Python SERP Scraping Tutorial: Step-By-Step in 2023

What is a SERP Scraper?

SERP Scraper API is a tool that gathers real-time parsed and ready-to-use search engine data from both organic and paid results.

Organic, popular products, paid videos, product listing ads, images, featured snippets, related searches, and many other public data sources can be extracted.

To monitor brand mentions or product counterfeiting, you can extract data for any search query from the search page, keyword pages, and other page types.

Building a web scraper: Python prepwork

Throughout this entire web scraping tutorial, Python 3.4+ version will be used. Specifically, we used 3.8.3 but any 3.4+ version should work just fine.

For Windows installations, when installing Python make sure to check "PATH installation". PATH installation adds executables to the default Windows Command Prompt executable search.

Windows will then recognize commands like "pip" or "python" without requiring users to point it to the directory of the executable (e.g. C:/tools/python/…/python.exe).

If you have already installed Python but did not mark the checkbox, just rerun the installation and select modify. On the second screen select "Add to environment variables".

Getting to the libraries

One of the Python advantages is a large selection of libraries for web scraping. These web scraping libraries are part of thousands of Python projects in existence – on PyPI alone, there are over 300,000 projects today.

Notably, there are several types of Python web scraping libraries from which you can choose:

• => Requests

• => Beautiful Soup

• => lxml

• => Selenium

Requests library

Web scraping starts with sending HTTP requests, such as POST or GET, to a website’s server, which returns a response containing the needed data.

However, standard Python HTTP libraries are difficult to use and, for effectiveness, require bulky lines of code, further compounding an already problematic issue.

Unlike other HTTP libraries, the Requests library simplifies the process of making such requests by reducing the lines of code, in effect making the code easier to understand and debug without impacting its effectiveness.

The library can be installed from within the terminal using the pip command:

pip install requests

Requests library provides easy methods for sending HTTP GET and POST requests. For example, the function to send an HTTP Get request is aptly named get():

import requests response = requests.get("https://oxylabs.io/") print(response.text)

If there is a need for a form to be posted, it can be done easily using the post() method. The form data can sent as a dictionary as follows:

form_data = {'key1': 'value1', 'key2': 'value2'} response = requests.post("https://oxylabs.io/ ", data=form_data) print(response.text)

The requests library also makes it very easy to use proxies that require authentication.

proxies={'http': 'http://user:password@proxy.oxylabs.io'} response = requests.get('http://httpbin.org/ip', proxies=proxies) print(response.text)

But this library has a limitation in that it does not parse the extracted HTML data, i.e., it cannot convert the data into a more readable format for analysis. Also, it cannot be used to scrape websites that are written using purely JavaScript.

Beautiful Soup

Beautiful Soup is a Python library that works with a parser to extract data from HTML and can turn even invalid markup into a parse tree.

However, this library is only designed for parsing and cannot request data from web servers in the form of HTML documents/files. For this reason, it is mostly used alongside the Python Requests Library.

Note that Beautiful Soup makes it easy to query and navigate the HTML, but still requires a parser. The following example demonstrates the use of the html.parser module, which is part of the Python Standard Library.

... to be continued...

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