Python Scrapy Scrape Web Data Using Python

Python Scrapy Scrape Web Data Using Python

Making Web Crawlers Using Scrapy for Python – DataCamp

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If you would like an overview of web scraping in Python, take DataCamp’s Web Scraping with Python course.
In this tutorial, you will learn how to use Scrapy which is a Python framework using which you can handle large amounts of data! You will learn Scrapy by building a web scraper for which is an e-commerce website. Let’s get scrapping!
Scrapy Overview
Scrapy Vs. BeautifulSoup
Scrapy Installation
Scrapy Shell
Creating a project and Creating a custom spider
A basic HTML and CSS knowledge will help you understand this tutorial with greater ease and speed. Read this article for a fresher on HTML and CSS.
Web scraping has become an effective way of extracting information from the web for decision making and analysis. It has become an essential part of the data science toolkit. Data scientists should know how to gather data from web pages and store that data in different formats for further analysis.
Any web page you see on the internet can be crawled for information and anything visible on a web page can be extracted [2]. Every web page has its own structure and web elements that because of which you need to write your web crawlers/spiders according to the web page being extracted.
Scrapy provides a powerful framework for extracting the data, processing it and then save it.
Scrapy uses spiders, which are self-contained crawlers that are given a set of instructions [1]. In Scrapy it is easier to build and scale large crawling projects by allowing developers to reuse their code.
In this section, you will have an overview of one of the most popularly used web scraping tool called BeautifulSoup and its comparison to Scrapy.
Scrapy is a Python framework for web scraping that provides a complete package for developers without worrying about maintaining code.
Beautiful Soup is also widely used for web scraping. It is a Python package for parsing HTML and XML documents and extract data from them. It is available for Python 2. 6+ and Python 3.
Here are some differences between them in a nutshell:

Scrapy is the complete package for downloading web pages, processing them and save it in files and databases
BeautifulSoup is basically an HTML and XML parser and requires additional libraries such as requests, urlib2 to open URLs and store the result [6] Learning Curve
Scrapy is a powerhouse for web scraping and offers a lot of ways to scrape a web page. It requires more time to learn and understand how Scrapy works but once learned, eases the process of making web crawlers and running them from just one line of command. Becoming an expert in Scrapy might take some practice and time to learn all functionalities.
BeautifulSoup is relatively easy to understand for newbies in programming and can get smaller tasks done in no time
Speed and Load
Scrapy can get big jobs done very easily. It can crawl a group of URLs in no more than a minute depending on the size of the group and does it very smoothly as it uses Twister which works asynchronously (non-blocking) for concurrency.
BeautifulSoup is used for simple scraping jobs with efficiency. It is slower than Scrapy if you do not use multiprocessing.
Extending functionality
Scrapy provides Item pipelines that allow you to write functions in your spider that can process your data such as validating data, removing data and saving data to a database. It provides spider Contracts to test your spiders and allows you to create generic and deep crawlers as well. It allows you to manage a lot of variables such as retries, redirection and so on.
If the project does not require much logic, BeautifulSoup is good for the job, but if you require much customization such as proxys, managing cookies, and data pipelines, Scrapy is the best option.
Information: Synchronous means that you have to wait for a job to finish to start a new job while Asynchronous means you can move to another job before the previous job has finished
Here is an interesting DataCamp BeautifulSoup tutorial to learn.
With Python 3. 0 (and onwards) installed, if you are using anaconda, you can use conda to install scrapy. Write the following command in anaconda prompt:
conda install -c conda-forge scrapy
To install anaconda, look at these DataCamp tutorials for Mac and Windows.
Alternatively, you can use Python Package Installer pip. This works for Linux, Mac, and Windows:
pip install scrapy
Scrapy also provides a web-crawling shell called as Scrapy Shell, that developers can use to test their assumptions on a site’s behavior. Let us take a web page for tablets at AliExpress e-commerce website. You can use the Scrapy shell to see what components the web page returns and how you can use them to your requirements.
Open your command line and write the following command:
scrapy shell
If you are using anaconda, you can write the above command at the anaconda prompt as well. Your output on the command line or anaconda prompt will be something like this:
You have to run a crawler on the web page using the fetch command in the Scrapy shell. A crawler or spider goes through a webpage downloading its text and metadata.
Note: Always enclose URL in quotes, both single and double quotes work
The output will be as follows:
The crawler returns a response which can be viewed by using the view(response) command on shell:
And the web page will be opened in the default browser.
You can view the raw HTML script by using the following command in Scrapy shell:
You will see the script that’s generating the webpage. It is the same content that when you left right-click any blank area on a webpage and click view source or view page source. Since, you need only relevant information from the entire script, using browser developer tools you will inspect the required element. Let us take the following elements:
Tablet name
Tablet price
Number of orders
Name of store
Right-click on the element you want and click inspect like below:
Developer tools of the browser will help you a lot with web scraping. You can see that it is an tag with a class product and the text contains the name of the product:
You can extract this using the element attributes or the css selector like classes. Write the following in the Scrapy shell to extract the product name:
(“. product::text”). extract_first()
The output will be:
extract_first() extract the first element that satisfies the css selector. If you want to extract all the product names use extract():
(“. extract()
Following code will extract price range of the products:
(“”). extract()
Similarly, you can try with a number of orders and the name of the store.
XPath is a query language for selecting nodes in an XML document [7]. You can navigate through an XML document using XPath. Behind the scenes, Scrapy uses Xpath to navigate to HTML document items. The CSS selectors you used above are also converted to XPath, but in many cases, CSS is very easy to use. But you should know how the XPath in Scrapy works.
Go to your Scrapy Shell and write fetch() the same way as before. Try out the following code snippets [3]:
(‘/html’). extract()
This will show you all the code under the tag. / means direct child of the node. If you want to get the

tags under the html tag you will write [3]:
(‘/html//div’). extract()
For XPath, you must learn to understand the use of / and // to know how to navigate through child and descendent nodes. Here is a helpful tutorial for XPath Nodes and some examples to try out.
If you want to get all

tags, you can do it by drilling down without using the /html [3]:
(“//div”). extract()
You can further filter your nodes that you start from and reach your desired nodes by using attributes and their values. Below is the syntax to use classes and their values.
(“//div[@class=’quote’]/span[@class=’text’]”). extract()
(“//div[@class=’quote’]/span[@class=’text’]/text()”). extract()
Use text() to extract all text inside nodes
Consider the following HTML code:
You want to get the text inside the tag, which is child node of

haing classes site-notice-container container you can do it as follows:
(‘//div[@class=”site-notice-container container”]/a[@class=”notice-close”]/text()’). extract()
Creating a Scrapy project and Custom Spider
Web scraping can be used to make an aggregator that you can use to compare data. For example, you want to buy a tablet, and you want to compare products and prices together you can crawl your desired pages and store in an excel file. Here you will be scraping for tablets information.
Now, you will create a custom spider for the same page. First, you need to create a Scrapy project in which your code and results will be stored. Write the following command in the command line or anaconda prompt.
scrapy startproject aliexpress
This will create a hidden folder in your default python or anaconda installation. aliexpress will be the name of the folder. You can give any name. You can view the folder contents directly through explorer. Following is the structure of the folder:
deploy configuration file
Project’s Python module, you’ll import your code from here
Initialization file
project items file
project pipelines file
project settings file
a directory where you’ll later put your spiders
Once you have created the project you will change to the newly created directory and write the following command:
[scrapy genspider aliexpress_tablets]()
This creates a template file named in the spiders directory as discussed above. The code in that file is as below:
import scrapy
class AliexpressTabletsSpider():
name = ‘aliexpress_tablets’
allowed_domains = [”] start_urls = [”] def parse(self, response):
In the above code you can see name, allowed_domains, sstart_urls and a parse function.
name: Name is the name of the spider. Proper names will help you keep track of all the spider’s you make. Names must be unique as it will be used to run the spider when scrapy crawl name_of_spider is used.
allowed_domains (optional): An optional python list, contains domains that are allowed to get crawled. Request for URLs not in this list will not be crawled. This should include only the domain of the website (Example:) and not the entire URL specified in start_urls otherwise you will get warnings.
start_urls: This requests for the URLs mentioned. A list of URLs where the spider will begin to crawl from, when no particular URLs are specified [4]. So, the first pages downloaded will be those listed here. The subsequent Request will be generated successively from data contained in the start URLs [4].
parse(self, response): This function will be called whenever a URL is crawled successfully. It is also called the callback function. The response (used in Scrapy shell) returned as a result of crawling is passed in this function, and you write the extraction code inside it!
Information: You can use BeautifulSoup inside parse() function of the Scrapy spider to parse the html document.
Note: You can extract data through css selectors using () as discussed in scrapy shell section but also using XPath (XML) that allows you to access child elements. You will see the example of () in the code edited in pass() function.
You will make changes to the file. I have added another URL in start_urls. You can add the extraction logic to the pass() function as below:
# -*- coding: utf-8 -*-
start_urls = [”,
”] print(“procesing:”)
#Extract data using css selectors
(‘. product::text’). extract()
(”). extract()
#Extract data using xpath
(“//em[@title=’Total Orders’]/text()”). extract()
(“//a[@class=’store $p4pLog’]/text()”). extract()
row_data=zip(product_name, price_range, orders, company_name)
#Making extracted data row wise
for item in row_data:
#create a dictionary to store the scraped info
scraped_info = {
‘product_name’: item[0], #item[0] means product in the list and so on, index tells what value to assign
‘price_range’: item[1],
‘orders’: item[2],
‘company_name’: item[3], }
#yield or give the scraped info to scrapy
yield scraped_info
Information: zip() takes n number of iterables and returns a list of tuples. ith element of the tuple is created using the ith element from each of the iterables. [8] The yield keyword is used whenever you are defining a generator function. A generator function is just like a normal function except it uses yield keyword instead of return. The yield keyword is used whenever the caller function needs a value and the function containing yield will retain its local state and continue executing where it left off after yielding value to the caller function. Here yield gives the generated dictionary to Scrapy which will process and save it!
Now you can run the spider:
scrapy crawl aliexpress_tablets
You will see a long output at the command line like below:
Exporting data
You will need data to be presented as a CSV or JSON so that you can further use the data for analysis. This section of the tutorial will take you through how you can save CSV and JSON file for this data.
To save a CSV file, open from the project directory and add the following lines:
After saving the, rerun the scrapy crawl aliexpress_tablets in your project directory.
The CSV file will look like:
Note: Everytime you run the spider it will append the file.
FEED_FORMAT [5]: This sets the format you want to store the data. Supported formats are: + JSON
+ JSON Lines
FEED_URI [5]: This gives the location of the file. You can store a file on your local file storage or an FTP as well.
Scrapy’s Feed Export can also add a timestamp and the name of spider to your file name, or you can use these to identify a directory in which you want to store. %(time)s: gets replaced by a timestamp when the feed is being created [5]%(name)s: gets replaced by the spider name [5] For Example:
Store in FTP using one directory per spider [5]:
ftpuser:[email protected]/scraping/feeds/%(name)s/%(time)
The Feed changes you make in will apply to all spiders in the project. You can also set custom settings for a particular spider that will override the settings in the file.
custom_settings={ ‘FEED_URI’: “aliexpress_%(time)”,
‘FEED_FORMAT’: ‘json’}
#yield or give the scraped info to Scrapy
returns the URL of the page from which response is generated. After running the crawler using scrapy crawl aliexpress_tablets you can view the json file:
Following Links
You must have noticed, that there are two links in the start_urls. The second link is the page 2 of the same tablets search results. It will become impractical to add all links. A crawler should be able to crawl by itself through all the pages, and only the starting point should be mentioned in the start_urls.
If a page has subsequent pages, you will see a navigator for it at the end of the page that will allow moving back and forth the pages. In the case you have been implementing in this tutorial, you will see it like this:
Here is the code that you will see:
As you can see that under there is a tag with class class that is the current page you are on, and under that are all
tags with links to the next page. Everytime you will have to get the tags after this tag. Here comes a little bit of CSS! In this, you have to get sibling node and not a child node, so you have to make a css selector that tells the crawler to find tags that are after tag with class.
Remember! Each web page has its own structure. You will have to study the structure a little bit on how you can get the desired element. Always try out (SELECTOR) on Scrapy Shell before writing them in code.
Modify your as below:
‘FEED_FORMAT’: ‘csv’}
NEXT_PAGE_SELECTOR = ‘ + a::attr(href)’
next_page = (NEXT_PAGE_SELECTOR). extract_first()
if next_page:
yield quest(
response. urljoin(next_page), )
In the above code:
you first extracted the link of the next page using next_page = (NEXT_PAGE_SELECTOR). extract_first() and then if the variable next_page gets a link and is not empty, it will enter the if body.
response. urljoin(next_page): The parse() method will use this method to build a new url and provide a new request, which will be sent later to the callback. [9] After receiving the new URL, it will scrape that link executing the for body and again look for the next page. This will continue until it doesn’t get a next page link.
Here you might want to sit back and enjoy your spider scraping all the pages. The above spider will extract from all subsequent pages. That will be a lot of scraping! But your spider will do it! Below you can see the size of the file has reached 1. 1MB.
Scrapy does it for you!
In this tutorial, you have learned about Scrapy, how it compares to BeautifulSoup, Scrapy Shell and how to write your own spiders in Scrapy. Scrapy handles all the heavy load of coding for you, from creating project files and folders till handling duplicate URLs it helps you get heavy-power web scraping in minutes and provides you support for all common data formats that you can further input in other programs. This tutorial will surely help you understand Scrapy and its framework and what you can do with it. To become a master in Scrapy, you will need to go through all the fantastic functionalities it has to provide, but this tutorial has made you capable of scraping groups of web pages in an efficient way.
For further reading, you can refer to Offical Scrapy Docs.
Also, don’t forget to check out DataCamp’s Web Scraping with Python course.
[1] [2] [3] [4] [5] [6] [7] [8] [9] Implementing Web Scraping in Python with Scrapy

Implementing Web Scraping in Python with Scrapy

Nowadays data is everything and if someone wants to get data from webpages then one way to use an API or implement Web Scraping techniques. In Python, Web scraping can be done easily by using scraping tools like BeautifulSoup. But what if the user is concerned about performance of scraper or need to scrape data overcome this problem, one can make use of MultiThreading/Multiprocessing with BeautifulSoup module and he/she can create spider, which can help to crawl over a website and extract data. In order to save the time one use the help of Scrapy one can:
1. Fetch millions of data efficiently
2. Run it on server
3. Fetching data
4. Run spider in multiple processesScrapy comes with whole new features of creating spider, running it and then saving data easily by scraping it. At first it looks quite confusing but it’s for the ’s talk about the installation, creating a spider and then testing 1: Creating virtual environmentIt is good to create one virtual environment as it isolates the program and doesn’t affect any other programs present in the machine. To create virtual environment first install it by using:sudo apt-get install python3-venvCreate one folder and then activate it:mkdir scrapy-project && cd scrapy-project
python3 -m venv myvenv
If above command gives Error then try this:python3. 5 -m venv myvenvAfter creating virtual environment activate it by using:source myvenv/bin/activate Step 2: Installing Scrapy moduleInstall Scrapy by using:pip install scrapyTo install scrapy for any specific version of python:python3. 5 -m pip install scrapyReplace 3. 5 version with some other version like 3. 6. Step 3: Creating Scrapy projectWhile working with Scrapy, one needs to create scrapy startproject gfgIn Scrapy, always try to create one spider which helps to fetch data, so to create one, move to spider folder and create one python file over there. Create one spider with name python file. Step 4: Creating SpiderMove to the spider folder and create While creating spider, always create one class with unique name and define requirements. First thing is to name the spider by assigning it with name variable and then provide the starting URL through which spider will start crawling. Define some methods which helps to crawl much deeper into that website. For now, let’s scrap all the URL present and store all those scrapyclass ExtractUrls(): name = “extract” def start_requests(self): for url in urls: yield quest(url = url, callback =)Main motive is to get each url and then request it. Fetch all the urls or anchor tags from it. To do this, we need to create one more method parse, to fetch data from the given url. Step 5: Fetching data from given pageBefore writing parse function, test few things like how to fetch any data from given page. To do this make use of scrapy shell. It is just like python interpreter but with the ability to scrape data from the given url. In short, its a python interpreter with Scrapy shell URLNote: Make sure to in the same directory where is present, else it will not shell for fetching data from the given page, use selectors. These selectors can be either from CSS or from Xpath. For now, let’s try to fetch all url by using CSS get anchor tag (‘a’)To extract the data:links = (‘a’). extract()For example, links[0] will show something like this:’
GeeksforGeeks‘To get href attribute, use attributes = (‘a::attr(href)’). extract()This will get all the href data which is very useful. Make use of this link and start requesting, let’s create parse method and fetch all the urls and then yield it. Follow that particular URL and fetch more links from that page and this will keep on happening again and again. In short, we are fetching all url present on that, by default, filters those url which has already been visited. So it will not crawl the same url path again. But it’s possible that in two different pages there are two or more than two similar links. For example, in each page, the header link will be available which means that this header link will come in each page request. So try to exclude it by checking parse(self, response): title = (‘title::text’). extract_first() links = (‘a::attr(href)’). extract() for link in links: yield { ‘title’: title, ‘links’: link} if ‘geeksforgeeks’ in link: yield quest(url = link, callback =) Below is the implementation of scraper:import scrapyclass ExtractUrls(): name = “extract” def start_requests(self): for url in urls: yield quest(url = url, callback =) def parse(self, response): title = (‘title::text’). extract() for link in links: yield { ‘title’: title, ‘links’: link} if ‘geeksforgeeks’ in link: yield quest(url = link, callback =) Step 6: In last step, Run the spider and get output in simple json filescrapy crawl NAME_OF_SPIDER -o links. jsonHere, name of spider is “extract” for given example. It will fetch loads of data within few: Note: Scraping any web page is not a legal activity. Don’t perform any scraping operation without permission. Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics. To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course
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 python scrapy scrape web data using python

How do you scrape data from a website using Python Scrapy?

In Python, Web scraping can be done easily by using scraping tools like BeautifulSoup….Step 5 : Fetching data from given pageTo get anchor tag : response.css(‘a’)To extract the data : links = response.css(‘a’).extract()To get href attribute, use attributes tag. links = response.css(‘a::attr(href)’).extract()Nov 8, 2019

Is web scraping with Python legal?

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. … Big companies use web scrapers for their own gain but also don’t want others to use bots against them.

What is Scrapy web scraping?

Scrapy is a Python framework for large scale web scraping. It gives you all the tools you need to efficiently extract data from websites, process them as you want, and store them in your preferred structure and format. … Scrapy is that framework.Jul 25, 2017

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