Creating a Web Scraper with Python and Scrapy

The vast amount of data available on the internet can be both a blessing and a curse. While there is a wealth of information out there, gathering and organizing it can be a daunting task. This is where web scraping comes in. Web scraping is the process of extracting data from websites and it can be a valuable tool for businesses and researchers alike. In this guide, we will explore how to create a web scraper using Python and Scrapy, a powerful and popular web scraping framework. Whether you’re a beginner or an experienced programmer, by the end of this article, you’ll have the knowledge to start building your own web scraper.

Introduction to Web Scraping and scrapy

Web scraping is a technique used to extract information from websites using automated scripts. This process is essential when you need to gather large amounts of data from the internet. Web scraping can be used for various purposes, such as data mining, data extraction, sentiment analysis and more.

Before we get started, it’s essential to understand that web scraping raises some ethical and legal concerns. Always ensure you have permission to scrape a website and follow the site’s terms of service and robots.txt file. Be respectful and don’t overwhelm the server with requests.

Scrapy is an open-source web scraping framework for Python. It provides a comprehensive set of tools to create and manage web scraping projects. Scrapy is designed for extensibility, which means you can customize it to fit your specific needs. Some of its key features include:

  • A built-in HTTP client to handle web requests
  • CSS and XPath selectors to extract data from web pages
  • A simple and extensible item pipeline to process and store the scraped data
  • Middleware support for handling requests and responses
  • Built-in support for exporting data in various formats (CSV, JSON, XML)
  • Command-line tools to generate, run and debug Scrapy projects

Setting Up Your Python Environment

To begin, you’ll need to have Python installed on your computer. If you don’t have Python installed already, visit the official Python website to download and install the latest version.

We also recommend setting up a virtual environment for your web scraping project. This helps to keep your project’s dependencies separate from your system’s global Python environment. To create a virtual environment, you can use the venv module:

python -m venv scraper_env

Activate the virtual environment:

  • On Windows:


  • On macOS and Linux:

source scraper_env/bin/activate

With your virtual environment activated, you can now install Scrapy using pip:

With your virtual environment activated, you can now install Scrapy using pip:

pip install Scrapy

Creating a Scrapy Project

To create a new Scrapy project, navigate to the directory where you want to store your project files and run the following command:

scrapy startproject my_scraper

This command will create a new directory called my_scraper with the following structure:


The spider’s directory is where you’ll create your web-scraping spiders. A spider is a class that defines how to crawl and extract data from a website.

Defining a Scrapy Spider

To create a new spider, navigate to the spider’s directory and create a new Python file. In this example, we’ll name it In this file, define a new spider class that inherits from scrapy.Spider. The spider must have a unique name and a start_urls attribute, a list of URLs to begin scraping.

import scrapy
import scrapy
class ExampleSpider(scrapy.Spider):
    name = 'example'
    start_urls = ['']

    def parse(self, response):

The parse method is a callback function that will be called with the HTTP response of each URL in start_urls. This method should extract the data you want to scrape and, if needed, generate more requests to follow links to other pages.

Extracting Data with CSS and XPath Selectors

Scrapy provides built-in support for extracting data from web pages using CSS and XPath selectors. You can use the response.css and response.xpath methods to create selectors that match elements on the page.

For example, if you want to extract all the links from a page, you can use the following code:

def parse(self, response):
    links = response.css('a::attr(href)').getall()

You can also use selectors to extract other types of data, such as text or images. For example, to extract the title of a page, you can use:

def parse(self, response):
title = response.css('title::text').get()

You can also chain selectors together to extract more specific data. For example, to extract the text of all paragraphs inside a div element with a class of “content”, you can use:

def parse(self, response):
paragraphs = response.css('div.content p::text').getall()

XPath selectors work similarly to CSS selectors but use a different syntax. To use an XPath selector, use the response.xpath method instead of response.css.

Storing the Scraped Data

Once you’ve extracted the data you want to scrape, you’ll need to store it somewhere. Scrapy provides a built-in item pipeline that makes it easy to process and store scraped data. To use the item pipeline, you’ll need to define an item class that defines the structure of your scraped data. You can then use instances of this class to store the scraped data.

In the file, define a new item class that inherits from scrapy.Item. Define the fields you want to store as class attributes:

import scrapy
class MyItem(scrapy.Item):
title = scrapy.Field()
description = scrapy.Field()
# add more fields as needed

In your spider, use instances of the item class to store the scraped data. You can create an instance of the item class and set its fields using dictionary-like syntax:

from my_scraper.items import MyItem
def parse(self, response):
item = MyItem()
item['title'] = response.css('title::text').get()
item['description'] = response.css('meta[name="description"]::attr(content)').get()
yield item

The yield keyword returns the item to the item pipeline, which processes it and stores it. By default, the item pipeline stores items in memory, but you can customize it to store items in a database or file.

Fine-tuning Your Scrapy Spider

Scrapy provides various settings that you can use to customize your spider’s behavior. You can define settings in the file. Some useful settings include:

USER_AGENT: The user agent to use when sending HTTP requests.

ROBOTSTXT_OBEY: Whether to obey the website’s robots.txt file.

DOWNLOAD_DELAY: The delay between requests to the same domain.

CONCURRENT_REQUESTS: The number of concurrent requests to make.

You can also define middleware functions to modify requests and responses. Middleware functions can be used to add headers, handle redirects, or modify the response before it’s processed by the spider.

Deploying Your Scrapy Spider

Once you’ve created and tested your spider, you may want to deploy it to a server or cloud service. Scrapy provides built-in support for deploying spiders to various platforms, such as Scrapy Cloud or Amazon Web Services. To deploy your spider, you’ll need to configure the deployment settings in the file and run the deploy command:

scrapy deploy


In this tutorial, we’ve covered the basics of creating a web scraper using Python and Scrapy. We’ve learned how to define a spider, extract data using selectors and store the scraped data using the item pipeline. We’ve also covered some advanced topics, such as customizing spider settings and deploying your spider to a cloud service. With these tools, you should be able to create powerful and flexible web scraping scripts to extract data from the web.