Web scraping has become a crucial aspect of data extraction, data mining, and data scraping in various industries. Scrapy, a Python-based web scraping framework, has emerged as a popular tool for web scraping. In this step-by-step tutorial, we will explore Scrapy and how it can be used for web scraping.

Table of Contents:

1. Introduction to Scrapy
2. Scrapy Architecture
3. Installation of Scrapy
4. Scrapy Spiders
5. XPath Selectors and CSS Selectors
6. HTML Parsing
7. Request and Response Objects
8. Data Extraction and Pipeline
9. Data Cleaning and Transformation
10. Data Analysis
11. Best Practices
12. Large Scale Scraping
13. E-commerce Scraping
14. Social Media Scraping
15. Price Monitoring
    

1. Introduction to Scrapy:
   Scrapy is an open-source Python web crawling framework used for building web scrapers that can scrape multiple websites. It is widely used in industries such as e-commerce, finance, healthcare, and others for data extraction, data mining, and data scraping. Scrapy allows you to extract data from websites efficiently and quickly.
2. Scrapy Architecture:
   Scrapy follows a specific architecture that comprises different components, such as the engine, scheduler, downloader, and spider. The engine controls the flow of data between components, the scheduler manages the requests and responses, the downloader downloads the web pages, and the spider crawls the web pages.
3. Installation of Scrapy:
   To install Scrapy, you need to have Python installed on your computer. Once you have Python, you can install Scrapy using pip, a package manager for Python. Open your command prompt and type the following command:

pip install scrapy
 

Scrapy Spiders:
A spider is a Scrapy component that defines how to crawl a website and extract data from it. Spiders are Python classes that define the start URLs and how to follow the links from the start URLs. Scrapy provides different types of spiders, such as BaseSpider, CrawlSpider, and SitemapSpider.
Scrapy Spiders are an essential component of the Scrapy framework, allowing developers to define how to crawl a website and extract data from it. Spiders are Python classes that define the start URLs and how to follow the links from the start URLs to scrape the desired data.

Scrapy offers different types of spiders, each with its unique characteristics and use cases. The BaseSpider is the simplest type of spider that defines how to make HTTP requests and parse responses. The CrawlSpider, on the other hand, is a more advanced spider that can follow links automatically and can handle rules to define how to extract data. Lastly, the SitemapSpider is a specialized spider designed to crawl sitemaps and extract data from them.

To create a spider in Scrapy, you need to define the start URLs and implement a method to parse the response from each URL. The parse() method is the default method for parsing responses in Scrapy. Within this method, you can use XPath or CSS selectors to extract data from the HTML response.

Spiders can also be customized further by adding additional methods to handle different types of responses, such as handling JSON or XML responses. Additionally, Scrapy allows you to define rules for following links and how to extract data from them, making it a powerful tool for web scraping.

In conclusion, Scrapy Spiders are an essential component of the Scrapy framework, allowing developers to define how to crawl a website and extract data from it. With its different types of spiders and customizable features, Scrapy provides a flexible and powerful tool for web scraping projects of any complexity.

4. XPath Selectors and CSS Selectors: Scrapy supports two types of selectors: XPath selectors and CSS selectors. XPath selectors allow you to select elements based on their location in the HTML structure, while CSS selectors select elements based on their attributes.

example for XPath Selectors and CSS Selectors:

XPath selectors and CSS selectors are two common methods used in Scrapy spiders to extract data from HTML documents.

An XPath selector is a string expression used to select elements from an XML or HTML document. It can be used to select elements by name, attribute, text content, and position in the document hierarchy. For example, the following XPath selector would select all links on a webpage:

 //a

This selector starts with a double forward slash, which means it will select all elements in the document that match the following criteria. In this case, the “a” after the double slash means it will select all “a” elements, which are links.

A CSS selector, on the other hand, uses a syntax similar to CSS to select elements from an HTML document. It can be used to select elements by name, class, ID, attribute, and position in the document hierarchy. For example, the following CSS selector would select all links on a webpage:

 This selector simply targets all "a" elements on the page.

Both XPath selectors and CSS selectors have their advantages and disadvantages, depending on the project’s specific requirements. XPath selectors tend to be more powerful and flexible, allowing for more complex selections, while CSS selectors are often faster and easier to write for simple selections.

In summary, XPath selectors and CSS selectors are both powerful tools for selecting elements in HTML documents and are commonly used in Scrapy spiders for web scraping.

Once you have selected the elements you want to scrape using XPath or CSS selectors, you can extract data from them using Scrapy’s built-in ItemLoaders.

ItemLoaders are used to define the fields you want to scrape and how to extract them from the selected elements. They provide a convenient way to define the data structure of your scraped items and handle data cleaning and transformation.

Here’s an example of how to use ItemLoaders in a Scrapy spider:

pythonCopy codefrom scrapy.loader import ItemLoader
from scrapy.loader.processors import TakeFirst, MapCompose

class QuoteLoader(ItemLoader):
    default_output_processor = TakeFirst()
    text_in = MapCompose(str.strip)

class QuoteItem(scrapy.Item):
    text = scrapy.Field()
    author = scrapy.Field()

class QuotesSpider(scrapy.Spider):
    name = "quotes"
    start_urls = [
        'http://quotes.toscrape.com/page/1/',
    ]

    def parse(self, response):
        for quote in response.xpath('//div[@class="quote"]'):
            loader = QuoteLoader(item=QuoteItem(), selector=quote)
            loader.add_xpath('text', './/span[@class="text"]/text()')
            loader.add_xpath('author', './/span/small/text()')
            yield loader.load_item()

In this example, we define a custom ItemLoader called QuoteLoader that defines how to extract the text and author fields from each scraped item. The default_output_processor attribute specifies that only the first value should be returned for each field, while the text_in attribute specifies that the text field should have any leading or trailing whitespace stripped.

In the parse method of the spider, we create a new QuoteLoader instance for each quote element on the page and use add_xpath to specify which fields to extract and the corresponding XPath selectors. Finally, we call loader.load_item() to load the extracted data into a new QuoteItem instance, which is yielded to the output pipeline.

By using ItemLoaders, you can easily define the structure of your scraped items and handle data cleaning and transformation in a modular and reusable way.

HTML Parsing:

HTML parsing is the process of extracting data from HTML documents. Scrapy provides different methods for parsing HTML documents, such as lxml and BeautifulSoup. example

For instance, if we want to extract the title and body of an article from a website, we can use the following code using BeautifulSoup:

 from scrapy.selector import Selector
from scrapy.http import HtmlResponse
import requests
from bs4 import BeautifulSoup

url = "https://example.com/article"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')

title = soup.find('h1', {'class': 'title'}).text.strip()
body = soup.find('div', {'class': 'body'}).text.strip()

This code sends a request to the website, and then uses BeautifulSoup to parse the HTML response and extract the title and body of the article using their respective HTML tags and classes.

Similarly, we can use XPath selectors with Scrapy to parse HTML documents and extract data from them. For example, the following code snippet extracts all the links from a webpage using XPath selectors:

 from scrapy.selector import Selector
from scrapy.http import HtmlResponse

url = "https://example.com"
response = requests.get(url)

sel = Selector(text=response.text)
links = sel.xpath('//a/@href').extract()

This code sends a request to the website, and then uses Scrapy’s Selector class to create an XPath selector object that extracts all the href attributes from the anchor tags on the page.

Request and Response Objects: Scrapy uses request and response objects to interact with websites. The request object is used to make a request to a website, while the response object is used to get the response from the website. and give some example:

In Scrapy, making a request to a website is done using the Request object. Here is an example of how to create a request object:

 import scrapy

class MySpider(scrapy.Spider):
    name = "myspider"
    start_urls = [
        "http://www.example.com"
    ]

    def parse(self, response):
        # parse the response
        pass

    def make_requests_from_url(self, url):
        return scrapy.Request(url=url, callback=self.parse)

In this example, the spider starts with a single URL http://www.example.com, and the parse method will be called to handle the response.

The Response object is used to handle the response from a website. Here is an example of how to use the Response object:

 import scrapy

class MySpider(scrapy.Spider):
    name = "myspider"
    start_urls = [
        "http://www.example.com"
    ]

    def parse(self, response):
        # extract data from the response
        title = response.xpath('//title/text()').get()
        yield {'title': title}

In this example, the parse method extracts the title of the website using an XPath selector and yields a dictionary with the title. This data can then be processed by a pipeline or stored in a database.

Here’s an example of using request and response objects in Scrapy:

pythonCopy codeimport scrapy

class MySpider(scrapy.Spider):
    name = "example"
    start_urls = [
        "http://www.example.com",
    ]

    def parse(self, response):
        # Extracting the page title using XPath selector
        title = response.xpath('//title/text()').get()
        print(title)

        # Extracting the page content using CSS selector
        content = response.css('div.content::text').getall()
        print(content)

In this example, we define a spider named “example” and provide it with a list of start URLs. The parse method is called automatically by Scrapy for each URL in the start_urls list.

Inside the parse method, we use the response object to extract information from the website. We use an XPath selector to extract the page title and a CSS selector to extract the page content. We then print these values to the console.

This is just a simple example, but Scrapy provides a wide range of features for making requests and processing responses.

we continute on next post later (p2)

Suggess course: