#web scraping python

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Python is a versatile language that can be used for many different things. One neat little trick it can do is scrape Google search results.

This can be useful for a variety of reasons, such as conducting market research or keeping track of a competitor’s online presence.

Luckily, a few different Python libraries make this process relatively simple. In this blog post, we’ll take a look at a few of them and see how to get started.

Why Python for google scraping?

Being a very simple language it is also flexible and easy to understand even if you are a beginner. The Python community is too big and it helps when you face any error while coding.

Many forums like StackOverflow, GitHub, etc already have the answers to the errors that you might face while coding when you scrape google search results.

On top of that, there are many libraries which make our job easier. You can do many things with python but for now, we will learn web scraping with it.

Scrape Google Search Results with Python

In this post, we will learn to scrape google search results for any specific country using Python and a free residential proxy. But first, we will focus on creating a basic python script that can scrape the first 10 results.

The end result will be JSON data that will consist of link, title, description, and position. You can use this data for SEO, product verifications, etc.

Prerequisite to scrape

Generally, google scraping with python is divided into two parts:

Fetching data by making an HTTP request.

Extracting essential data by parsing the HTML DOM.

Libraries & Tools

Beautiful Soup is a Python library for pulling data out of HTML and XML files.

Requests allow you to send HTTP requests very easily.

Residential Proxy to extract the HTML code of the target URL.

Setup

Our setup is pretty simple. Just create a folder and install Beautiful Soup & requests. For creating a folder and installing libraries type below given commands. I am assuming that you have already installed Python 3.x.

mkdir scraper <br>pip install beautifulsoup4 <br>pip install requests

Now, create a file inside that folder by any name you like. I am using google.py.

The import the libraries we just installed in that file.

from bs4 import BeautifulSoup<br>import requests

Preparing the Food

Now, since we have all the ingredients to prepare the scraper, we should make a GET request to the target URL to get the raw HTML data. Now we will scrape Google Search results using requests library as shown below.

We will first try to scrape 10 search results and then we will focus on country-specific results.

headers={‘User-Agent’:’Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36'}

url=’https://www.google.com/search?q=pizza&amp;ie=utf-8&amp;oe=utf-8&amp;num=10'

html = requests.get(url,headers=headers)

this will provide you with an HTML code of that target URL. Now, you have to use BeautifulSoup to parse HTML.

soup = BeautifulSoup(html.text, ‘html.parser’)

When you inspect the google page you will find that all the results come under a class “g”. Of course, this name will change after some time because google doesn’t like scrapers. You have to keep this in check.

We will extract all the classes with the name “g”.

allData = soup.find_all(“div”,{“class”:”g”})

Now, we will run a for loop to reach each and every item in the allData list.

g=0

Data = [ ]

l={}

for i in range(0,len(allData)):

link = allData[i].find(‘a’).get(‘href’)

if(link is not None):

if(link.find(‘https’) != -1 and link.find(‘http’) == 0 and link.find(‘aclk’) == -1):

g=g+1

l[“link”]=link

try:

l[“title”]=allData[i].find(‘h3’).text

except:

l[“title”]=None

try:

l[“description”]=allData[i].find(“span”,{“class”:”aCOpRe”}).text

except:

l[“description”]=None

l[“position”]=g

Data.append(l)

l={}

else:

continue

else:

continue

print(Data)

Inside for loop, we have to find the website link, title, and description. We can find the link inside the a tag, title in h3 tag, and description in a span tag with class aCOpRe.

We have to filter out the legit google links from the raw data. Therefore we have used find() method to filter out the garbage and ad links. You can filter out ad links just by checking whether they contain ‘aclk’ within the URL string. Then we will add all the data inside a dictionary l and then append it to list Data.

On printing the list Data the output will look like this.

This method is not reliable because google will block you after certain requests. We need some advanced tools to overcome this problem.

Scraping google search results from different countries

Now, since we have learned to scrape google search results using python we should move on to learn even more advanced techniques. Google shows different results in different countries for the same keyword.

So, we will now scrape the google results according to country origin. We will use a residential proxy to achieve our results.

There are plenty of tools out there that you can use to scrape google results from websites, but one of the most popular and reliable tool is Scrapingdog.

It’s a simple tool that can be used to extract data from almost any website. All you need to do is enter the URL of the website you want to scrape, and it will do the rest.

First, we will create a list of user agents so that we can rotate them on every request. For this tutorial we will create a list of 10 user agents. If you want more, then you can find them here.

userAgents=[‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36’,’Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36',’Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.157 Safari/537.36',’Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.113 Safari/537.36',’Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36',’Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.90 Safari/537.36',’Mozilla/5.0 (Windows NT 10.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.121 Safari/537.36',’Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36',’Mozilla/5.0 (Windows NT 5.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.71 Safari/537.36',’Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/21.0.1180.83 Safari/537.1']

Now, we need a residential proxy provider via which we can rotate proxies and change the origin of the request. When you signup to Scrapingdog you get 1000 free requests. You can find the proxy documentation here.

You will find your proxy URL on the dashboard. We will create a proxy object to pass it on to the requests method.

http_proxy = “http://scrapingdog:[email protected]:8081"

https_proxy = “http://scrapingdog:[email protected]:8081"

proxyDict = {“http” : http_proxy,”https” : https_proxy}

We have used -country=us as a param in our proxy to use USA proxies. Similarly, you can use ‘ca’ for Canada, ‘gb’ for England, ‘in’ for India, etc.

We will use the random library to rotate user agents.

from random import randrange

headers={‘User-Agent’:userAgents[randrange(10)]}

html = requests.get(url,proxies=proxyDict,headers=headers)

And that’s it. All the rest of the code will remain the same as earlier.

As earlier, we will create a Beautifulsoup object and then extract the same classes. But this time google won’t be able to block as you are using a new IP on every request.

For the USA, the results will look like this.

For the United Kingdom, the google search result will look like this.

Similarly, you can check for other countries.

But if you want to avoid handling all this hassle, then you can use our Google Search API to scrape google search results in just one single GET request.

Limitations of scraping google search results with python

Although python is a great language but when it comes to google scraping there are some limitations with it. Since it is a dynamic language it can lead to runtime errors and it cannot handle multiple threads as well as other languages.

Further a slow response rate is observed while using python for scraping google search results.

Other than that you cannot continue using just python for scraping google at a large scale because then it will ultimately block your script for such a large amount of traffic from just one single IP.

You can use Scrapingdog API where you don’t have to maintain a web scraping script. Scrapingdog will handle all the hassle and deliver the data in a seamless manner. You can take a trial where the first 1000 requests are on us.

Conclusion

In this article, we learned how we can scrape data from Google using Python & Residential Proxy regardless of the type of website. Feel free to comment and ask me anything. You can follow me on Twitter.

Yelp is an online reviewing platform where people posts review about different businesses.

Scraping yelp review data can help you to save time.

Benefits of scrapping yelp

1. Help You To Analyze your business reviews

You can understand how satisfied customers are with your brand when you scape it for your yelp listing. Further, it can help you to know the customer satisfaction of your user base.

2. Help You To Analyze Your Competitors' Business Reviews

Extracting competitor reviews via web scraper can help you to do competitor research. It can help you to understand the strengths and weaknesses of your competitor.

You can find out the main complaints of their users and the things they appreciate the most. Also, the data will evaluate competitors' product quality, reliability, service, and many other business matrices.

Think of this as the opportunity to deliver your solution to competitors' unsatisfied customers. :))

Focus on things people like about your competitor & make sure you improve on these aspects too.

You can do the above-mentioned practice for your clients too to leverage maximum. If you want to scrape yelp reviews you can use scrapingdog's API.

Real estate is a prominent company that relies heavily on making long-term decisions to succeed. Many people also feel that real estate investments are worthwhile since they provide profitable returns while lowering risk.

Intro to Yelp Web Scraping Using Python

Originally published June 17, 2020 Like many programmers who hold degrees that are not even relevant to computer programming, I was struggling to learn coding by myself since 2019 in the hope to succeed in the job. As a self-taught developer, I’m more practical and goal-oriented about things that I’ve learned. This is why I like web scraping particularly, not only it has a wide variety of use…