What data can be extracted from MarksandSpencer.com using Python?

Jacinda Thilini · 2024-12-21T11:57:36+00:00

Scraping data from MarksandSpencer.com using Python allows you to gather details such as product names, prices, and availability across categories like clothing, food, and homeware. Marks & Spencer is a leading retailer in the UK, offering a wide range of products that make it an excellent source for market research. Using Python, you can automate the process of collecting this data efficiently. The first step involves inspecting the HTML structure of the product pages to identify relevant elements such as product titles and price tags. By using Python's libraries to send HTTP requests and parse HTML, you can build a robust scraper.One challenge when scraping Marks & Spencer is handling multiple categories and subcategories, as the website is vast. Automating navigation across categories ensures that the scraper captures a comprehensive dataset. Additionally, tracking customer reviews and ratings alongside product data adds depth to the analysis. Below is an example Python script for scraping product data from MarksandSpencer.com.import requests from html.parser import HTMLParserclass MarksAndSpencerParser(HTMLParser): def __init__(self): super().__init__() self.in_product_name False self.in_product_price False self.products self.current_product {} def handle_starttag(self, tag, attrs): attrs dict(attrs) if tag "h2" and "class" in attrs and "product-title" in attrs: self.in_product_name True if tag "span" and "class" in attrs and "price" in attrs: self.in_product_price True def handle_endtag(self, tag): if self.in_product_name and tag "h2": self.in_product_name False if self.in_product_price and tag "span": self.in_product_price False def handle_data(self, data): if self.in_product_name: self.current_product data.strip() if self.in_product_price: self.current_product data.strip() self.products.append(self.current_product) self.current_product {}url "https://www.marksandspencer.com/"response requests.get(url)parser MarksAndSpencerParser()parser.feed(response.text)for product in parser.products: print(f"Product: {product}, Price: {product}")This script extracts product names and prices from MarksandSpencer.com. By adding functionality for tracking stock levels or analyzing reviews, the scraper can provide more comprehensive insights. Introducing random delays between requests ensures smoother operations and reduces detection risks.

General Web Scraping

What data can be extracted from MarksandSpencer.com using Python?

Posted by Jacinda Thilini on 12/21/2024 at 11:57 am
Scraping data from MarksandSpencer.com using Python allows you to gather details such as product names, prices, and availability across categories like clothing, food, and homeware. Marks & Spencer is a leading retailer in the UK, offering a wide range of products that make it an excellent source for market research. Using Python, you can automate the process of collecting this data efficiently. The first step involves inspecting the HTML structure of the product pages to identify relevant elements such as product titles and price tags. By using Python’s libraries to send HTTP requests and parse HTML, you can build a robust scraper.
One challenge when scraping Marks & Spencer is handling multiple categories and subcategories, as the website is vast. Automating navigation across categories ensures that the scraper captures a comprehensive dataset. Additionally, tracking customer reviews and ratings alongside product data adds depth to the analysis. Below is an example Python script for scraping product data from MarksandSpencer.com.
```
import requests
from html.parser import HTMLParser
class MarksAndSpencerParser(HTMLParser):
    def __init__(self):
        super().__init__()
        self.in_product_name = False
        self.in_product_price = False
        self.products = []
        self.current_product = {}
    def handle_starttag(self, tag, attrs):
        attrs = dict(attrs)
        if tag == "h2" and "class" in attrs and "product-title" in attrs["class"]:
            self.in_product_name = True
        if tag == "span" and "class" in attrs and "price" in attrs["class"]:
            self.in_product_price = True
    def handle_endtag(self, tag):
        if self.in_product_name and tag == "h2":
            self.in_product_name = False
        if self.in_product_price and tag == "span":
            self.in_product_price = False
    def handle_data(self, data):
        if self.in_product_name:
            self.current_product["name"] = data.strip()
        if self.in_product_price:
            self.current_product["price"] = data.strip()
            self.products.append(self.current_product)
            self.current_product = {}
url = "https://www.marksandspencer.com/"
response = requests.get(url)
parser = MarksAndSpencerParser()
parser.feed(response.text)
for product in parser.products:
    print(f"Product: {product['name']}, Price: {product['price']}")
```
This script extracts product names and prices from MarksandSpencer.com. By adding functionality for tracking stock levels or analyzing reviews, the scraper can provide more comprehensive insights. Introducing random delays between requests ensures smoother operations and reduces detection risks.
Jacinda Thilini replied 5 hours, 9 minutes ago 1 Member · 0 Replies
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