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Scraping a website With Python + Scrapy

Logo of Scrapy.
Logo of Scrapy.

In this tutorial, you will learn how to write a simple webscraper in Python using the Scrapy framework. The Data Blogger website will be used as an example in this article.

An open source and collaborative framework for extracting the data you need from websites. In a fast, simple, yet extensible way.

Content + Link extractor

The purpose of Scrapy is to extract content and links from a website by recursively following all the links on the given website.

Installing Scrapy

According to, we just have to execute the following command to install Scrapy:

pip install scrapy

If this does not work, please have a look at the detailed installation instructions.

Setting up the project

Now we will create the folder structure for your project. For the Data Blogger scraper, the following command is used. You can change datablogger_scraper to the name of your project.

scrapy startproject datablogger_scraper

Creating an Object

The next thing to do, is to create a spider that will crawl the website(s) of interest. The spider needs to know what data is crawled. This data can be put into an object. In this tutorial we will crawl internal links of a website. A link is defined as an object having a source URL on which the link can be found and it has a destination URL to which the link is navigating to when it is clicked. It is called an internal link if both the source URL and destination URL are on the website itself. The object is defined in and for this project, has the following contents:

import scrapy

class DatabloggerScraperItem(scrapy.Item):
    # The source URL
    url_from = scrapy.Field()
    # The destination URL
    url_to = scrapy.Field()

Notice that in your project, you can define any object you would like to crawl! For example, you can specify an object Game Console (with properties “vendor”, “price” and “release date”) when you are scraping a website about Game Consoles. If you are scraping information about music from multiple websites, you could define an object with properties like “artist”, “release date” and “genre”.

Creating the Spider

Now we have encapsulated the data into an object, we can start creating the spider. First, we will navigate towards the project folder and then we will execute the following command to create a spider (which can then be found in the spiders/ directory):

scrapy genspider datablogger 

Now, a spider is created (spiders/ You can customize this file as much as you want. I ended up with the following code:

# -*- coding: utf-8 -*-
import scrapy
from scrapy.linkextractor import LinkExtractor
from scrapy.spiders import Rule, CrawlSpider
from datablogger_scraper.items import DatabloggerScraperItem

class DatabloggerSpider(CrawlSpider):
    # The name of the spider
    name = "datablogger"

    # The domains that are allowed (links to other domains are skipped)
    allowed_domains = [""]

    # The URLs to start with
    start_urls = [""]

    # This spider has one rule: extract all (unique and canonicalized) links, follow them and parse them using the parse_items method
    rules = [

    # Method which starts the requests by visiting all URLs specified in start_urls
    def start_requests(self):
        for url in self.start_urls:
            yield scrapy.Request(url, callback=self.parse, dont_filter=True)

    # Method for parsing items
    def parse_items(self, response):
        # The list of items that are found on the particular page
        items = []
        # Only extract canonicalized and unique links (with respect to the current page)
        links = LinkExtractor(canonicalize=True, unique=True).extract_links(response)
        # Now go through all the found links
        for link in links:
            # Check whether the domain of the URL of the link is allowed; so whether it is in one of the allowed domains
            is_allowed = False
            for allowed_domain in self.allowed_domains:
                if allowed_domain in link.url:
                    is_allowed = True
            # If it is allowed, create a new item and add it to the list of found items
            if is_allowed:
                item = DatabloggerScraperItem()
                item['url_from'] = response.url
                item['url_to'] = link.url
        # Return all the found items
        return items

A few things are worth mentioning. The crawler extends the CrawlSpider object, which has a parse method for scraping a website recursively. In the code, one rule is defined which tells the crawler to follow all links it encounters. The rule also specifies that only unique links are parsed, so none of the links will be parsed twice! Furthermore, the canonicalize property makes sure that links are not parsed twice.


The LinkExtractor is a module with the purpose of extracting links from web pages.

Executing the Spider

Go to the root folder of your project. Then execute the following command:

scrapy crawl datablogger -o links.csv -t csv

This command then runs over your website and generates a CSV file. In my case, I got a CSV file named links.csv with the following content:



It is relatively easy to write your own spider with Scrapy. You can specify the data you want to scrape in an object and you can specify the behaviour of your crawler. If you have any questions, feel free to ask them in the comments section!

Kevin Jacobs

Kevin Jacobs

Kevin Jacobs is a certified Data Scientist and blog writer for Data Blogger. He is passionate about any project that involves large amounts of data and statistical data analysis. Kevin can be reached using Twitter (@kmjjacobs), LinkedIn or via e-mail: