A Beginner’s Guide to learn web scraping with python!

Let’s say you need to scrape a lot of information from the web, and time is of the essence. What alternative would there be to manually accessing each website and retrieving the information? “Web Scraping” is the technique that can be used. Web scraping merely facilitates and accelerates the process.

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. 

Why is Web Scraping Used?

Web scraping is used to collect large information from websites. You can also find more in-depth concepts about Web Scraping on Edureka’s Python course. 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 user.
• Data Collection: To gather large amounts of well-structured data.
• Market Research: Following competitors, information on goods, sentiment analysis.
• Lead Generation: It extracts contact details for sales and marketing.
• Content Aggregation: It aggregates articles and news from multiple sources.
• Price Monitoring: Compare prices across different websites automatically.
• Machine Learning: Train models on large datasets scraped from the web.
• Real Estate and Travel: Retrie Web scraping serves quick gathering of data on websites for a variety of reasons, including the following cases listing information, prices, and offers.
• Government and Public Data: Scrape public information such as weather forecasts and demographic data.

What is Web Scraping?

Web scraping is one of the automated processes for gathering extensive information from the World Wide Web. The information found on the websites is disorganized. In order to store this data in a more organized fashion, web scraping is a useful tool. Online services, application programming interfaces (APIs), and custom code are just some of the options for scraping websites. This article will show how to use Python to perform web scraping.

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 “robots.txt” file. You can find this file by appending “/robots.txt” to the URL that you want to scrape. For this example, I am scraping Flipkart website. So, to see the “robots.txt” file, the URL is www.flipkart.com/robots.txt.

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 programming 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 from.

How 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:

1. Find the URL that you want to scrape
2. Inspecting the Page
3. Find the data you want to extract
4. Write the code
5. Run the code and extract the data
6. Store the data in the required format 

Now let us see how to extract data from the Flipkart website using Python.

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Libraries 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. 
• Scrapy: This is a framework, hence, particularly developed for web crawling and scraping tasks with the aim to extract structured data from varied websites, and handling their requests efficaciously.
• Selenium: this library is particularly adapted to web scraping dynamic content in a website created by JavaScript. It’s very good at replicating user interaction with web browsers; hence it often finds itself at hand during scraping.
• LXML: This is a high-performance library for the parsing of XML and HTML documents in particular. It often finds linkage with Requests or other libraries for parsing and navigating of HTML efficiently.
• MechanicalSoup: This library provides a programmatic way of accessing websites that provide an interface to submit forms and follow links. Useful when scraping sites that involve login processes in particular.
• PyQuery: Run jQuery queries, which operate on XML docs. It offers near JQuery syntax for manipulating HTML documents.

How can you scrape data from websites with Python?

To use python in scraping data from a webpage, Follow these steps:

Step one: Selecting the webpage and its URL.

Determine which website you are going to take your content from. In our case, we are going to pull the highest rated film list from IMDb at https://www.imdb.com/.

Step two: Inspecting the website

View the structure of the webpage. This can be done by inspection using the “Inspect” option when right-clicking on the page. Note the class names and IDs of elements which look interesting.

Third Step: Install Required Libraries 

The following commands install the libraries needed to perform web-scraping:

“`bash

pip install requests beautifulsoup4 pandas

“`

Fourth step: Write the Python Code**

Now write the following Python code to extract the data:

import requests
from bs4 import BeautifulSoup
import pandas as pd
Not a scrap, but enabling for scraping websites in Python as of October 2022: import time

# Website URL to scrape
url = "https://www.imdb.com/chart/top"
# Send GET request to the website
response = requests.get(url)
# Parse the HTML code using BeautifulSoup
soup = BeautifulSoup(response.content, 'html.parser')
# Extract relevant data from the scraped HTML code
movies = []
for row in soup.select('tbody.lister-list tr'):
title = row.find('td', class_='titleColumn').find('a').get_text()
year = row.find('td', class_='titleColumn').find('span', class_='secondaryInfo').get_text()[1:-1]
rating = row.find('td', class_='ratingColumn imdbRating').find('strong').get_text()
movies.append([title, year, rating])
# Store the info in a pandas dataframe
df = pd.DataFrame(movies, columns=['Title', 'Year', 'Rating'])
# Add delay between requests to not flood the website
time.sleep(1)
# Export the data into a CSV file
df.to_csv('top-rated-movies.csv', index=False)

Fifth Step : Export the Extracted Data**

Export this scraped data as a CSV file using pandas.

Sixth step : Check the Extracted Data**

Now, open the CSV file to check that the data has been scraped and stored correctly.

This code can be easily adjusted to suit any website and data that a user wants to extract.

Web Scraping Example : Scraping Flipkart Website

Pre-requisites:

• Python 2.x or Python 3.x with Selenium, BeautifulSoup, pandas libraries installed
• Google-chrome browser
• Ubuntu Operating System

Let’s get started!

Step 1: Find the URL that you want to scrape

For this example, we are going scrape Flipkart website to extract the Price, Name, and Rating of Laptops. The URL for this page is https://www.flipkart.com/laptops/~buyback-guarantee-on-laptops-/pr?sid=6bo%2Cb5g&uniqBStoreParam1=val1&wid=11.productCard.PMU_V2.

Step 2: Inspecting the Page

The 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”.

When you click on the “Inspect” tab, you will see a “Browser Inspector Box” open.

Step 3: Find the data you want to extract

Let’s extract the Price, Name, and Rating which is in the “div” tag respectively.

Step 4: Write the code

First, let’s create a Python file. To do this, open the terminal in Ubuntu and type gedit <your file name> with .py extension.

I am going to name my file “web-s”. Here’s the command:

gedit web-s.py

Now, let’s write our code in this file. 

First, let us import all the necessary libraries:

from selenium import webdriver
from BeautifulSoup import BeautifulSoup
import pandas as pd

To configure webdriver to use Chrome browser, we have to set the path to chromedriver

driver = webdriver.Chrome("/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
driver.get("https://www.flipkart.com/laptops/~buyback-guarantee-on-laptops-/pr?sid=6bo%2Cb5guniq")

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 <div> tags. So, I will find the div tags with those respective class-names, extract the data and store the data in a variable. Refer the code below:

content = driver.page_source
soup = BeautifulSoup(content)
for a in soup.findAll('a',href=True, attrs={'class':'_31qSD5'}):
name=a.find('div', attrs={'class':'_3wU53n'})
price=a.find('div', attrs={'class':'_1vC4OE _2rQ-NK'})
rating=a.find('div', attrs={'class':'hGSR34 _2beYZw'})
products.append(name.text)
prices.append(price.text)
ratings.append(rating.text) 

Step 5: Run the code and extract the data

To run the code, use the below command:

python web-s.py

Step 6: Store the data in a required format

After 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 = pd.DataFrame({'Product Name':products,'Price':prices,'Rating':ratings}) 
df.to_csv('products.csv', index=False, encoding='utf-8')

Now, I’ll run the whole code again.

A file name “products.csv” is created and this file contains the extracted data.

How to parse text from the website?

You can easily parse text from a website using Beautiful Soup or lxml. Here are the steps involved with the code.

• We will send an HTTP request to the URL and obtain the HTML content of the webpage.
• Then, with the HTML structure at hand, we will find() (using Beautiful Soup), searching for a tag or attribute.
• Finally, extract text content using the text attribute itself:.

The following is used to scrape text from a website using BeautifulSoup:.

import requests
from bs4 import BeautifulSoup
# Send an HTTP request to the URL of the webpage you want to access
response = requests.get("https://www.imdb.com")
# Parse the HTML content using BeautifulSoup
soup = BeautifulSoup(response.content, "html.parser")
# Extract the text content of the webpage
text = soup.get_text()
print(text)

Thats all you have to do to parse the text from website

How can one scrape HTML forms using Python?

You will most likely use a Python library, such as BeautifulSoup, lxml, or even mechanize, for scraping HTML forms. Here are the typical steps: 

Send an HTTP request to the URL of the webpage containing the form you want to scrape. The request is responded to by the server returning the HTML content of the webpage.

After getting the HTML content, you will further leverage an HTML parser that assists in finding a form you want to scrape. You are, for example, going to be in a position to use the discover() method from BeautifulSoup to find the form tag.

Now, locate the form. Extracting the input fields along with their values will be using the HTML parser. For example, you’ll get through find_all() function from BeautifulSoup all the input labels located in the form then extract title and value attributes.

You later use that info when returning a frame or further processing information.

import requests
from bs4 import BeautifulSoup
def get_imdb_movie_info(movie_title):
base_url = "https://www.imdb.com"
search_url = f"{base_url}/find?q={movie_title}&s=tt"
response = requests.get(search_url)
soup = BeautifulSoup(response.content, 'html.parser')
result = soup.find(class_='findList').find('td', class_='result_text').find('a')
if not result:
return None
movie_path = result['href']
movie_url = f"{base_url}{movie_path}"
movie_response = requests.get(movie_url)
movie_soup = BeautifulSoup(movie_response.content, 'html.parser')
title = movie_soup.find(class_='title_wrapper').h1.text.strip()
rating = movie_soup.find(itemprop='ratingValue').text.strip()
summary = movie_soup.find(class_='summary_text').text.strip()
print(f"Title: {title}")
print(f"Rating: {rating}")
print(f"Summary: {summary}")
movie_title = input("Enter a movie title: ")
get_imdb_movie_info(movie_title)

Scrape and Parse Text From Websites

To scrape and parse text from websites in Python, you can use the requests library to fetch the HTML content of the website and then use a parsing library like BeautifulSoup or lxml to extract the relevant text from the HTML. Here’s a step-by-step guide:

Step 1: Import necessary modules

import requests 
from bs4 import BeautifulSoup
import re

Step 2: Fetch the HTML content of the website using `requests`

url = 'https://example.com'; # Replace this with the URL of the website you want to scrape
response = requests.get(url)
# Check if the request was successful
if response.status_code == 200:
html_content = response.content
else:
 print("Failed to fetch the website.")
exit()

Step 3: Parse the HTML content using `BeautifulSoup`

# Parse the HTML content with BeautifulSoup
soup = BeautifulSoup(html_content, 'html.parser')

 
Step 4: Extract the text from the parsed HTML using string methods

# Find all the text elements (e.g., paragraphs, headings, etc.) you want to scrape
text_elements = soup.find_all(['p', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'span'])
# Extract the text from each element and concatenate them into a single string
scraped_text = ' '.join(element.get_text() for element in text_elements)
print(scraped_text)

Step 5: Extract text from HTML using regular expressions

</pre>
<span> </span></pre>
<span style="font-weight: 400;">Note: The regular expression in Step 5 is a simple pattern that matches any HTML tag and removes them from the HTML content. In real-world scenarios, you may need more complex regular expressions depending on the structure of the HTML.</span>

<strong>Check Your Understanding:</strong>

<span style="font-weight: 400;">Now that you have built your web scraper, you can use either the string method approach or the regular expression approach to extract text from websites. Remember to use web scraping responsibly and adhere to website policies and legal restrictions. Always review the website's terms of service and robots.txt file before scraping any website. Additionally, excessive or unauthorized scraping may put a strain on the website's server and is generally considered unethical.</span>
<h2>Use an HTML Parser for Web Scraping in Python</h2>
<span style="font-weight: 400;">Here are the steps to use an HTML parser like Beautiful Soup for web scraping in Python:</span>

<b>Step 1: Install Beautiful Soup</b>

<span style="font-weight: 400;">Make sure you have the Beautiful Soup library installed. If not, you can install it using `pip`:</span>
[python]
bash
pip install beautifulsoup4

Step 2: Create a BeautifulSoup Object

Import the necessary modules and create a BeautifulSoup object to parse the HTML content of the website.

from bs4 import BeautifulSoup
import requests

Step 3: Use a BeautifulSoup Object

Fetch the HTML content of the website using the `requests` library, and then create a BeautifulSoup object to parse the HTML content.

[Python]
url = ‘https://example.com’
# Replace this with the URL of the website you want to scrape
response = requests.get(url)
# Check if the request was successful
if response.status_code == 200:
html_content = response.content
else:
print(“Failed to fetch the website.”)
exit()
# Create a BeautifulSoup object to parse the HTML content
soup = BeautifulSoup(html_content, ‘html.parser’)
[/python]

Step 4: Check Your Understanding

Now that you have a BeautifulSoup object (`soup`), you can use its various methods to extract specific data from the HTML. For example, you can use `soup.find()` to find the first occurrence of a specific HTML element, `soup.find_all()` to find all occurrences of an element, and `soup.select()` to use CSS selectors to extract elements.

Here’s an example of how to use `soup.find()` to extract the text of the first paragraph (`<p>`) tag:

# Find the first paragraph tag and extract its text
first_paragraph = soup.find('p').get_text()
print(first_paragraph)

You can explore more methods available in the BeautifulSoup library to extract data from the HTML content as needed for your web scraping task.

Remember to use web scraping responsibly, adhere to website policies and legal restrictions, and review the website’s terms of service and robots.txt file before scraping any website. Additionally, excessive or unauthorized scraping may put a strain on the website’s server and is generally considered unethical.

Interact With HTML Forms

Certainly! Here are the steps to interact with HTML forms using MechanicalSoup in Python:

 Step 1: Install MechanicalSoup

Ensure you have the MechanicalSoup library installed. If not, you can install it using `pip`:

pip install MechanicalSoup

 Step 2: Create a Browser Object

Import the necessary modules and create a MechanicalSoup browser object to interact with the website.

 import mechanicalsoup 

  Step 3: Submit a Form with MechanicalSoup

Create a browser object and use it to submit a form on a specific webpage.

# Create a MechanicalSoup browser object
browser = mechanicalsoup.StatefulBrowser()
# Navigate to the webpage with the form
url = 'https://example.com/form-page'
# Replace this with the URL of the webpage with the form
browser.open(url)
# Fill in the form fields
form = browser.select_form()
# Select the form on the webpage
form['username'] = 'your_username';
# Replace 'username' with the name attribute of the username input field
form['password'] = 'your_password'; 
# Replace 'password' with the name attribute of the password input field
# Submit the form
browser.submit_selected()

Step 4: Check Your Understanding

In this example, we used MechanicalSoup to create a browser object (`browser`) and navigate to a webpage with a form. We then selected the form using `browser.select_form()`, filled in the username and password fields using `form[‘username’]` and `form[‘password’]`, and finally submitted the form using `browser.submit_selected()`.

 With these steps, you can interact with HTML forms programmatically. MechanicalSoup is a powerful tool for automating form submissions, web scraping, and interacting with websites that have forms.

Remember to use web scraping and form submission responsibly, adhere to website policies and legal restrictions, and review the website’s terms of service before interacting with its forms. Additionally, make sure that the website allows automated interactions and that you are not violating any usage policies. Unauthorized and excessive form submissions can cause strain on the website’s server and may be considered unethical.

How To Scrape Websites in Real Time

You will be able to apply libraries in Python like ‘request’ to get the web pages, and then use modules likes Beautiful Soup or lxml for parsing HTML content. Here is the step-by-step procedure to scrape this online website in real-time:

Step 1:

Install Required Libraries

Make sure you have got the fundamental libraries installed an imported. You’ll introduce them utilizing pip in the event that you haven’t as of now:

Step 2:

Type in the Scraping Code

This is a fundamental case of how to scrape an online site in genuine time utilizing Python:

Step 3:

import requests
from bs4 import BeautifulSoup
def get_imdb_movie_info(movie_title):
base_url = "https://www.imdb.com"
search_url = f"{base_url}/find?q={movie_title}&s=tt"
response = requests.get(search_url)
soup = BeautifulSoup(response.content, 'html.parser')
result = soup.find(class_='findList').find('td', class_='result_text').find('a')
if not result:
return None
movie_path = result['href']
movie_url = f"{base_url}{movie_path}"
movie_response = requests.get(movie_url)
movie_soup = BeautifulSoup(movie_response.content, 'html.parser')
title = movie_soup.find(class_='title_wrapper').h1.text.strip()
rating = movie_soup.find(itemprop='ratingValue').text.strip()
summary = movie_soup.find(class_='summary_text').text.strip()
return {
'Title': title,
'Rating': rating,
'Summary': summary
}
# Example usage
movie_title = input("Enter a movie title: ")
movie_info = get_imdb_movie_info(movie_title)
if movie_info:
print(f"Title: {movie_info['Title']}")
print(f"Rating: {movie_info['Rating']}")
print(f"Summary: {movie_info['Summary']}")
else:
print("Movie not found on IMDb.")
Get it and Change Code

Beautiful Soup strategies like find or find_all can be used to search for specific elements within the page HTML structure.

Error Handling:

Then make your scrape more resilient by making sure it handles probable mistakes, like association problems or unexpected HTML structure.

The example also specifically covers the IMDb site for which these inputs by the user  are used to extract data directly from IMDb. Setup—Algorithm for parsing—based on an existing structure of the IMDb website, the Beautiful Soup selectors.

Interact With Websites in Real Time

Interacting with websites in real-time typically involves performing actions on a webpage and receiving immediate feedback or responses without requiring a full page reload. There are several methods to achieve real-time interactions with websites, depending on the use case and technologies involved. Here are some common approaches:

1. JavaScript and AJAX: JavaScript is a powerful client-side scripting language that allows you to manipulate the DOM (Document Object Model) of a webpage. AJAX (Asynchronous JavaScript and XML) enables you to make asynchronous HTTP requests to the server without reloading the entire page. With JavaScript and AJAX, you can perform actions like submitting forms, updating content, and fetching data from the server in real-time.
2. WebSockets: WebSockets provide full-duplex communication channels over a single TCP connection, enabling real-time, bidirectional communication between a client and a server. WebSockets are ideal for applications that require continuous data streams or real-time updates, such as chat applications, live notifications, and collaborative platforms.
3. Server-Sent Events (SSE): SSE is a standard that enables a server to send real-time updates to a client over an HTTP connection. Unlike WebSockets, SSE is unidirectional (server to client), making it suitable for scenarios where the client only needs to receive updates from the server without sending data back.
4. WebRTC: Web Real-Time Communication (WebRTC) is a technology that allows peer-to-peer communication between browsers. It is commonly used for video conferencing, audio calls, and other real-time media interactions directly between users.
5. Push Notifications: Push notifications are messages sent from a server to a client’s device, notifying them of new events or updates. They are commonly used on mobile devices and web browsers to deliver real-time alerts or updates to users, even when the application is not open.
6. Single Page Applications (SPAs): SPAs are web applications that load a single HTML page and dynamically update the content as the user interacts with the page. SPAs use JavaScript frameworks like React, Angular, or Vue.js to manage state and handle real-time updates efficiently.

Overall, the choice of the approach for real-time interactions with websites depends on the specific requirements and technologies involved. JavaScript, AJAX, WebSockets, SSE, WebRTC, and push notifications are some of the common technologies used to enable real-time communication and interactivity on modern web applications.

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Comparing all Python web scraping libraries

Lets understand this with help of pro-con of each library , then you can decide for yourself which one is your preference.

Beautiful Soup

pros:

• Excellent for parsing HTML and XML documents.
• Provides simple, API for navigating and manipulating the parsed data.
• Works in conjunction with libraries like Request.

cons:

• Requires knowledge of HTML and CSS for efficient use.
• Requires additional libraries like Selenium when its required to scrape javascript-rendered sites.

Scrapy

• pros:
  • Scrapy is a framework designed specifically for web scraping.
  • It can handles large-scale scraping tasks efficiently because it also support parallel request handling which is an in-built tool.
  • Includes features like automatic throttling and also use user agent rotation  to avoid detection.
• Cons:
  • It can be rather not easy to learn for beginners due to its comprehensive features.
  • Requires basic  knowledge of XPath and CSS selectors for effective scraping.

Requests-HTML

• Pros:

• • • The process of making HTTP requests and parsing HTML is simpler.
    • Offers jQuery-like methods for selecting and manipulating elements.
    • Good for quick and simple scraping tasks where jQuery familiarity is beneficial.

• Cons:

• • Less feature-rich compared to other libraries like Scrapy or Beautiful Soup.
  • Relatively new compared to more established libraries, so community support might be less extensive.

 LXML

• Pros:

• • • Fast and efficient for parsing XML and HTML documents.
    • Supports XPath and CSS selectors for querying elements.
    • Includes features for handling broken HTML documents and encoding issues.

• Cons:

• • Can be complex to set up and use compared to higher-level libraries like Beautiful Soup.
  • Might not be as beginner-friendly due to its focus on performance and flexibility.

PyQuery

• Pros:

• • • Provides a jQuery-like interface for parsing HTML documents.
    • Suitable for developers familiar with jQuery who want to leverage similar syntax for scraping.

• Cons:

• • Less popular and maintained compared to other libraries like Beautiful Soup or LXML.
  • Limited community support and documentation compared to more widely used libraries.

Selenium

• Pros:

• • • Automates web browsers to scrape data from dynamically rendered pages (e.g., JavaScript-driven content).
    • Useful for scraping websites that require interaction, like clicking buttons or filling forms.
  • Cons:
  • Slower compared to libraries like Beautiful Soup for static content since it requires browser automation.
  • Resource-intensive due to browser instances being launched.

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. 

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