How To Parse Data From Website
Web Scraping Basics – Towards Data Science
How to scrape data from a website in PythonWe always say “Garbage in Garbage out” in data science. If you do not have good quality and quantity of data, most likely you would not get many insights out of it. Web Scraping is one of the important methods to retrieve third-party data automatically. In this article, I will be covering the basics of web scraping and use two examples to illustrate the 2 different ways to do it in is Web ScrapingWeb Scraping is an automatic way to retrieve unstructured data from a website and store them in a structured format. For example, if you want to analyze what kind of face mask can sell better in Singapore, you may want to scrape all the face mask information on an E-Commerce website like you scrape from all the websites? Scraping makes the website traffic spike and may cause the breakdown of the website server. Thus, not all websites allow people to scrape. How do you know which websites are allowed or not? You can look at the ‘’ file of the website. You just simply put after the URL that you want to scrape and you will see information on whether the website host allows you to scrape the for an file of can see that Google does not allow web scraping for many of its sub-websites. However, it allows certain paths like ‘/m/finance’ and thus if you want to collect information on finance then this is a completely legal place to scrape. Another note is that you can see from the first row on User-agent. Here Google specifies the rules for all of the user-agents but the website may give certain user-agent special permission so you may want to refer to information does web scraping work? Web scraping just works like a bot person browsing different pages website and copy pastedown all the contents. When you run the code, it will send a request to the server and the data is contained in the response you get. What you then do is parse the response data and extract out the parts you do we do web scraping? Alright, finally we are here. There are 2 different approaches for web scraping depending on how does website structure their roach 1: If website stores all their information on the HTML front end, you can directly use code to download the HTML contents and extract out useful are roughly 5 steps as below:Inspect the website HTML that you want to crawlAccess URL of the website using code and download all the HTML contents on the pageFormat the downloaded content into a readable formatExtract out useful information and save it into a structured formatFor information displayed on multiple pages of the website, you may need to repeat steps 2–4 to have the complete and Cons for this approach: It is simple and direct. However, if the website’s front-end structure changes then you need to adjust your code roach 2: If website stores data in API and the website queries the API each time when user visit the website, you can simulate the request and directly query data from the APISteps:Inspect the XHR network section of the URL that you want to crawlFind out the request-response that gives you the data that you wantDepending on the type of request(post or get) and also the request header & payload, simulate the request in your code and retrieve the data from API. Usually, the data got from API is in a pretty neat format. Extract out useful information that you needFor API with a limit on query size, you will need to use ‘for loop’ to repeatedly retrieve all the dataPros and Cons for this approach: It is definitely a preferred approach if you can find the API request. The data you receive will be more structured and stable. This is because compared to the website front end, it is less likely for the company to change its backend API. However, it is a bit more complicated than the first approach especially if authentication or token is required. Different tools and library for web scrapingThere are many different scraping tools available that do not require any coding. However, most people still use the Python library to do web scraping because it is easy to use and also you can find an answer in its big most commonly used library for web scraping in Python is Beautiful Soup, Requests, and autiful Soup: It helps you parse the HTML or XML documents into a readable format. It allows you to search different elements within the documents and help you retrieve required information quests: It is a Python module in which you can send HTTP requests to retrieve contents. It helps you to access website HTML contents or API by sending Get or Post lenium: It is widely used for website testing and it allows you to automate different events(clicking, scrolling, etc) on the website to get the results you can either use Requests + Beautiful Soup or Selenium to do web scraping. Selenium is preferred if you need to interact with the website(JavaScript events) and if not I will prefer Requests + Beautiful Soup because it’s faster and Scraping Example:Problem statement: I want to find out about the local market for face mask. I am interested on online face mask price, discount, ratings, sold quantity roach 1 Example(Download HTML for all pages) — Lazada:Step 1: Inspect the website(if using Chrome you can right-click and select inspect)Inspect Lazada page on ChromeHTML result for price on LazadaI can see that data I need are all wrap in the HTML element with the unique class 2: Access URL of the website using code and download all the HTML contents on the page# import libraryfrom bs4 import BeautifulSoupimport requests# Request to website and download HTML contentsurl=”(url)content=req. textRequest content before applying Beautiful SoupI used the requests library to get data from a website. You can see that so far what we have is unstructured 3: Format the downloaded content into a readable formatsoup=BeautifulSoup(content)This step is very straightforward and what we do is just parse unstructured text into Beautiful Soup and what you get is as content after using Beautiful SoupThe output is a much more readable format and you can search different HTML elements or classes in 4: Extract out useful information and save it into a structured formatThis step requires some time to understand website structure and find out where the data is stored exactly. For the Lazada case, it is stored in a Script section in JSON (‘script’)[3]ad_json((“geData=”)[1], orient=’records’)#Store datafor item in [‘listItems’, ‘mods’]: (item[‘brandName’]) (item[‘price’]) (item[‘location’]) (ifnull(item[‘description’], 0)) (ifnull(item[‘ratingScore’], 0))I created 5 different lists to store the different fields of data that I need. I used the for loop here to loop through the list of items in the JSON documents inside. After that, I combine the 5 columns into the output file. #save data into an Frame({‘brandName’:brand_name, ‘price’:price, ‘location’:location, ‘description’:description, ‘rating score’:rating_score})Final output in Python DataFrame formatStep 5: For information displayed on multiple pages of the website, you may need to repeat steps 2–4 to have the complete you want to scrape all the data. Firstly you should find out about the total count of sellers. Then you should loop through pages by passing in incremental page numbers using payload to URL. Below is the full code that I used to scrape and I loop through the first 50 pages to get content on those i in range(1, 50): (max((5, 1), 2)) print(‘page’+str(i)) payload[‘page’]=i (url, params=payload) soup=BeautifulSoup(content) ndAll(‘script’)[3] ad_json((“geData=”)[1], orient=’records’) for item in [‘listItems’, ‘mods’]: (item[‘brandName’]) (item[‘price’]) (item[‘location’]) (ifnull(item[‘description’], 0)) (ifnull(item[‘ratingScore’], 0))Approach 2 example(Query data directly from API) — Ezbuy:Step 1: Inspect the XHR network section of the URL that you want to crawl and find out the request-response that gives you the data that you wantXHR section under Network — Product list API request and responseI can see from the Network that all product information is listed in this API called ‘List Product by Condition’. The response gives me all the data I need and it is a POST 2: Depending on the type of request(post or get) and also the request header & payload, simulate the request in your code and retrieve the data from API. #Define API urlurl_search=”#Define header for the post requestheaders={‘user-agent’:’Mozilla/5. 0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537. 36 (KHTML, like Gecko) Chrome/83. 0. 4103. 116 Safari/537. 36′}#Define payload for the request formdata={ “searchCondition”: {“categoryId”:0, “freeShippingType”:0, “filter: [], “keyWords”:”mask”}, “limit”:100, “offset”:0, “language”:”en”, “dataType”:”new”} (url_search, headers=headers, json=data)Here I create the HTTP POST request using the requests library. For post requests, you need to define the request header(setting of the request) and payload(data you are sending with this post request). Sometimes token or authentication is required here and you will need to request for token first before sending your POST request. Here there is no need to retrieve the token and usually just follow what’s in the request payload in Network and define ‘user-agent’ for the header. Another thing to note here is that inside the payload, I specified limit as 100 and offset as 0 because I found out it only allows me to query 100 data rows at one time. Thus, what we can do later is to use for loop to change offset and query more data 3: Extract out useful information that you need#read the data back as json ()# Store data into the fields for item in j[‘products’]: (item[‘price’]) (item[‘originCode’]) (item[‘name’]) (item[‘leftView’][‘rateScore’]) (item[‘rightView’][‘text’](‘ Sold’)[0]#Combine all the columns Frame({‘Name’:name, ‘price’:price, ‘location’:location, ‘Rating Score’:ratingScore, ‘Quantity Sold’:quantity})Data from API is usually quite neat and structured and thus what I did was just to read it in JSON format. After that, I extract the useful data into different columns and combine them together as output. You can see the data output face mask data outputStep 4: For API with a limit on query size, you will need to use ‘for loop’ to repeatedly retrieve all the data#Define API urlurl_search=”#Define header for the post requestheaders={‘user-agent’:’Mozilla/5. 36′}for i in range(0, 14000, 100): (max((3, 1), 2)) print(i) data={ “searchCondition”: {“categoryId”:0, “freeShippingType”:0, “filters”: [], “keyWords”:”mask”}, “limit”:100, “offset”:i, “language”:”en”, “dataType”:”new”} (url_search, headers=headers, json=data) () for item in j[‘products’]: (item[‘price’]) (item[‘originCode’]) (item[‘name’]) (item[‘leftView’][‘rateScore’]) (item[‘rightView’][‘text’](‘ Sold’)[0])#Combine all the columns Frame({‘Name’:name, ‘price’:price, ‘location’:location, ‘Rating Score’:ratingScore, ‘Quantity Sold’:quantity})Here is the complete code to scrape all rows of face mask data in Ezbuy. I found that the total number of rows is 14k and thus I write a for loop to loop through incremental offset number to query all the results. Another important thing to note here is that I put a random timeout at the start of each loop. This is because I do not want very frequent HTTP requests to harm the traffic of the website and get spotted out by the nally, RecommendationIf you want to scrape a website, I would suggest checking the existence of API first in the network section using inspect. If you can find the response to a request that gives you all the data you need, you can build a stable and neat solution. If you cannot find the data in-network, you should try using requests or Selenium to download HTML content and use Beautiful Soup to format the data. Lastly, please use a timeout to avoid a too frequent visits to the website or API. This may prevent you from being blocked by the website and it helps to alleviate the traffic for the good of the website.
A Beginner’s Guide to learn web scraping with python! – Edureka
Last updated on Sep 24, 2021 641. 9K Views Tech Enthusiast in Blockchain, Hadoop, Python, Cyber-Security, Ethical Hacking. Interested in anything… Tech Enthusiast in Blockchain, Hadoop, Python, Cyber-Security, Ethical Hacking. Interested in anything and everything about Computers. 1 / 2 Blog from Web Scraping Web Scraping with PythonImagine you have to pull a large amount of data from websites and you want to do it as quickly as possible. How would you do it without manually going to each website and getting the data? Well, “Web Scraping” is the answer. Web Scraping just makes this job easier and faster. In this article on Web Scraping with Python, you will learn about web scraping in brief and see how to extract data from a website with a demonstration. I will be covering the following topics: Why is Web Scraping Used? What Is Web Scraping? Is Web Scraping Legal? Why is Python Good For Web Scraping? How Do You Scrape Data From A Website? Libraries used for Web Scraping Web Scraping Example: Scraping Flipkart Website Why is Web Scraping Used? Web scraping is used to collect large information from websites. But why does someone have to collect such large data from websites? To know about this, let’s look at the applications of web scraping: Price Comparison: Services such as ParseHub use web scraping to collect data from online shopping websites and use it to compare the prices of products. Email address gathering: Many companies that use email as a medium for marketing, use web scraping to collect email ID and then send bulk emails. Social Media Scraping: Web scraping is used to collect data from Social Media websites such as Twitter to find out what’s trending. Research and Development: Web scraping is used to collect a large set of data (Statistics, General Information, Temperature, etc. ) from websites, which are analyzed and used to carry out Surveys or for R&D. Job listings: Details regarding job openings, interviews are collected from different websites and then listed in one place so that it is easily accessible to the is Web Scraping? Web scraping is an automated method used to extract large amounts of data from websites. The data on the websites are unstructured. Web scraping helps collect these unstructured data and store it in a structured form. There are different ways to scrape websites such as online Services, APIs or writing your own code. In this article, we’ll see how to implement web scraping with python. Is Web Scraping Legal? Talking about whether web scraping is legal or not, some websites allow web scraping and some don’t. To know whether a website allows web scraping or not, you can look at the website’s “” file. You can find this file by appending “/” to the URL that you want to scrape. For this example, I am scraping Flipkart website. So, to see the “” file, the URL is in-depth Knowledge of Python along with its Diverse Applications Why is Python Good for Web Scraping? Here is the list of features of Python which makes it more suitable for web scraping. Ease of Use: Python is simple to code. You do not have to add semi-colons “;” or curly-braces “{}” anywhere. This makes it less messy and easy to use. Large Collection of Libraries: Python has a huge collection of libraries such as Numpy, Matlplotlib, Pandas etc., which provides methods and services for various purposes. Hence, it is suitable for web scraping and for further manipulation of extracted data. Dynamically typed: In Python, you don’t have to define datatypes for variables, you can directly use the variables wherever required. This saves time and makes your job faster. Easily Understandable Syntax: Python syntax is easily understandable mainly because reading a Python code is very similar to reading a statement in English. It is expressive and easily readable, and the indentation used in Python also helps the user to differentiate between different scope/blocks in the code. Small code, large task: Web scraping is used to save time. But what’s the use if you spend more time writing the code? Well, you don’t have to. In Python, you can write small codes to do large tasks. Hence, you save time even while writing the code. Community: What if you get stuck while writing the code? You don’t have to worry. Python community has one of the biggest and most active communities, where you can seek help Do You Scrape Data From A Website? 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. To extract data using web scraping with python, you need to follow these basic steps: Find the URL that you want to scrape Inspecting the Page Find the data you want to extract Write the code Run the code and extract the data Store the data in the required format Now let us see how to extract data from the Flipkart website using Python, Deep Learning, NLP, Artificial Intelligence, Machine Learning with these AI and ML courses a PG Diploma certification program by NIT braries used for Web Scraping As we know, Python is has various applications and there are different libraries for different purposes. In our further demonstration, we will be using the following libraries: Selenium: Selenium is a web testing library. It is used to automate browser activities. BeautifulSoup: Beautiful Soup is a Python package for parsing HTML and XML documents. It creates parse trees that is helpful to extract the data easily. 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. Subscribe to our YouTube channel to get new updates..! Web Scraping Example: Scraping Flipkart WebsitePre-requisites: Python 2. x or Python 3. x with Selenium, BeautifulSoup, pandas libraries installed Google-chrome browser Ubuntu Operating SystemLet’s get started! Step 1: Find the URL that you want to scrapeFor this example, we are going scrape Flipkart website to extract the Price, Name, and Rating of Laptops. The URL for this page is 2: Inspecting the PageThe data is usually nested in tags. So, we inspect the page to see, under which tag the data we want to scrape is nested. To inspect the page, just right click on the element and click on “Inspect” you click on the “Inspect” tab, you will see a “Browser Inspector Box” 3: Find the data you want to extractLet’s extract the Price, Name, and Rating which is in the “div” tag respectively. Learn Python in 42 hours! Step 4: Write the codeFirst, let’s create a Python file. To do this, open the terminal in Ubuntu and type gedit
from BeautifulSoup import BeautifulSoup
import pandas as pdTo configure webdriver to use Chrome browser, we have to set the path to chromedriverdriver = (“/usr/lib/chromium-browser/chromedriver”)Refer the below code to open the URL: products=[] #List to store name of the product
prices=[] #List to store price of the product
ratings=[] #List to store rating of the product
(“)
Now that we have written the code to open the URL, it’s time to extract the data from the website. As mentioned earlier, the data we want to extract is nested in
soup = BeautifulSoup(content)
for a in ndAll(‘a’, href=True, attrs={‘class’:’_31qSD5′}):
(‘div’, attrs={‘class’:’_3wU53n’})
(‘div’, attrs={‘class’:’_1vC4OE _2rQ-NK’})
(‘div’, attrs={‘class’:’hGSR34 _2beYZw’})
()
Step 5: Run the code and extract the dataTo run the code, use the below command: python 6: Store the data in a required formatAfter extracting the data, you might want to store it in a format. This format varies depending on your requirement. For this example, we will store the extracted data in a CSV (Comma Separated Value) format. To do this, I will add the following lines to my code:df = Frame({‘Product Name’:products, ‘Price’:prices, ‘Rating’:ratings})
_csv(”, index=False, encoding=’utf-8′)Now, I’ll run the whole code again. A file name “” is created and this file contains the extracted data. I hope you guys enjoyed this article on “Web Scraping with Python”. I hope this blog was informative and has added value to your knowledge. Now go ahead and try Web Scraping. Experiment with different modules and applications of Python. If you wish to know about Web Scraping With Python on Windows platform, then the below video will help you understand how to do Scraping With Python | Python Tutorial | Web Scraping Tutorial | EdurekaThis Edureka live session on “WebScraping using Python” will help you understand the fundamentals of scraping along with a demo to scrape some details from a question regarding “web scraping with Python”? You can ask it on edureka! Forum and we will get back to you at the earliest or you can join our Python Training in Hobart get in-depth knowledge on Python Programming language along with its various applications, you can enroll here for live online Python training with 24/7 support and lifetime access.
A Beginner’s Guide to learn web scraping with python! – Edureka
Last updated on Sep 24, 2021 641. 9K Views Tech Enthusiast in Blockchain, Hadoop, Python, Cyber-Security, Ethical Hacking. Interested in anything… Tech Enthusiast in Blockchain, Hadoop, Python, Cyber-Security, Ethical Hacking. Interested in anything and everything about Computers. 1 / 2 Blog from Web Scraping Web Scraping with PythonImagine you have to pull a large amount of data from websites and you want to do it as quickly as possible. How would you do it without manually going to each website and getting the data? Well, “Web Scraping” is the answer. Web Scraping just makes this job easier and faster. In this article on Web Scraping with Python, you will learn about web scraping in brief and see how to extract data from a website with a demonstration. I will be covering the following topics: Why is Web Scraping Used? What Is Web Scraping? Is Web Scraping Legal? Why is Python Good For Web Scraping? How Do You Scrape Data From A Website? Libraries used for Web Scraping Web Scraping Example: Scraping Flipkart Website Why is Web Scraping Used? Web scraping is used to collect large information from websites. But why does someone have to collect such large data from websites? To know about this, let’s look at the applications of web scraping: Price Comparison: Services such as ParseHub use web scraping to collect data from online shopping websites and use it to compare the prices of products. Email address gathering: Many companies that use email as a medium for marketing, use web scraping to collect email ID and then send bulk emails. Social Media Scraping: Web scraping is used to collect data from Social Media websites such as Twitter to find out what’s trending. Research and Development: Web scraping is used to collect a large set of data (Statistics, General Information, Temperature, etc. ) from websites, which are analyzed and used to carry out Surveys or for R&D. Job listings: Details regarding job openings, interviews are collected from different websites and then listed in one place so that it is easily accessible to the is Web Scraping? Web scraping is an automated method used to extract large amounts of data from websites. The data on the websites are unstructured. Web scraping helps collect these unstructured data and store it in a structured form. There are different ways to scrape websites such as online Services, APIs or writing your own code. In this article, we’ll see how to implement web scraping with python. Is Web Scraping Legal? Talking about whether web scraping is legal or not, some websites allow web scraping and some don’t. To know whether a website allows web scraping or not, you can look at the website’s “” file. You can find this file by appending “/” to the URL that you want to scrape. For this example, I am scraping Flipkart website. So, to see the “” file, the URL is in-depth Knowledge of Python along with its Diverse Applications Why is Python Good for Web Scraping? Here is the list of features of Python which makes it more suitable for web scraping. Ease of Use: Python is simple to code. You do not have to add semi-colons “;” or curly-braces “{}” anywhere. This makes it less messy and easy to use. Large Collection of Libraries: Python has a huge collection of libraries such as Numpy, Matlplotlib, Pandas etc., which provides methods and services for various purposes. Hence, it is suitable for web scraping and for further manipulation of extracted data. Dynamically typed: In Python, you don’t have to define datatypes for variables, you can directly use the variables wherever required. This saves time and makes your job faster. Easily Understandable Syntax: Python syntax is easily understandable mainly because reading a Python code is very similar to reading a statement in English. It is expressive and easily readable, and the indentation used in Python also helps the user to differentiate between different scope/blocks in the code. Small code, large task: Web scraping is used to save time. But what’s the use if you spend more time writing the code? Well, you don’t have to. In Python, you can write small codes to do large tasks. Hence, you save time even while writing the code. Community: What if you get stuck while writing the code? You don’t have to worry. Python community has one of the biggest and most active communities, where you can seek help Do You Scrape Data From A Website? 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. To extract data using web scraping with python, you need to follow these basic steps: Find the URL that you want to scrape Inspecting the Page Find the data you want to extract Write the code Run the code and extract the data Store the data in the required format Now let us see how to extract data from the Flipkart website using Python, Deep Learning, NLP, Artificial Intelligence, Machine Learning with these AI and ML courses a PG Diploma certification program by NIT braries used for Web Scraping As we know, Python is has various applications and there are different libraries for different purposes. In our further demonstration, we will be using the following libraries: Selenium: Selenium is a web testing library. It is used to automate browser activities. BeautifulSoup: Beautiful Soup is a Python package for parsing HTML and XML documents. It creates parse trees that is helpful to extract the data easily. 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. Subscribe to our YouTube channel to get new updates..! Web Scraping Example: Scraping Flipkart WebsitePre-requisites: Python 2. x or Python 3. x with Selenium, BeautifulSoup, pandas libraries installed Google-chrome browser Ubuntu Operating SystemLet’s get started! Step 1: Find the URL that you want to scrapeFor this example, we are going scrape Flipkart website to extract the Price, Name, and Rating of Laptops. The URL for this page is 2: Inspecting the PageThe data is usually nested in tags. So, we inspect the page to see, under which tag the data we want to scrape is nested. To inspect the page, just right click on the element and click on “Inspect” you click on the “Inspect” tab, you will see a “Browser Inspector Box” 3: Find the data you want to extractLet’s extract the Price, Name, and Rating which is in the “div” tag respectively. Learn Python in 42 hours! Step 4: Write the codeFirst, let’s create a Python file. To do this, open the terminal in Ubuntu and type gedit
from BeautifulSoup import BeautifulSoup
import pandas as pdTo configure webdriver to use Chrome browser, we have to set the path to chromedriverdriver = (“/usr/lib/chromium-browser/chromedriver”)Refer the below code to open the URL: products=[] #List to store name of the product
prices=[] #List to store price of the product
ratings=[] #List to store rating of the product
(“)
Now that we have written the code to open the URL, it’s time to extract the data from the website. As mentioned earlier, the data we want to extract is nested in
soup = BeautifulSoup(content)
for a in ndAll(‘a’, href=True, attrs={‘class’:’_31qSD5′}):
(‘div’, attrs={‘class’:’_3wU53n’})
(‘div’, attrs={‘class’:’_1vC4OE _2rQ-NK’})
(‘div’, attrs={‘class’:’hGSR34 _2beYZw’})
()
Step 5: Run the code and extract the dataTo run the code, use the below command: python 6: Store the data in a required formatAfter extracting the data, you might want to store it in a format. This format varies depending on your requirement. For this example, we will store the extracted data in a CSV (Comma Separated Value) format. To do this, I will add the following lines to my code:df = Frame({‘Product Name’:products, ‘Price’:prices, ‘Rating’:ratings})
_csv(”, index=False, encoding=’utf-8′)Now, I’ll run the whole code again. A file name “” is created and this file contains the extracted data. I hope you guys enjoyed this article on “Web Scraping with Python”. I hope this blog was informative and has added value to your knowledge. Now go ahead and try Web Scraping. Experiment with different modules and applications of Python. If you wish to know about Web Scraping With Python on Windows platform, then the below video will help you understand how to do Scraping With Python | Python Tutorial | Web Scraping Tutorial | EdurekaThis Edureka live session on “WebScraping using Python” will help you understand the fundamentals of scraping along with a demo to scrape some details from a question regarding “web scraping with Python”? You can ask it on edureka! Forum and we will get back to you at the earliest or you can join our Python Training in Hobart get in-depth knowledge on Python Programming language along with its various applications, you can enroll here for live online Python training with 24/7 support and lifetime access.
Frequently Asked Questions about how to parse data from website
How do you parse information from a website?
How Do You Scrape Data From A Website?Find the URL that you want to scrape.Inspecting the Page.Find the data you want to extract.Write the code.Run the code and extract the data.Store the data in the required format.Sep 24, 2021
Is it legal to parse websites?
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.
Can we scrape data from any website?
Any website can be scraped There’s a bunch of ways to make a website scraping-proof. Although in reality, there’s no technical shield that could stop a full-fledged scraper from fetching data.Nov 17, 2017
