Creating a Web Crawler with Python and Scrapy

Web crawlers are powerful tools used by developers, researchers and data analysts to extract and analyze data from websites. In this tutorial, we will explore how to create a web crawler using Python and Scrapy, a popular web crawling framework. By the end of this tutorial, you will have a basic understanding of how to build and run a web crawler, as well as how to extract and store data from websites. Whether you are a beginner or an experienced developer, this guide will provide you with the necessary skills to start building your own web crawler.

Introduction to Web Crawling

Web crawling is a vital process in the world of data extraction and analysis. By systematically navigating and collecting data from web pages, web crawlers automate the tedious task of manually gathering information. Applications of web crawling include search engine indexing, data mining and sentiment analysis, among others.

Python is an excellent language for web crawling, thanks to its readability, simplicity and extensive library support. One such library is Scrapy, which provides an integrated framework for web crawling and data extraction. Scrapy’s features include a powerful selector engine, built-in support for various data formats and storage backends and an extensible architecture.

Setting Up the Environment

Before diving into Scrapy, ensure that you have Python installed on your machine. If you don’t have Python, download it from the official Python website.

Once Python is installed, you can install Scrapy using pip, Python’s package manager. Open a terminal and run the following command:

pip install Scrapy

This command installs Scrapy and its dependencies. After the installation is complete, you are ready to create your first Scrapy project.

Creating a Scrapy Project

To create a new Scrapy project, open a terminal and navigate to the directory where you want to create the project. Run the following command, replacing myproject with the desired project name:

scrapy startproject myproject

This command generates a new directory named myproject, containing the necessary files and directories for a Scrapy project. The generated directory structure looks like this:


Each file and directory in the project has a specific purpose:

  • scrapy.cfg: The project configuration file.
  • myproject/: The Python package containing the project’s code.
  • Defines the data structures for storing the scraped data.
  • Contains custom middlewares for the Scrapy engine.
  • Defines the data processing pipelines.
  • Contains project-wide settings.
  • spiders/: Holds the web crawlers (spiders) that will perform the data extraction.

Defining Items

Items in Scrapy are container classes that define the structure of the data you want to scrape. They are similar to Python dictionaries, but they provide a more structured way to define and manipulate the scraped data.

To define an item, open the file in the myproject directory and create a new class that inherits from scrapy.Item. For example, if you want to scrape information about books, you might define a Book item as follows:

import scrapy

class Book(scrapy.Item):
    title = scrapy.Field()
    author = scrapy.Field()
    price = scrapy.Field()
    rating = scrapy.Field()

This class defines a Book an item with four fields: title, author, price and rating. You can add or remove fields according to your specific requirements.

Building a Spider

A spider in Scrapy is a class that defines how a website should be crawled and how data should be extracted from its pages. To create a spider, navigate to the spiders directory in your project and create a new Python file, e.g.,

Inside this file, start by importing the necessary modules and define a new class that inherits from scrapy.Spider. Set the spider’s name attribute and define its start_urls list, containing the URLs the spider will start crawling. Then, implement the parse method, which will be called for each URL in the start_urls list. The parse method is responsible for extracting the data and yielding items or additional requests.

Here’s an example of a simple spider that extracts book information from a fictional website, “”:

import scrapy
from myproject.items import Book

class BookSpider(scrapy.Spider):
    name = 'book_spider'
    start_urls = ['']

    def parse(self, response):
        for book in response.css(''):
            item = Book()
            item['title'] = book.css('h2.title::text').get()
            item['author'] = book.css('').get()
            item['price'] = book.css('span.price::text').get()
            item['rating'] = book.css('span.rating::text').get()
            yield item

            next_page = response.css('a.next_page::attr(href)').get()
            if next_page is not None:
                yield response.follow(next_page, callback=self.parse)

This spider begins crawling at “” and extracts book information using CSS selectors. If there is a link to the next page, it follows the link and continues extracting data.

Extracting Data with Selectors

Scrapy supports two types of selectors for extracting data from a web page: CSS selectors and XPath selectors. In the previous spider example, we used CSS selectors to extract data.

Here’s the same example using XPath selectors:

import scrapy
from myproject.items import Book
class BookSpider(scrapy.Spider):
    name = 'book_spider'
    start_urls = ['']
    def parse(self, response):
        for book in response.xpath('//div[@class="book"]'):
            item = Book()
            item['title'] = book.xpath('./h2[@class="title"]/text()').get()
            item['author'] = book.xpath('./span[@class="author"]/text()').get()
            item['price'] = book.xpath('./span[@class="price"]/text()').get()
            item['rating'] = book.xpath('./span[@class="rating"]/text()').get()
            yield item
            next_page = response.xpath('//a[@class="next_page"]/@href').get()
            if next_page is not None:
                yield response.follow(next_page, callback=self.parse)

You can choose the selector type that best suits your needs or even mix both types in the same spider.

Scrapy Middleware and Settings

Scrapy provides middleware components to process requests and responses during the crawling process. Middleware can be used for tasks such as handling redirects, setting custom headers, or handling cookies. To create custom middleware, edit the file in your project and update the file to include your middleware.

Scrapy settings allow you to configure various aspects of your project, such as concurrent requests, download delay and user agent. You can modify the file to set these options.

Storing and Exporting Data

Scrapy provides built-in support for storing and exporting data in various formats, such as JSON, CSV and XML. To store the scraped data, use the -o option followed by the output file name and format when running your spider:

scrapy crawl book_spider -o books.json

This command will run the book_spider spider and store the extracted data in a JSON file named books.json.


In this article, we’ve covered the basics of creating a web crawler using Python and Scrapy. We’ve discussed setting up the environment, creating a Scrapy project, defining items, building a spider, extracting data with selectors, using middleware and setting and storing and exporting data. With this foundation, you can build powerful web crawlers to extract data from a wide range of websites for various applications, such as data mining, sentiment analysis and search engine indexing.