This tutorial/walk through assumes that you are already familiar with python and the terminal. I would also strongly advice anyone remotely interested in understanding scrapy to have a look at their documentation and preferably follow the quick tutorial and setup instruction outlined.
The example in the documentation is a simple spider. Basic spiders are good if we intend on retrieving information from a list of pages that we currently know the URLs of. This is no good in our scenario where a lot of pages need to be scraped. This is where the crawler comes in, crawlers provide a convenient mechanism to follow links, you can refer to the documentation here to refresh your memory.
###Pick a website
We are going to experiment with a Kenyan news website. Please feel free to substitue this website with one that suits your taste. Daily Nation For those of you keen enough, you will realise that I have an app for this news source on the App Store
Follow the Installation Guide here, but the gist of it all is basically.
pip install Scrapy
####Create a new project
**Have you installed scrapy yet? I struggled to get it to work on osx, if you get libxml issue, just know you are in for a long ride **
scrapy startproject hermes
This will create a project called
hermes in the directory you ran it in tutorial in this case.
Explore this directory, its pretty boring at first.
tutorial/ scrapy.cfg #config file for deployment tutorial/ #project dir/module __init__.py items.py #Model pipelines.py settings.py #project settings spiders/ #spider directory __init__.py ...
Scrapy did the burden of work for us. There are only two files we have to be concerned with at the moment, thats the items.py and the spiders. The spider directory will hold all the spiders.
###Define the model.
In scrapy, the models are defined in the items.py file. All items have a Field() type. We create our model named
NationmediaItem our items class looks like the following.
from scrapy.item import Item, Field class NationmediaItem(Item): title = Field() link = Field() summary = Field() content = Field() author = Field()
###Defining our Spider
Spiders are user defined and they are usually quite specific to a particular website, or a group of websites with the same structure.
A spider in its most basic form will have a list or URLS to download, rules that define how links are followed and a method to parse the content.
A spider must subclass
scrapy.spider.Spider and define the following:
name:Unique identifier for the spider.
start_urls:a list of URLS where crawling will start from.
parse():call back method called when the content has been downloaded, a
Responseobject returned and is passed to the method as an argument.
Navigate into the
spider directory and create an empty python file. Call this file nmdspider.py.
Our class looks like the following:
from scrapy.spider import BaseSpider from scrapy.selector import HtmlXPathSelector from nationmedia.items import NationmediaItem from scrapy.contrib.spiders import CrawlSpider, Rule from scrapy.contrib.linkextractors.sgml import SgmlLinkExtractor from scrapy.selector import Selector class MainParser(): def __init__(self, response, tags): self.response = response self.tags = tags def scrapNationOnline(self): hxs = Selector(self.response) item = NationmediaItem() item ["link"] = self.response.url item ["title"] = ''.join(hxs.xpath('//div[@class="story-view"]/header/h1/text()').extract()) #this worked by taking the page title, contains tags we dont need/want ''.join(hxs.css('title::text').extract()) item ["summary"] = ''.join(hxs.xpath('//div/ul/li/text()').extract()) item ["content"] = 'n'.join(hxs.xpath('//article/section/div/p/text()').extract()) #retrieve the author. There are numerous formatting issue with this tag author = hxs.xpath('//section[@class="author"]/strong/text()').extract() if not author: author = hxs.xpath('//section[@class="author"]/text()').extract() item ["author"] = ''.join(author) return item class PoliticsSpider(CrawlSpider): """Scrapes Polics News""" name = "politics" allowed_domains = ["nation.co.ke"] start_urls = ["http://www.nation.co.ke/news/politics/-/1064/1064/-/3hxffhz/-/index.html"] rules = ( Rule(SgmlLinkExtractor(allow=('news/politics/',), deny=('/view/asFeed/', '/-/1064/1064/-/3hxffhz/-/index.html')), callback='parse_page', follow=True), ) def parse_page(self, response): mainParser = MainParser(response, ["News", "Politics"]) item = mainParser.scrapNationOnline() return item
Take a look at the code from the repository here Its going to be clearer than the mess above.
###Time to run the crawler
scrapy crawl politics
Its as simple as that. To run the spider and save the results in a json file run
scrapy crawl politics -o politics.json -t json
The output should be a json file of all the scrapped articles. In this case from the politics section of nation.co.ke
As you can see from the code, we are using xpath to extract information from the html response, this is the part that makes scrapping such a fragile task, if the website you are scrapping updates its html files, then you have to do the same here.
This is our main class, if you are going to have multiple crawlers running, this is the class to duplicate. To use this class, you have to set a few parameters.
- Allowed Domains
All these are self explanatory apart from the last one. These are rules** to follow while crawling links originating from the Start_urls.
Allow: In our example, we are only accepting URLS containing
news/politics, as we sure that any article with that URL is a politics item.
Restricted_xpaths: Restricts the link to a certain xpath, we are currently not implementing this.
Deny: We also deny
/view/asFeed as this is the extension found in the RSS feed page, there are plenty of other rules we can apply, but these two seem to be of utmost importance.
Callback: This is the method we call back to after parsing is complete, please DO NOT name your method ‘parse’ as this is already in use internally by scrapy, I learnt this the hard way.
Follow: Instructs the program to continue following links as long as they exist, this is how we recursively crawl entire websites.
This method is called once we have retrieved the contents of a page. The page is still in its original format, in this case HTML, it will need to be structured.
We delegate the actual parsing to the MainParse class, just trying to observe some OOP principles. To execute the actual parsing, we make the following call.
item = mainParser.scrapNationOnline(). It returns an Item object, this item object defines a news article.
That is all that you need to parse a website, I would suggest you read the documentaion for Scrapy as I have left quite a lot unanswered here.
Website scraping is fun, but don’t be a douche bag, don’t run 100 scrapers 24/7 on someone else’s site. Also don’t steal information and claim it as your own, this really pisses some people off.
In the next web scraping tutorial, we will look at how we deploy our scraper to the cloud and automate the entire process.