Scrapy is one of the most accessible tools that you can use to scrape and also spider a website with effortless ease.

Today lets see how we can scrape Wikipedia data for any topic.

Here is the URL we are going to scrape https://en.wikipedia.org/wiki/List_of_common_misconceptions, which provides a list of common misconceptions in life!

First, we need to install scrapy if you haven't already.

pip install scrapy

Once installed, go ahead and create a project by invoking the startproject command.

scrapy startproject scrapingproject

This will output something like this.

New Scrapy project 'scrapingproject', using template directory '/Library/Python/2.7/site-packages/scrapy/templates/project', created in:
    /Applications/MAMP/htdocs/scrapy_examples/scrapingproject

You can start your first spider with:
    cd scrapingproject
    scrapy genspider example example.com

And create a folder structure like this.

Now CD into the scrapingproject. You will need to do it twice like this.

cd scrapingproject
cd scrapingproject

Now we need a spider to crawl through the Wikipedia page. So we use the genspider to tell scrapy to create one for us. We call the spider ourfirstbot and pass it to the URL of the Wikipedia page.

scrapy genspider ourfirstbot https://en.wikipedia.org/wiki/List_of_common_misconceptions

This should return successfully like this.

Created spider 'ourfirstbot' using template 'basic' in module:
  scrapingproject.spiders.ourfirstbot

Great. Now open the file ourfirstbot.py in the spider's folder. It should look like this.

# -*- coding: utf-8 -*-
import scrapy


class OurfirstbotSpider(scrapy.Spider):
    name = 'ourfirstbot'
    start_urls = ['https://en.wikipedia.org/wiki/List_of_common_misconceptions']

    def parse(self, response):
        pass

Let's examine this code before we proceed.

He allowed_domains array restricts all further crawling to the domain paths specified here.

start_urls is the list of URLs to crawl. For us, in this example, we only need one URL.

The def parse(self, response): function is called by scrapy after every successful URL crawl. Here is where we can write our code to extract the data we want.

We now need to find the CSS selector of the elements we need to extract the data. Go to the URL en.wikipedia.org and right-click on one of the headlines of the Wikipedia data and click on inspect. This will open the Google Chrome Inspector like below.

You can see that the CSS class name of the headline element is MW-headline, so we are going to ask scrapy to get us the contents of this class like this.

dates = response.css('.mw-headline').extract()

Now we see that there is an element that lists all the content pieces in bulleted list form, so let's get that by the selector below.

datas = response.css('ul').extract()

If you are unfamiliar with CSS selectors, you can refer to this page by Scrapy https://docs.scrapy.org/en/latest/topics/selectors.html


We have to now use the zip function to map a similar index of multiple containers so that they can be used just using a single entity. So here is how it looks.


# -*- coding: utf-8 -*-
import scrapy
from bs4 import BeautifulSoup
import urllib


class OurfirstbotSpider(scrapy.Spider):
    name = 'ourfirstbot'
    start_urls = [
        'https://en.wikipedia.org/wiki/List_of_common_misconceptions',
    ]

    def parse(self, response):
        #yield response
        headings = response.css('.mw-headline').extract()       
        datas = response.css('ul').extract()       

        
        for item in zip(headings, datas):
            all_items = {
                'headings' : BeautifulSoup(item[0]).text,
                'datas' : BeautifulSoup(item[1]).text,


            }


            yield all_items

We use BeautifulSoup to remove HTML tags and get pure text and now lets run this with the command (Notice we are turning off obeying Robots.txt)

scrapy crawl ourfirstbot -s ROBOTSTXT_OBEY=False

Bingo. You get the results below.




Now, let's export the extracted data to a CSV file. All you have to do is to provide an export file like this.


scrapy crawl ourfirstbot -o data.csv

Or if you want the data in the JSON format.


scrapy crawl ourfirstbot -o data.json

Scaling Scrapy


The example above is ok for small scale web crawling projects. But if you try to scrape large quantities of data at high speeds from websites like Wikipedia, you will find that sooner or later, your access will be restricted. Wikipedia can tell you are a bot, so one of the things you can do is run the crawler impersonating a web browser. This is done by passing the user agent string to the Wikipedia web server, so it doesn't block you.

Like this

scrapy crawl ourfirstbot -s USER_AGENT="Mozilla/5.0 (Windows NT 6.1; WOW64)/
 AppleWebKit/537.36 (KHTML, like Gecko) Chrome/34.0.1847.131 Safari/537.36" /
-s ROBOTSTXT_OBEY=False

In more advanced implementations, you will need to even rotate this string, so Wikipedia can't tell it the same browser! Welcome to web scraping.

If we get a little bit more advanced, you will realize that Wikipedia can simply block your IP, ignoring all your other tricks. This is a bummer, and this is where most web crawling projects fail.

Investing in a private rotating proxy service like Proxies API can most of the time make the difference between a successful and headache-free web scraping project, which gets the job done consistently and one that never really works.


Plus, with the 1000 free API calls running an offer, you have almost nothing to lose by using our rotating proxy and comparing notes. It only takes one line of integration to its hardly disruptive.

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• With millions of high speed rotating proxies located all over the world
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• With our automatic CAPTCHA solving technology

Hundreds of our customers have successfully solved the headache of IP blocks with a simple API.

A simple API can access the whole thing like below in any programming language.

curl "http://api.proxiesapi.com/?key=API_KEY&url=https://example.com"

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Once you have an API_KEY from Proxies API, you just have to change your code to this.

# -*- coding: utf-8 -*-
import scrapy
from bs4 import BeautifulSoup
import urllib


class OurfirstbotSpider(scrapy.Spider):
    name = 'ourfirstbot'
    start_urls = [
        'http://api.proxiesapi.com/?key=API_KEY&url=https://en.wikipedia.org/wiki/List_of_common_misconceptions',
    ]

    def parse(self, response):
        #yield response
        headings = response.css('.mw-headline').extract()       
        datas = response.css('ul').extract()       

        
        for item in zip(headings, datas):
            all_items = {
                'headings' : BeautifulSoup(item[0]).text,
                'datas' : BeautifulSoup(item[1]).text,


            }


            yield all_items

We have only changed one line at the start_urls array, and that will make sure we will never have to worry about IP rotation, user agent string rotation, or even rate limits ever again.