The A-Z Guide to Web Scraping

by | Dec 6, 2019 | Python Programming

10 Min Read. |

Data is a currency in today’s world and the internet is brimming with it. No wonder Data Scientists in India pull a salary of an average 10 LPA INR. However, to collect such a vast amount and contextualize it is difficult. This is where web scraping software comes to play.

What is Web Scraping?

Web scraping, web harvesting, or web data extraction is a technique of extracting data from websites. The web scraping software may access the World Wide Web directly using the Hypertext Transfer Protocol or through a web browser. The idea is to extract large amounts of data from websites automatically and save it on a local location.

When copying a tiny bit of information on to a table, manual work is easily done. However, when it comes to a vast amount of data, automation is the only way. Tools for web scraping are excellent ways of getting it done.

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An example can be that of a price comparator. A website that shows the prices of an item as displayed by different sellers on different sites is essentially just web scraper. Thus, in this case, web scraping helps our shopping be just a little cheaper.

What is Web Scraping, you ask? An example would be a Price Comparator.

What is Web Scraping, you ask? An example would be a Price Comparator. (Image Source: Freepik)

History of Web Scraping

Although the idea of handling huge chunks of data may remind one of Big Data or Machine Learning, web scraping is actually as old as the internet. At the beginning of the internet, it was just a collection of File Transfer Protocol (FTP) sites in which users would navigate to find specific shared files.

In order to find all these sites and index the files, people created programs called “web crawlers”, which would fetch all pages on the Internet and then copy all content into databases.

Gradually, the internet grew into an unending web and became searchable. Data could easily be found in a search. No longer did people have to type in physical addresses, they could instead type in a URL name that would fetch the intended address. But the information we need is often spread across different websites.

A tool for web scraping is just a collection of bots and web crawlers scaled up to function on the vast scale of the internet of today. They function the same way search engines do; except the don’t just index, they fetch and copy.

Uses of Web Scraping

Tools for web scraping are used for all sorts of odd jobs. Web scrapers, combined with other programs, can be used to automate plenty of mundane, manual jobs.

From buying plane tickets to ordering food, they can perform almost every task a human does on a browser. They can scan shopping sites periodically to find a good at a reasonable price if that’s what they are programmed to do.

Web scrapers find infinite uses. Here are a few enterprises uses of web scraping –

1. Search Engines

Do you know what Google is? It’s a vastly advanced and highly optimized web scraper. The most popular search engine in the world is perhaps the grandest example of tools for web scraping. There are several other search engines that use this idea.

What is web scraping? An ideal example is Google - the search engine.

What is web scraping? An ideal example is Google – the search engine. (Image Source – Freepik)

2. Price Monitoring

Sites like myDala or TripAdvisor are web scrapers. They use web scraping to offer you the best price on deals and holidays. Similar sites are available all over for price comparison.

3. Sales and Marketing

Ever wondered how different sales representatives get your contacts? Marketing and sales all over the world use web scraping to collate contact information of probable customers in order to make sales. A combination of web scrapers can enrich the data with emails, phone numbers and social media profiles for sales or marketing campaigns.

4. Content Aggregators

All content aggregators are essentially web scrapers. They collate content from different sites; in case of job portals it’s different job sites, and in case of shopping it’s different shopping sites. Services such as Naukri, Smile, etc. all use web scraping to gather their data.

5. SEO Monitoring

Ever wonder how Google alerts work? Again, it is web scraping at work. Similarly, SEO tools such as Moz, SEMRush, etc. work use web scrapers to find out keywords, rankings, and other information essential to SEO operations.
Training/Datasets for Machines – Scientists depend on data collected with tools for web scraping to train their machine learning models. Not all data on the web is readily available as a structured dataset, nor do all websites have an API.

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6. Data for Research

Researchers and journalists all over spend much time collecting data and cleaning and arranging it manually. Not all data on the web is readily available as a structured dataset, nor do all websites have an API. Having web scrapers ensures such tasks can be automated.

7. Workings of a Web Scraper

A web scraper is a program. It downloads text from websites, downloads those HTML contents and extracts data from it. Web scrapers are complicated, built using multiple modules, each designed for a specific task. Usually, these are written in different scripting languages. Web scraping with python is a popular way. Next, we are going to look at the different components that make up a web scraper.

Breaking Down a Web Scraper

Web scraping is like any other ETL process. ETL is short for extract, transform, load, three database functions that are combined into one tool to pull data out of one database and place it into another database.

That is what web scrapers do. They are essentially crawlers that crawl through websites, extract data and transform it into a usable structured format and load it into a file or database for subsequent use.

There are 4 essential components to a web scraper. They are –

What is web scraping? Think a crawler, just with more resources.

What is web scraping? Think a crawler, just with more resources. (Image Source – Freepik)

What is web scraping? Think of a crawler, just with more resources.

1. A Web Crawler Module

A web crawler module makes requests to web pages based on some pattern or pagination logic. Then it downloads the HTML response and passes it through the extractor.

2. A-Parser and Extractor

A parser then goes through the downloads. It then extracts data from the downloaded HTML content and structures them into a basic form. There are different parsing techniques –

(i) Regular Expressions

Regular expressions match patterns. It can do simple tasks such as parse extracted text for email patterns. However, for more complex searches, we need more sophisticated techniques.

(ii) HTML Parsing

This is the most common technique for parsing. HTML parsing builds a tree using the content pattern and uses that tree to tabulate information. Such structures can easily be created using CSS or HTML tags as markers.

(iii) DOM Parsing

Dynamic web pages, that continuously send and accept requests, are continuously updating data. Downloading such pages only lets one download the shell or structure of the page and not the data. Instead, such pages must be downloaded by individually querying for each request. This where DOM parsing comes into play.

(iv) Automated Extraction

This is an advanced and more complicated technique. It requires training web scrapers using machine learning models to extract data from websites. Named Entity Recognition models are popularly used for this.

3. A Data Cleaner and Transformer

Data extracted is not in a condition to be directly used. A cleaner and transformer then clean the data and transforms it into a usable format.

For example, when we extract data from the Facebook page of Mark Zuckerberg, we will extract “IntroBringing the world closer together. Founder and CEO at Facebook Work at Chan Zuckerberg Initiative Studied Computer Science and Psychology at Harvard University Live in Palo Alto, California from Dobbs Ferry, New YorkMarried to Priscilla ChanFollowed by 117,116,152 people”. It has to be arranged in a proper way such as –


Mark Zuckerberg

Works at





Computer Science and Psychology at Harvard University


Palo Alto, California

This is how a cleaner and transformer work. It changes raw data into a usable and sensible pile.

4. A Data Sterilizer and Storer

Once data has been cleaned up, it needs to be serialized and stored in the database. This may be a simple SQL server, or an Oracle DB or just a JSON. Once tabled up, data can be used by anyone who needs it by simply querying the database.

Building a Web Scraper

There are many ways to build a web scraper. Anyone who is well versed in coding practices can probably write a simple web scraper from scratch. With expertise, they may even write complex ones.

However, once things get large and complicated, it is wise to use frameworks. There are quite a few open-source platforms available for this purpose –

1. Scrapy

Scrapy is one way to build a tool for web scraping with python. It is built on top of an asynchronous framework which means it is well equipped to build a large, efficient and relatively fast system.

2. Mechanical Soup

Mechanical Soup

Mechanical Soup

Another tool for web scraping with Python. It is built using the parsing library Beautiful Soup. It simulates human browsing and scrapes data from websites. It’s simple and efficient in its approach.

3. PySpider



Another tool for web scraping with Python, this supports JavaScript pages and has a distributed architecture. It has an easy to use UI and is ideal for AJAX heavy websites. The best part is that it supports a backend database of MongoDB, Redis, etc. and allows developers options.

There are quite a few languages one may use to build a web scraper just as JavaScript, NodeJS, etc. However, with an array of libraries and framework support, building a tool for web scraping with Python is definitely the simplest way to go.

Common Misconception – Web Scraper VS Web Crawler

Web crawling is what a search engine does. It views a page, indexes it and if necessary, show the data on the page as a whole. It goes through links on that page to further the indexing process.

Web Scraping, on the other hand, is the programmatic analysis of each website crawled on to, extracting data and finding patterns. The data on the website can be further processed and stored elsewhere.

Thus web crawling is just a step in the larger process of web scraping.

The Importance of Web Scraping

Data is the newest currency on the block. It is highly valued and needed for almost every task that happens on the world wide web and in real life. It is the core of market research and strategy building.

Take the last US elections for example. There have been rampant allegations of using voter data to manipulate constituents and using targeted messaging over social media to influence voters. Whether or not is true, the allegations are not impossible; in today’s world, with the right data, this is achievable.

Whether it is to start a business, make a move on an existing business or just understand what the world needs next, data is of paramount importance. Every massive retailer uses data scraped from all over the internet to advance their business. Web scraping is even used to understand public sentiments before the government decides on policies.

Evolution of Data Analytics

Evolution of Data Analytics (Image Source – YouTube)

Engineers and technicians versed in the science of Web Scraping are the newest ensembles of must-have resources for many companies. Not to mention the thousands of companies whose business is to analyze that data and sell the results to clients.

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The internet is a leveling tool when it comes to running businesses in today’s world. It allows everyone the same level of access, which is why everyone is availing of the benefits. Data is the basis of every decision making. It is a business of its own. It’s high time professionals leveraged it.

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