Javascript Screen Scraping

Web Scraping Using Node JS in JavaScript – Analytics Vidhya

This article was published as a part of the Data Science Blogathon.
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Gathering information across the web is web scraping, also known as Web Data Extraction & Web Harvesting. Nowadays data is like oxygen for startups & freelancers who want to start a business or a project in any domain. Suppose you want to find the price of a product on an eCommerce website. It’s easy to find but now let’s say you have to do this exercise for thousands of products across multiple eCommerce websites. Doing it manually; not a good option at all.
Get to know the Tool
JavaScript is a popular programming language and it runs in any web browser.
Node JS is an interpreter and provides an environment for JavaScript with some specific useful libraries.
In short, Node JS adds several functionality & features to JavaScript in terms of libraries & make it more powerful.
Let’s get to understand web scraping using Node JS with an example. Suppose you want to analyze the price fluctuations of some products on an eCommerce website. Now, you have to list out all the possible factors of the cause & cross-check it with each product. Similarly, when you want to scrape data, then you have to list out parent HTML tags & check respective child HTML tag to extract the data by repeating this activity.
Steps Required for Web Scraping
Creating the file
Install & Call the required libraries
Select the Website & Data needed to Scrape
Set the URL & Check the Response Code
Inspect & Find the Proper HTML tags
Include the HTML tags in our Code
Cross-check the Scraped Data
I’m using Visual Studio to run this task.
Step 1- Creating the file
To create a file, I need to run npm init and give a few details as needed in the below screenshot.
Step 2- Install & Call the required libraries
Need to run the below codes to install these libraries.
Install Libraries
Once the libraries are properly installed then you will see these messages are getting displayed.
logs after packages get installed
Call the required libraries:
Call the library
Step 3- Select the Website & Data needed to Scrape.
I picked this website “ and want to scrape data of gold rates along with dates.
Data we want to scrape
Step 4- Set the URL & Check the Response Code
Node JS code looks like this to pass the URL & check the response code.
Passing URL & Getting Response Code
Step 5- Inspect & Find the Proper HTML tags
It’s quite easy to find the proper HTML tags in which your data is present.
To see the HTML tags; right-click and select the inspect option.
Inspecting the HTML Tags
Select proper HTML Tags:-
If you noticed there are three columns in our table, so our HTML tag for table row would be “HeaderRow” & all the column names are present with tag “th” (Table Header).
And for each table row (“tr”) our data resides in “DataRow” HTML tag
Now, I need to get all HTML tags to reside under “HeaderRow” & need to find all the “th” HTML tags & finally iterate through “DataRow” HTML tag to get all the data within it.
Step 6- Include the HTML tags in our Code
After including the HTML tags, our code will be:-
Code Snippet
Step 7- Cross-check the Scraped Data
Print the Data, so the code for this is like:-
Our Scraped Data
If you go to a more granular level of HTML Tags & iterate them accordingly, you will get more precise data.
That’s all about web scraping & how to get rare quality data like gold.
I tried to explain Web Scraping using Node JS in a precise way. Hopefully, this will help you.
Find full code on
If you have any questions about the code or web scraping in general, reach out to me on
We will meet again with something new.
Till then,
Happy Coding..!
Getting Started with Web Scraping in JavaScript - Geekflare

Getting Started with Web Scraping in JavaScript – Geekflare

Web scraping is one of the most interesting things in the coding world.
What is web scraping?
Why is it even exist?
Let’s find out the answers.
What is Web Scraping?
Web scraping is an automated task to extract data from websites.
There are many applications of web scraping. Extracting the prices of products and comparing them with different e-Commerce platforms. Getting a daily quote from the web. Building your own search engine like Google, Yahoo, etc.., The list goes on.
You can do more than you think with web scraping. Once you get to know how to extract the data from websites, then you can do whatever you want with the data.
The program which extracts the data from websites is called a web scraper. You are going to learn to write web scrapers in JavaScript.
There are mainly two parts to web scraping.
Getting the data using request libraries and a headless browser.
Parsing the data to extract the exact information that we want from the data.
Without further ado let’s get started.
Project Setup
I assume you have Node installed, if not check out the NodeJS installation guide.
We are going to use the packages node-fetch and cheerio for web scraping in JavaScript. Let’s set up the project with the npm to work with a third-party package.
Let’s quickly see the steps to complete our setup.
Create a directory called web_scraping and navigate to it.
Run the command npm init to initialize the project.
Answer all the questions based on your preference.
Now, install the packages using the command
npm install node-fetch cheerio
Let’s see the glimpses of the installed packages.
The package node-fetch brings the to the node js environment. It helps to make the HTTP requests and get the raw data.
The package cheerio is used to parse and extract the information that is necessary from the raw data.
Two packages node-fetch and cheerio are good enough for web scraping in JavaScript. We are not going to see every method that the packages are providing. We will see the flow of web scraping and the most useful methods in that flow.
You will learn web scraping by doing it. So, let’s get to the work.
Scraping Cricket World Cup List
Here in this section, we are going to do actual web scraping.
What are we extracting?
By the title of the section, I think you would easily guess it. Yeah, whatever you are thinking is correct. Let’s extract all cricket world cup winners and runner-ups till now.
Create a file called in the project.
We will be using the Wikipedia Cricket World Cup page to get the desired information.
First, get the raw data using the node-fetch package.
Below code gets the raw data of the above Wikipedia page.
const fetch = require(“node-fetch”);
// function to get the raw data
const getRawData = (URL) => {
return fetch(URL)
((response) => ())
((data) => {
return data;});};
// URL for data
const URL = “;
// start of the program
const getCricketWorldCupsList = async () => {
const cricketWorldCupRawData = await getRawData(URL);
// invoking the main function
We got the raw data from the URL. Now, it’s time to extract the information that we need from the raw data. Let’s use the package cheerio to extract the data.
Extracting data that involves HTML tags with cheerio is a cakewalk. Before getting into the actual data, let’s see some sample data parsing using cheerio.
Parse the HTML data using the method.
const parsedSampleData = (

I’m title

We have parsed the above HTML code. How to extract the p tag content from it? It’s the same as the selectors in JavaScript DOM manipulation.
You can select the tags as you want. You can check out different methods from the cheerio official website.
Now, it’s time to extract the world cup list. To extract the information, we need to know the HTML tags that information lies on the page. Go to the cricket world cup Wikipedia page and inspect the page to get HTML tags information.
Here is the complete code.
const cheerio = require(“cheerio”);
// parsing the data
const parsedCricketWorldCupData = (cricketWorldCupRawData);
// extracting the table data
const worldCupsDataTable = parsedCricketWorldCupData(“table”)[0]. children[1]. children;
(“Year — Winner — Runner”);
rEach((row) => {
// extracting `td` tags
if ( === “tr”) {
let year = null,
winner = null,
runner = null;
const columns = ((column) => === “td”);
// extracting year
const yearColumn = columns[0];
if (yearColumn) {
year = ildren[0];
if (year) {
year = ildren[0];}}
// extracting winner
const winnerColumn = columns[3];
if (winnerColumn) {
winner = ildren[1];
if (winner) {
winner = ildren[0];}}
// extracting runner
const runnerColumn = columns[5];
if (runnerColumn) {
runner = ildren[1];
if (runner) {
runner = ildren[0];}}
if (year && winner && runner) {
(`${year} — ${winner} — ${runner}`);}}});};
And, here is the scraped data.
Year — Winner — Runner
1975 — West Indies — Australia
1979 — West Indies — England
1983 — India — West Indies
1987 — Australia — England
1992 — Pakistan — England
1996 — Sri Lanka — Australia
1999 — Australia — Pakistan
2003 — Australia — India
2007 — Australia — Sri Lanka
2011 — India — Sri Lanka
2015 — Australia — New Zealand
2019 — England — New Zealand
Cool , is int’ it?
Scraping Template
Getting the raw data from the URL is common in every web scraping project. The only part that changes is extracting the data as per the requirement. You can try the below code as a template.
const fs = require(“fs”);
const scrapeData = async () => {
const rawData = await getRawData(URL);
const parsedData = (rawData);
// write code to extract the data
// here
//… };
You have learned how to scrape a webpage. Now, it’s your turn to practice coding.
I would also suggest checking out popular web scraping frameworks to explore and cloud-based web-scraping solutions.
Happy Coding
Is Web Scraping Illegal? Depends on What the Meaning of the Word Is

Is Web Scraping Illegal? Depends on What the Meaning of the Word Is

Depending on who you ask, web scraping can be loved or hated.
Web scraping has existed for a long time and, in its good form, it’s a key underpinning of the internet. “Good bots” enable, for example, search engines to index web content, price comparison services to save consumers money, and market researchers to gauge sentiment on social media.
“Bad bots, ” however, fetch content from a website with the intent of using it for purposes outside the site owner’s control. Bad bots make up 20 percent of all web traffic and are used to conduct a variety of harmful activities, such as denial of service attacks, competitive data mining, online fraud, account hijacking, data theft, stealing of intellectual property, unauthorized vulnerability scans, spam and digital ad fraud.
So, is it Illegal to Scrape a Website?
So is it legal or illegal? Web scraping and crawling aren’t illegal by themselves. After all, you could scrape or crawl your own website, without a hitch.
Startups love it because it’s a cheap and powerful way to gather data without the need for partnerships. Big companies use web scrapers for their own gain but also don’t want others to use bots against them.
The general opinion on the matter does not seem to matter anymore because in the past 12 months it has become very clear that the federal court system is cracking down more than ever.
Let’s take a look back. Web scraping started in a legal grey area where the use of bots to scrape a website was simply a nuisance. Not much could be done about the practice until in 2000 eBay filed a preliminary injunction against Bidder’s Edge. In the injunction eBay claimed that the use of bots on the site, against the will of the company violated Trespass to Chattels law.
The court granted the injunction because users had to opt in and agree to the terms of service on the site and that a large number of bots could be disruptive to eBay’s computer systems. The lawsuit was settled out of court so it all never came to a head but the legal precedent was set.
In 2001 however, a travel agency sued a competitor who had “scraped” its prices from its Web site to help the rival set its own prices. The judge ruled that the fact that this scraping was not welcomed by the site’s owner was not sufficient to make it “unauthorized access” for the purpose of federal hacking laws.
Two years later the legal standing for eBay v Bidder’s Edge was implicitly overruled in the “Intel v. Hamidi”, a case interpreting California’s common law trespass to chattels. It was the wild west once again. Over the next several years the courts ruled time and time again that simply putting “do not scrape us” in your website terms of service was not enough to warrant a legally binding agreement. For you to enforce that term, a user must explicitly agree or consent to the terms. This left the field wide open for scrapers to do as they wish.
Fast forward a few years and you start seeing a shift in opinion. In 2009 Facebook won one of the first copyright suits against a web scraper. This laid the groundwork for numerous lawsuits that tie any web scraping with a direct copyright violation and very clear monetary damages. The most recent case being AP v Meltwater where the courts stripped what is referred to as fair use on the internet.
Previously, for academic, personal, or information aggregation people could rely on fair use and use web scrapers. The court now gutted the fair use clause that companies had used to defend web scraping. The court determined that even small percentages, sometimes as little as 4. 5% of the content, are significant enough to not fall under fair use. The only caveat the court made was based on the simple fact that this data was available for purchase. Had it not been, it is unclear how they would have ruled. Then a few months back the gauntlet was dropped.
Andrew Auernheimer was convicted of hacking based on the act of web scraping. Although the data was unprotected and publically available via AT&T’s website, the fact that he wrote web scrapers to harvest that data in mass amounted to “brute force attack”. He did not have to consent to terms of service to deploy his bots and conduct the web scraping. The data was not available for purchase. It wasn’t behind a login. He did not even financially gain from the aggregation of the data. Most importantly, it was buggy programing by AT&T that exposed this information in the first place. Yet Andrew was at fault. This isn’t just a civil suit anymore. This charge is a felony violation that is on par with hacking or denial of service attacks and carries up to a 15-year sentence for each charge.
In 2016, Congress passed its first legislation specifically to target bad bots — the Better Online Ticket Sales (BOTS) Act, which bans the use of software that circumvents security measures on ticket seller websites. Automated ticket scalping bots use several techniques to do their dirty work including web scraping that incorporates advanced business logic to identify scalping opportunities, input purchase details into shopping carts, and even resell inventory on secondary markets.
To counteract this type of activity, the BOTS Act:
Prohibits the circumvention of a security measure used to enforce ticket purchasing limits for an event with an attendance capacity of greater than 200 persons.
Prohibits the sale of an event ticket obtained through such a circumvention violation if the seller participated in, had the ability to control, or should have known about it.
Treats violations as unfair or deceptive acts under the Federal Trade Commission Act. The bill provides authority to the FTC and states to enforce against such violations.
In other words, if you’re a venue, organization or ticketing software platform, it is still on you to defend against this fraudulent activity during your major onsales.
The UK seems to have followed the US with its Digital Economy Act 2017 which achieved Royal Assent in April. The Act seeks to protect consumers in a number of ways in an increasingly digital society, including by “cracking down on ticket touts by making it a criminal offence for those that misuse bot technology to sweep up tickets and sell them at inflated prices in the secondary market. ”
In the summer of 2017, LinkedIn sued hiQ Labs, a San Francisco-based startup. hiQ was scraping publicly available LinkedIn profiles to offer clients, according to its website, “a crystal ball that helps you determine skills gaps or turnover risks months ahead of time. ”
You might find it unsettling to think that your public LinkedIn profile could be used against you by your employer.
Yet a judge on Aug. 14, 2017 decided this is okay. Judge Edward Chen of the U. S. District Court in San Francisco agreed with hiQ’s claim in a lawsuit that Microsoft-owned LinkedIn violated antitrust laws when it blocked the startup from accessing such data. He ordered LinkedIn to remove the barriers within 24 hours. LinkedIn has filed to appeal.
The ruling contradicts previous decisions clamping down on web scraping. And it opens a Pandora’s box of questions about social media user privacy and the right of businesses to protect themselves from data hijacking.
There’s also the matter of fairness. LinkedIn spent years creating something of real value. Why should it have to hand it over to the likes of hiQ — paying for the servers and bandwidth to host all that bot traffic on top of their own human users, just so hiQ can ride LinkedIn’s coattails?
I am in the business of blocking bots. Chen’s ruling has sent a chill through those of us in the cybersecurity industry devoted to fighting web-scraping bots.
I think there is a legitimate need for some companies to be able to prevent unwanted web scrapers from accessing their site.
In October of 2017, and as reported by Bloomberg, Ticketmaster sued Prestige Entertainment, claiming it used computer programs to illegally buy as many as 40 percent of the available seats for performances of “Hamilton” in New York and the majority of the tickets Ticketmaster had available for the Mayweather v. Pacquiao fight in Las Vegas two years ago.
Prestige continued to use the illegal bots even after it paid a $3. 35 million to settle New York Attorney General Eric Schneiderman’s probe into the ticket resale industry.
Under that deal, Prestige promised to abstain from using bots, Ticketmaster said in the complaint. Ticketmaster asked for unspecified compensatory and punitive damages and a court order to stop Prestige from using bots.
Are the existing laws too antiquated to deal with the problem? Should new legislation be introduced to provide more clarity? Most sites don’t have any web scraping protections in place. Do the companies have some burden to prevent web scraping?
As the courts try to further decide the legality of scraping, companies are still having their data stolen and the business logic of their websites abused. Instead of looking to the law to eventually solve this technology problem, it’s time to start solving it with anti-bot and anti-scraping technology today.
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Frequently Asked Questions about javascript screen scraping

Is JavaScript good for web scraping?

js, JavaScript is a great language to use for a web scraper: not only is Node fast, but you’ll likely end up using a lot of the same methods you’re used to from querying the DOM with front-end JavaScript.

Can you scrape data with JavaScript?

You are going to learn to write web scrapers in JavaScript. There are mainly two parts to web scraping. Getting the data using request libraries and a headless browser. Parsing the data to extract the exact information that we want from the data.Apr 4, 2021

Is screen scraping legal?

Web scraping and crawling aren’t illegal by themselves. … Web scraping started in a legal grey area where the use of bots to scrape a website was simply a nuisance. Not much could be done about the practice until in 2000 eBay filed a preliminary injunction against Bidder’s Edge.

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