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We have been talking about big data and data science for a very long time, dealing with huge amounts of data really requires a lot of time and computational power. But from where do these huge data come from? In this article, we are going to see a new world of web scrapping along with its practical use cases. We will deeply discuss python web scraping, the benefits of python over other programming languages, and many other related topics.
What Is Web scrapping?
In simple words, we can say that web scraping is the method of extracting data from websites. It is one of the most famous methods to obtain structured datasets for big data and data analysis. This is also known in many other names such as data scraping, web harvesting .etc. Almost all the data we need will already be available in the HTML code of the website. There are numerous libraries available on the internet which will make the web scraping a piece of cake and autonomous.
What Is The Use Of Web Scraping?
Web scraping is used in many cases like price monitoring, price intelligence, news monitoring, lead generation, market research, social media monitoring, among many others. In general web scraping is used by people and organizations who would like to make use of the vast amount of publically available data to make smarter decisions, remember more data or information we have the better will be the decisions we make. The main benefit of web scraping is that it gives us structured data from any public website.
How To Do Web Scraping?
Usually, web scraping is a 5-6 step process,
- Make a list of URL’s of the page you want to scrape
- Inspect the page
- Find the data you want to extract
- Write the code
- Run the code and extract the data
- Store the data in required format
It may seem to be simple, but rather it’s way too complicated. There are two integral parts for web scraping they are, Web Crawling and Web Scraping. Both these steps can be performed using pre-existing tools available on the internet, some of which we will see in the latter part of the article.
Web Crawler is an artificial intelligence that browses through the internet and indexes each page, sometimes often referred to as spiders. While Web Scraper is a specialized tool that will extract data from the web pages, there are many parts for a web scraper and the most important of them is data locator.
When you run the code for web scraping, a request is sent to the URL that you have mentioned. As a response to the request, the server sends the data and allows you to read the HTML or XML page. The code then, parses the HTML or XML page, finds the data, and extracts it.
Which Websites Allows Web Scraping?
Some websites will let you scrape data from them but some will not and some will let you partially scrape data. To find whether the website you are targeting lets you scrape data there is a simple way, add the following extension to the URL and see whether any limitations are imposed.
For example, if you are targeting wazirx.com just use the following link “https://www.wazirx.com/robots.txt“(click here)
If you open the link above what you are going to see is
This means that Wazirx website allows us to scrape data using any bots and no limitations or exclusions are imposed on any bots, but this may or may not be the case for the website you are looking for. To know more about robots.txt files refer to this article.
Python Web Scraping Libraries
Now that we have a basic understanding of what is web scraping, now let’s focus on the main topic that is web scraping using python. As we have discussed earlier, to make things easy for us we use many tools, these tools come in the form of libraries in python. I expect there is no need for an introduction to python as we have discussed it in various other articles, you may seek references from here.
We primarily use 3 python libraries to carry out our operation they are namely, Scrapy, Beautiful Soup, Selenium. Along with these libraries we also use pandas and requests. All of them have their own different applications, let’s see each of them in action.
|Library Name||Uses||How To install (*)|
|Requests||The requests library is used to send requests and get responses from a website.||pip install requests|
|BeautifulSoup||Beautiful Soup is a Python package for parsing HTML and XML documents. It creates parse trees that are helpful to extract the data easily.||pip install beautifulsoup4|
|Scrapy||Scrapy is a free and open-source web-crawling framework written in Python. Originally designed for web scraping, it can also be used to extract data using APIs or as a general-purpose web crawler.||pip install Scrapy|
|Selenium||Selenium is a portable framework for testing web applications. Selenium provides a playback tool for authoring functional tests without the need to learn a test scripting language.||pip install selenium|
|Pandas||Pandas is a library used for data manipulation and analysis. It is used to extract the data and store it in the desired format.||pip install pandas|
Well, there are many other alternatives for these tools but these are the most common and popular ones among web scrapers. each of them has its own benefits and drawbacks.
Python Web Scraping Tutorial
Ok now let’s see python web scraping in practice by scraping data from wazirx.com (India’s largest crypto exchange)
import requests from bs4 import BeautifulSoup #importing all the required libraries
First, we start off by importing all the libraries that we are going to use. After that, we need to determine the URL of the website which we are planning to scrape, in our case it is “https://wazirx.com/”.
res=requests.get( "https://wazirx.com/" ) #sending a request to the website, this will return the html of the website res.text
this will show you some intimidating results, something like what you see below.
Don’t worry, it’s is the HTML code of the website. It contains all the data that we are looking for. But getting the required data from that huge pile of HTML tags and semicolons is a very hard task and is literally impossible to do manually. To see this in a better way we can use prettify function, but for the time being since we are covering only the basics of web scraping that I not actually necessary.
Now we have a variable res which is actually an object of requests and it is holding all the details we want. For extracting those data we need to convert requests objects into BeautifulSoup objects.
soup=BeautifulSoup( "res.text","html.parser" )
Now we have made a BeautifulSoup object which contains all the data from our request object, this step is very important without which we cannot extract data using BeautifulSoup. Now it’s very easy to scrape the required data from that website, for example, run the following code to get the title of the website.
title=soup.select( "title" ) print( title )
In this case, what we are going to see is something like this
That text enclosed between <title> and </title> is the data we are looking for.
This is the most simple example of web scraping, but in real life things are complex. We haven’t made use of the panda’s library since we are not storing any data into CSV files, also multiple for loops and while loops are made use of in real life for crawling through multiple URLs.
Selenium vs Scrapy vs BeautifulSoup
Selenium is an open-source web-based automation tool. Selenium is primarily used for testing in the industry but It can also be used for web scraping. But for the working of selenium, we need to have another extra tool that is chrome driver(for chrome and Microsoft edge driver for edge). you can download it from here(for edge users here).
Scrapy is an open-source collaborative framework for extracting the data from the websites that we need. Its performance is ridiculously fast and it is one of the most powerful libraries available out there.
BeautifulSoup is really a beautiful tool for web scrappers because of its core features. It can help the programmer to quickly extract the data from a certain web page. This library will help us to pull the data out of HTML and XML files.
Now let’s compare all these tools in tabular form.
It can handle AJAX & PJAX requests.
Scrappy can extract data from HTML sources using CSS and XPATH expressions.
It is Easily Extensible and is extremely fast.
It consumes less memory and GPU usage.
It Is easy to learn and master.
Easy to read the documentation.
Good community support.
Speed of data extraction using selenium is very slow compared to scrapy and BeautifulSoup.
Complicated to learn, complex than BeautifulSoup but simpler than scrapy.
Scrapy is a bit complicated to learn than BeautifulSoup and selenium.
It cannot send HTML requests by itself, for that purpose external libraries like requests are made use of.
Python Web scraping Projects
As we can see there are many manual activities that are needed to be done by the programmer itself, we as a programmer may need to read and understand the HTML codes of the websites in order to scrape data efficiently. This comes from experience, and experience comes from practice. Now let’s see the five best python web scraping project ideas for beginners.
If you don’t know, Reddit is a gold mine of data. There is literally an infinite number of subreddits on all topics. Scraping text data and image download links from those subreddits is a simple task and can be done by any beginner.
Consumer Research is a vital part of any business. It helps businesses by letting them know what their targetted customers want. You as a beginner could easily scrape data from websites reviewing websites like Trustpilot, BBB, Yelp, Gripeo. Python web scraping makes consumer research really easy and companies could list their products at a competitive price.
Competitive analysis is one of the many aspects of digital marketing. It also requires data scientists and analysts’ expertise because they have to gather data and find what their competition is doing. You can start this by taking the list of all the companies present in your industry sector. Do python web scraping on their websites to gather information about their new product listing, their prices, and upcoming launches.
Python Web Scraping For SEO
Search Engine Optimization (SEO) is the process of modifying a webpage for the purpose of having more reach among the audience. It is done by fulfilling some search engine laid criteria and adding competitive keywords and tags to rank better on search results. There are many ways to do python web scraping for SEO. You can take inspiration from Moz or Ahrefs and build an advanced web scraper yourself. There’s a lot of demand for useful web scraping tools in the SEO industry.
Scrape Data Of Your Favourite Sports Team
Use your knowledge of python web scraping to gather data about your favorite sports teams and get interesting insights from the data. There are many sports-oriented websites like ESPN Cricinfo, cricbuzz, one football, you can try python web scraping on any of these websites.
Python Web Scraping Interview Questions
As we can see python web scraping is a hot topic it opens a new window of jobs for us, the industry is in need of data, and people who have data are given privileges. Many companies hire web scrapers, while some companies hire web scrapers as data analysts because web scraping is considered a bad practice in the industry. But everyone does it behind the screen because without web scraping it would be very difficult to be in the market. Let’s see some of the frequently asked interview questions about python web scraping.
What are the libraries you have used for python web scrapping?
Ans. BeautifulSoup, Scrapy, and Selenium are the most common and popular python web scraping libraries, along with it sometimes lxml and other database management libraries like pandas are used. urllib is also used in some cases.
What is the purpose of the request module in Python?
Ans. The requests module is used to a sent HTTP requests to websites and get responses, this response will have the data we need. This library is used along with BeautifulSoup since BeautifulSoup by default cannot be sent HTTP requests on its own.
How to deal if your IP address is blocked by the website?
Ans. It Is quite common that some websites block our IP address due to a lot of frequent visits, this issue can be solved by using proxy addresses.
Explain different steps involved in python web scraping.
There are mainly three steps involved in python web scraping, they are
- Reading Data
- Parsing Data
- Storing Data
These three are the main steps involved further they can be subdivided into many more steps for simplicity.
Python Web Scraping Reference Books
We have discussed all the necessary details needed for a beginner to kickstart his data scraping career, but it is not enough there is a sea of knowledge available on the internet. Here I will share some of the best books out there from where you can learn python web scraping in depth.
This book introduces web scraping and crawling techniques which give access to unlimited data from any web source with any formatting. This book is ideal for programmers, webmasters, and other professionals familiar with Python.
In this book, Automate the Boring Stuff with Python, you will figure out how to utilize Python to make programs that will do all that dreary, snort work for you. Filling out online structures, looking for records, making, moving, refreshing, and renaming documents and envelopes, and looking through web content and in any event, downloading, these should be possible with no exertion, thus substantially more as well.
The book investigates what web scratching is, Why you should utilize Python for the scratching, how to structure projects, order line contents, Modules, and Libraries, and overseeing them.
This is an exceptionally famous book and Michael Schrenk, a profoundly respected web bot engineer, shows you how to make the information that you pull from sites simpler to decipher and break down. Additionally how to robotize buys, closeout offers, and other online exercises to save time.
In this incredible book, you can get fully operational quickly with the essentials of web scratching utilizing PHP. You will learn it in an Instant! A short, quick, centered aide conveying prompt outcomes. It trains you to construct a re-usable scratching class to develop for future tasks.
The creator investigates the most well-known grievances about web scratching, and why they likely won’t make any difference for you. How information is sent from a site to a PC end client’s PC and is parsed, and how you can utilize web scratching to capture this interaction and get the information you are searching for. In short agreement web advances, finding and removing information is what’s truly going on with this book and an absolute necessity read for anybody considering these objectives.
Today we have taken a deep dive into the world of python web scraping, web-bots, and crawlers. But it’s not complete yet, there is a lot more interesting stuff happening around, make sure to learn about it from other sources. If you like our article do check out our other beautiful articles here. All the best for your learning journey.
Is python good for web scraping?
Python is absolutely the best programing language for web scraping. Python is dynamically typed and is very easy to learn, moreover there is very strong community support and there are many free open source tools compatible with python available to do web scraping.
Is R or Python better for web scraping?
Web scrapping is more efficient with python since R is more focused on statistical analysis while python is a general-purpose language. And advanced tools like Scrapy are available in python.
Is web scraping legal?
Considering web scraping as a tool it can be used for good things and bad things as well. Web scraping as default cannot be considered as an illegal activity but the purpose of extraction and usage of data thus extracted can be illegal. But in the industry data scraping is considered a bad activity, some businesses even hire python web scrapers in the label of data analysts.
Is BeautifulSoup faster than selenium?
Yes. BeautifulSoup is obviously faster than selenium because BeautifulSoup uses a requests library to send HTML requests which are faster than selenium’s method. Selenium uses a chrome driver to open websites and scrape data from that website, which is much complex and slower than the BeatifulSoups method.
How to find text between fonts in web scraping using python?
It is not necessarily be font, be it any tags extracting data from website contained between two tags can be done in same way.
for t in soupe.find_all(“<font>”):
In the above example replace “soupe” with the BeautifulSoupe object and ” <font>” tag with the required tag.
How to connect customer search with python web scraping?
For the purpose of customer research, we can make use of review websites like Trustpilot, BBB. Also, we can make use of E-commerce website reviews from amazon.com, eBay, etc. also social media scraping and forums scraping could be made use here to gather more data.