Using Python to Create a Google SERP Checker

Python is the preferred language when it comes to analysis and automation. The high-level language is simple to use and has a massive development community behind it.

Among many of the tasks you can do with Python, one of them is creating a Google SERP checker. With just a few lines of code, you can track and collect Google SERP data.

In this tutorial, we’ll guide you through the process step by step on how you can use Python to create a Google SERP checker.

Fraud Detection API (Application Programming Interface) suite features a variety of different risk analysis APIs designed to Proactively Prevent Fraud™ with industry leading accuracy to identify fraudulent users, suspicious payments, and abusive behavior.

Create a Google SERP Checker with Python

Before you start, you must ensure you have all the things you’d need. Working with software can get sophisticated, partly because you need to configure the various components.

The things you’d need to create a Google SERP checker are:

  • Python 3 or higher
  • BeautifulSoup 4 or higher
  • Requests module

If you do not have these on your computer, then you’re required to install them first.

For this, create a file and name it “Googlescrape.txt.” You can name it anything you want as long as you can memorize it.

Inside the text file, you need to save “requests” and “bs4”. When following these steps, we already assume that you have Python 3 or a higher version installed on your computer.

Once you’ve saved the file, run this command: pip install -r Googlescrape.txt. Then import the libraries into your own script.

Open the terminal and paste the following:

import requests from bs4 import BeautifulSoup

# City Name Information

cityName = input(“Enter the City Name: “)

searchString = “Weather in {}”.format(city)


url = f” / search?&q ={searchString}”

# Send HTTP request

req = requests.get(url)

# Pulling HTTP data

sor = BeautifulSoup(req.text, “html.parser”)

#  Temperature in Degree Celsius

temp = sor.find(“div”, class_=’BNeawe’).text


The output will be the following:

Enter the City Name: London


Now, you have successfully scraped information from Google. You searched for the temperature in London and got the result as 20 degrees Celsius.

Similarly, you can scrape other types of information from Google. You just have to modify the parameters.

The majority of the complex work is handled automatically by BeautifulSoup, which is a Python-based HTML parser. It parses the HTML code that the Google SERP is written in and collects the data.

You can learn more about BeautifulSoup by clicking on this link.

Automate Google SERP Checking

You can manually collect the data you need from Google. But there’s a problem.

Google doesn’t allow scraping from its data. In 2011, it depreciated the Google Web Search API. This API was used previously to collect the data.

Now it has strict rules for any data scraping activities. You need to bypass its proxies and Captchas.  If it detects irregular traffic from your IP address, it may even block your IP temporarily or permanently.

Furthermore, collecting data manually is a tedious process. You have to perform multiple searches, collect the data in a spreadsheet, and then sit down to analyze the results.

To make matters a bit more complex, Google results aren’t constant. They keep changing from time to time, within minutes.

It also shows different results to different users. So, you may be seeing a web page ranked number one, but your friend will see a different web page altogether.

That’s because Google is personalizing search results for each user.

If you’re searching for salons near you and you’re located in Chicago, Google will serve the salons that are located near you. The same goes for someone who is searching for salons from Miami.

Therefore, after you’ve done all the hard work, you’ll find that your data collection efforts were a waste.

That’s why you should automate Google Search tracking using specialized tools.

How to Automate Google SERP Tracking?

There are a few tools you can use that allow you to get real-time search results data. One of them is WhatsMySerp’s Scraper SERP API tool.

With this tool, you do not have to interact with Google. You interact with the tool’s database.

Services like Whatsmyserp will scrap the data for you. They regularly scrape the information from the Google search results page using proprietary scraping technologies.

So you do not have to worry about proxies or solving captchas either.

To get the SERP data automatically, you need to send in an API request to the Whatspmyserp endpoint. You can get the query string and API credentials from the documentation page.

In the query string, you must specify the keyword terms, the location, types of search results data you need, number of results, among other things. Some of the parameters are compulsory, while others are optional.

Once processed, the SERP API will return the SERP data to you in JSON format. You can use this data to analyze the search engine data and track your website ranking automatically.

To Sum up

It’s possible to build a Google SERP Checker from scratch using Python. It’s easy to set up and easy to use.

But there’s another option of automating the entire process. So you need to weigh both the options available and make the decision for yourself.

For Your Info

Residential proxies are invaluable tools in Python-based data scraping endeavors. By integrating residential proxies into Python scripts, users can mimic genuine user behavior and access websites without raising suspicion. This allows for large-scale data extraction from various sources, enabling comprehensive market research and competitive analysis. Residential proxies provide a cloak of anonymity, ensuring uninterrupted scraping sessions by rotating IPs. Their authentic residential IPs offer superior connectivity and authority for access, granting users unfettered access to target websites. This synergy between residential proxies and Python opens up a realm of possibilities for businesses and researchers seeking reliable, high-quality data for their analytical endeavors.