Hello Stack Overflow Community,

I have written a Scrapy spider to scrape product data from a website. The spider navigates through multiple pages to reach a specific product and extracts details such as the product name, price, color, size, and reviews. I would greatly appreciate it if you could review my code and provide feedback on the following aspects:

• Correct usage of Scrapy framework features.
• Adherence to best practices in web scraping.
• Code efficiency and readability.
• Error handling and robustness.
• Overall structure and design.

Here is my code:

import scrapy
import json

class MarksAndSpencerSpider(scrapy.Spider):
    name = "marksandspencer"
    allowed_domains = ["marksandspencer.com"]
    start_urls = ["https://www.marksandspencer.com"]

    def parse(self, response):
        option = response.css('#country-selector-country option[value="BG"]')
        if option:
            value = option.css('::attr(value)').get().lower()
            bg_url = f"https://www.marksandspencer.com/{value}"
            self.logger.info(f"BG URL found: {bg_url}")
            yield response.follow(bg_url, callback=self.parse_bg_page)
        else:
            self.logger.warning("BG option not found on the homepage.")

    def parse_bg_page(self, response):
        men_link = response.css('.nav-item.dropdown.order-lg-3 .subcategory a::attr(href)').get()
        if men_link:
            self.logger.info(f"Men's link found: {men_link}")
            yield response.follow(men_link, callback=self.parse_mens_page)
        else:
            self.logger.warning("Men's section link not found on the BG page.")

    def parse_mens_page(self, response):
        casual_shirts_link = response.xpath('//a[contains(text(), "Casual shirts")]/@href').get()
        if casual_shirts_link:
            self.logger.info(f"Casual shirts link found: {casual_shirts_link}")
            yield response.follow(casual_shirts_link, callback=self.parse_casual_shirts)
        else:
            self.logger.warning("Casual shirts link not found on the Men's page.")

    def parse_casual_shirts(self, response):
        product_link = response.css('div.pdp-link a:contains("Easy Iron Geometric Print Shirt")::attr(href)').get()
        if product_link:
            self.logger.info(f"Product link found: {product_link}")
            yield response.follow(product_link, callback=self.parse_product_page)
        else:
            self.logger.warning("Product link not found in the Casual Shirts section.")

    def parse_product_page(self, response):
        product_name = response.css('.product-name::text').get()
        price = response.css('.value::text').get()
        selected_colour = response.css('button.qa-addtocart-button')
        size = response.css('#plp-select').get()
        reviews_script = response.xpath('(//script[@type="application/ld+json"])[2]/text()').get()

        if reviews_script:
            json_data = json.loads(reviews_script)
            review_count = json_data.get('AggregateRating', {}).get('reviewCount', 0)
            avg_rating = json_data.get('AggregateRating', {}).get('ratingValue', 0.0)
        else:
            review_count = 0
            avg_rating = 0.0

        color = ''
        for button in selected_colour:
            color = button.attrib.get('data-defaultcolor', '')

        sizes = []
        if size:
            select_element = scrapy.Selector(text=size, type="html")
            option_tags = select_element.css('option')
            for option in option_tags:
                option_text = option.xpath('normalize-space(text())').get()
                if option_text != "Select Size":
                    sizes.append(option_text)

        product_data = {
            "name": product_name or '',
            "price": price or '',
            "colour": color,
            "size": sizes,
            "reviews_count": int(review_count),
            "reviews_score": float(avg_rating),
        }

        yield product_data

I am eager to learn and improve, so any suggestions or advice you can provide will be very helpful. Thank you in advance for your time and expertise!