencies
Let’s install pytrends and required dependencies with the pip package manager.
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#install pytrends pip install pytrends #install pandas pip install pandas #install plotly pip install plotly #install country-converter pip install country-converter |
The first command in the above list installs Pytrends. The second command installs Pandas which is an open-source data analysis and editing tool that is quick, powerful, versatile, and simple to use. plotly is a package for data analytics and visualization. Country-converter which we install in the fourth command is a Python module for converting and matching country names between classes.
Configure A Connection To Google
Now we can import the necessary packages and initialize the TrendReq class, which defines the settings for connecting to the Google Trends API.
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from pytrends.request import TrendReq pytrends = TrendReq(hl='en-US', tz=360) |
Two critical parameters are sent to the TrendReq. hl denotes the language for accessing Google Trends hosting, in this case, it is US English. Tz is the timezone, in this case, it is the US time zone.
Build Payload
For sending calls to the Google Trends API, all Pytrends methods use the following parameters:
- kw list – a set of keywords to search for information on.
- cat – Uses Google category to restrict down results.
Here is an example.
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kw_list = ["zensarp", "api", "google"] # list of keywords to get data pytrends.build_payload(kw_list, cat=0, timeframe='today 12-m') |
However, if you need you can use the following additional parameters as well.
- geo – Searches keywords based on geolocation (default is World)
- timeframe – sets the timeframe for retrieving search results.
- gprop – enables you to filter results based on a Google property.
Interest By Region
You could be curious about the performance of a keyword by region from time to time. The pytrends method interest_by_region will display you which regions search the term you picked on a scale ranging from zero to 100, with 100 indicating the region with the highest searches and 0 indicating a region with insufficient data.
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by_region = pytrends.interest_by_region(resolution='COUNTRY', inc_low_vol=True, inc_geo_code=False) by_region.head(10) |
Using pandas, the results are delivered inside a data frame.
You may also utilize pandas to find regions with a score higher than 10.
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by_region[by_region["machine learning"] > 10] |
Interest Over Time
This method will provide historical data from Google Trend for the searched phrase based on the timeframe you selected in the build payload method.
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interest_over_time_df = pytrend.interest_over_time().drop(columns='isPartial') interest_over_time_df.head() |
Keyword Suggestions
Google Trends will provide you with a set of Google keyword suggestions based on your core keyword. The obtained data may then be visualized by using Plotly library to gain further understanding of it.
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df = pd.DataFrame(pytrend.suggestions('Google Trends API')) df.head() |
You are making a request to discover ideas for the term “Google Trends API.” The pytrends recommendations function will retrieve keyword ideas from Google Trends and deliver them in a data frame.
Related Queries
Pytrends may also assist you in locating keywords that are tightly associated with a core term of your selection and then provide a set of related keywords as indicated on Google Trends.
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rq = pytrend.related_queries() rq.values() df = pd.DataFrame(rq.get('aws s3').get('rising')) df.head() |
Frequently Asked Questions (FAQs)
Is there an API for Google Trends?
Yes.
What is Pytrends in Python?
Pytrends is an unofficial Google Trends API that offers many techniques for downloading trending information from Google Trends.
How do I get Google Trends in Python?
There are many methods from the Google Trends API by Pytrends including Interest over time, Related queries, Keyword suggestions, Keyword suggestions related words, and more.
How do I extract data from Google Trends?
In Google Trends, there is a download button on the top-right corner of the page. This will open your file in a spreadsheet app, like Google Sheets.
How does Google implement Google Trends?
Google Trends normalizes search data so that term comparisons are easier. It normalizes search results to the location and time of a query. For instance, to assess relative popularity, Google divides each data item by the entire number of searches in the location and time range it represents.
Why Should You Use Zenserp To Scrape Search Engine Results Pages?
Google Trends is a fantastic service that allows you to investigate web search traffic on users’ Google search-based queries. Obtaining Search Engine Results Pages (SERP) as we did above has always been difficult. Zenserp allows you to scrape search engine result pages in a scalable and rapid manner. Zenserp is not supported or associated in any way with any search engine provider, and it does not use any API supplied through any search engine.
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