Table of Contents

1. Challenge

   Set Up a Project and Virtualenv using Pipenv and the Scrapy Generator

   To set up our project, we're going to create a new directory with an internal directory of the same name (daily_wiki) to hold our scraper project:

   $ mkdir daily_wiki
   $ cd daily_wiki
   

   Next, let's make sure that Pipenv is installed and then use it to create our virtualenv and install Scrapy:

   $ pip3.7 install --user -U pipenv
   ...
   $ pipenv --python python3.7 install scrapy
   ...
   

   Once the virtualenv is created, we should activate it while working on this project:

   $ pipenv shell
   (daily_wiki) $
   

   Lastly, we'll create our project using the Scrapy project generator:

   (daily_wiki) $ scrapy startproject daily_wiki .
   

2. Challenge

   Create an `Article` Item

   Before we extract the information from the page, we need to set up a class that has fields for our Article within the daily_wiki/items.py file. This item will have a title field and a link field. By defining the item, we'll be able to have Scrapy automatically export information for us later on.

   daily_wiki/items.py

   # -*- coding: utf-8 -*-
   
   import scrapy
   
   class Article(scrapy.Item):
       title = scrapy.Field()
       link = scrapy.Field()
   
   

   Now we'll be able to yield one of these items for each of the links that we find on the featured content page.

3. Challenge

   Create an Articles Spider

   Almost all of the code that we need to write is going to go into a Scrapy spider. Thankfully, Scrapy's CLI provides a way to generate a spider. Let's do that now. This is a spider for articles, so we'll call it article, and our domain will be en.wikipedia.org.

   (daily_wiki) $ scrapy genspider article en.wikipedia.org
   Created spider 'article' using template 'basic' in module:
     daily_wiki.spiders.article
   

   Now we have a file at daily_wiki/spiders/article.py that we'll be working in. The URL that we're going to be working with is https://en.wikipedia.org/wiki/Wikipedia:Featured_articles. If we take a look at this page, we can see that there are a lot of links. Thankfully, the links that are important to us share a common CSS class for the container called "featured_article_metadata", and the link is just an anchor within it. This is the only page we'll need to work with, so let's write our selector and loop to get this information into the parse method:

   daily_wiki/spiders/article.py

   # -*- coding: utf-8 -*-
   import scrapy
   
   from daily_wiki.items import Article
   
   class ArticleSpider(scrapy.Spider):
       name = 'article'
       allowed_domains = ['en.wikipedia.org']
       start_urls = ['https://en.wikipedia.org/wiki/Wikipedia:Featured_articles']
   
       def parse(self, response):
           host = self.allowed_domains[0]
           for link in response.css(".featured_article_metadata > a"):
               yield Article(
                   title = link.attrib.get("title"),
                   link = f"https://{host}{link.attrib.get('href')}"
               )
   
   

   We can test this spider by running the following command:

   (daily_wiki) $ scrapy crawl article
   ...
   2019-08-09 14:46:38 [scrapy.core.engine] INFO: Closing spider (finished)
   2019-08-09 14:46:38 [scrapy.statscollectors] INFO: Dumping Scrapy stats:
   {'downloader/request_bytes': 585,
    'downloader/request_count': 2,
    'downloader/request_method_count/GET': 2,
    'downloader/response_bytes': 154870,
    'downloader/response_count': 2,
    'downloader/response_status_count/200': 2,
    'elapsed_time_seconds': 1.507018,
    'finish_reason': 'finished',
    'finish_time': datetime.datetime(2019, 8, 9, 18, 46, 38, 405739),
    'item_scraped_count': 3945,
    'log_count/DEBUG': 3947,
    'log_count/INFO': 10,
    'memusage/max': 51822592,
    'memusage/startup': 51822592,
    'response_received_count': 2,
    'robotstxt/request_count': 1,
    'robotstxt/response_count': 1,
    'robotstxt/response_status_count/200': 1,
    'scheduler/dequeued': 1,
    'scheduler/dequeued/memory': 1,
    'scheduler/enqueued': 1,
    'scheduler/enqueued/memory': 1,
    'start_time': datetime.datetime(2019, 8, 9, 18, 46, 36, 898721)}
   2019-08-09 14:46:38 [scrapy.core.engine] INFO: Spider closed (finished)
   

   It looks like we were able to scrape 3945 items from the page (this number may vary if the page has changed). Now that we've successfully extracted the data, all that's left to do is configure Scrapy to export this information as JSON.

4. Challenge

   Export Articles as JSON

   Scrapy provides various configuration values that we can set to have it export a "feed". One easy way that we can do this is by adding a custom_settings attribute to our spider and setting the FEED_FORMAT and FEED_URI keys. Let's do this now. We'll set it to export as JSON and use a format of featured-articles-%(time).json within the /tmp directory:

   daily_wiki/spiders/article.py

   # -*- coding: utf-8 -*-
   import scrapy
   
   from daily_wiki.items import Article
   
   class ArticleSpider(scrapy.Spider):
       name = 'article'
       allowed_domains = ['en.wikipedia.org']
       start_urls = ['https://en.wikipedia.org/wiki/Wikipedia:Featured_articles']
   
       # Enable Feed Storage
       custom_settings = {
           'FEED_FORMAT': 'json',
           'FEED_URI': 'file:///tmp/featured-articles-%(time)s.json'
       }
   
       def parse(self, response):
           host = self.allowed_domains[0]
           for link in response.css(".featured_article_metadata > a"):
               yield Article(
                   title = link.attrib.get("title"),
                   link = f"https://{host}{link.attrib.get('href')}"
               )
   
   

   Now if we run our crawler again and check the /tmp directory, we should see a new JSON file.

   Note: Your timestamp will be different.

   (daily_wiki) $ scrapy crawl article
   ...
   (daily_wiki) $ ls -al /tmp | grep featured
   -rw-r--r--   1 cloud_user  wheel  377238 Aug  9 14:54 featured-articles-2019-08-09T18-54-38.json
   

   We've successfully created a web scraper that can get the featured articles from Wikipedia and put them in a JSON file.