Google Search Analysis with Python

This article will guide you through the subsequent steps on analyzing the google search using the python programming language.

Exactly, Google does not reveal it's scale but general estimations include 228 million searches per hour or 5. It is indicated that 8 billion searches are made on a daily basis. Wow, that is so big it is difficult even to imagine what it might mean. With the help of python let us go for Google search analysis based on different types of search queries.

It is also possible to scrape Google search results by sending a request for results to Google search API and then analyzing the result set. There are search results titles and snippets, which together with links create a basis for analyzing search data.

Here are the steps you can follow to perform Google search analysis with Python:

• Set Up API Access: An API key and a custom search engine ID (CX) are required in order to utilize the Google Custom Search JSON API.
• Make API Requests: To obtain search results from the Google Custom Search JSON API, use the API key and custom search engine ID.
• Parse Search Results: To extract links, excerpts, and titles from search results, parse the JSON response.
• Analyze the Data: Examine and analyze the search results, identifying popular terms, identifying patterns, and classifying the findings.
• Visualize the Data: To better understand trends and patterns in the search results, create visualizations (charts, graphs) based on the analysis.

What is Pytrends?

Python users can utilize Pytrends, an unauthorized Google Trends API. Based on various areas and languages, it is helpful to evaluate and compile a list of the most popular results on Google related to a certain topic or issue.

How is Pytrends installed?

Installing this API on your systems is a prerequisite for using it. The command pip install pytrends makes installation simple.

Code snippet:

Connect to Google

Now that the necessary Python modules have been imported, let's begin the work of evaluating the trends in Google searches. The first step in creating a dataframe is to import pandas. In order to obtain the Google trending topics, we must first establish a connection to Google. To do this, import the TrendReq function from the pytrends.request package. Matplotlib will also be imported in order to view the data.

Code snippet:

Build Payload

We will now construct a dataframe of the top ten nations where people look up "CLOUD COMPUTING." The build_payload function, which enables you to save a list of terms you wish to search for, will be used for this. Additionally, you may define the category and timeframe to query the data from in this.

Code snippet:

Interest Over Time

Based on the timeframe selected in the create payload method, the interest_over_time() function gives historical, indexed data for the most popular times the supplied term was searched.

Code snippet:

Interest in Historical Hours

The historical, indexed, hourly data for the peak search periods for the given keyword is returned by the get_historical_interest() function. You may also specify different time period criteria, such month_start,, year_start, day_start, year_end, month_end, hour_start, day_end, and hour_end, for which you would need historical data.

Code snippet:

Interest by Area

The interest_by_region method comes next; this will show you how well the keyword is performing in each region. Results will be displayed on a scale of 0 to 100, with 100 denoting the nation with the most searches and 0 denoting those with the fewest searches or insufficient data.

Code snippet:

Observation:

Depending on the period specified in the build_payload function, you will receive an output after running the code above that looks somewhat like the output below.

Next, a bar chart may be used to display the data mentioned above.

Code snippet:

Output:

Top Charts

We can obtain the most popular searches of the year by using this strategy. Let's now investigate the most popular searches in 2020.

Code snippet:

Observation:

According to the result above, "Coronavirus" is the most searched-for subject in 2020, followed by the other topics.

Related Questions

There's a good chance that a person searching on Google will look up other inquiries pertaining to the same issue if they look up information about it. We refer to these as linked inquiries. Let's look up a list of pertinent questions about "cloud computing."

Code snippet:

Here are a few of the most popular Google searches pertaining to cloud computing.

Ideas for Keywords

You may investigate what the public is looking for with the aid of the recommendations() function. It provides a list of more recommended terms that may be applied to refine a Google trending search.

Code snippet:

Output:

Don't give up the opportunity to benefit from the data revolution! By leveraging data, every sector is reaching new heights. Develop your abilities and join the most popular movement of the twenty-first century.