Web crawling and scraping in Python

In this article, we will be checking up few things:

  • Basic crawling setup In Python
  • Basic crawling with AsyncIO
  • Scraper Util service
  • Python scraping via Scrapy framework

Web Crawler

A web crawler is an automatic bot that extracts useful information by systematically browsing the world wide web.

The web crawler is also known as a spider or spider bot. Some websites use web crawling for updating their web content. Some websites do not allow crawling because of their security, so on that websites crawler works by either asking for permission or exiting out of the website.

Web Crawler

Web Scraping

Extracting the data from the websites is known as web scraping. Web scraping requires two parts crawler and scraper.

Crawler is known to be an artificial intelligence algorithm and it browses the web which leads to searching of the links we want to crawl across the internet.

Scraper is the tool that was specifically used for extracting information from the internet.

By web scraping, we can obtain a large amount of data which is in unstructured data in an HTML format and then it is converted into structured data.

Web Scraping

Crawler Demo

Mainly, we have been using two tools:

Task I

Scrap recurship website is used for extracting all the links and images present on the page.

Demo Code:

Import requests

from parsel import Selector

import time

start = time.time()

response = requests.get('http://recurship.com/')

selector  = Selector(response.text)

href_links = selector.xpath('//a/@href').getall()

image_links = selector.xpath('//img/@src').getall()

print("********************href_links****************")

print(href_links)

print("******************image_links****************")

print(image_links)

end = time.time()

print("Time taken in seconds:", (end_start)

 

Task II

Scrap recurship site and extract links, one of one navigate to each link and extract information of the images.

Demo code:

import requests
from parsel import Selector

import time
start = time.time()


all_images = {} 
response = requests.get('http://recurship.com/')
selector = Selector(response.text)
href_links = selector.xpath('//a/@href').getall()
image_links = selector.xpath('//img/@src').getall()

for link in href_links:
try:
response = requests.get(link)
if response.status_code == 200:
image_links = selector.xpath('//img/@src').getall()
all_images[link] = image_links
except Exception as exp:
print('Error navigating to link : ', link)

print(all_images)
end = time.time()
print("Time taken in seconds : ", (end-start))

 

Task II takes 22 seconds to complete. We are constantly using the python parsel” and request” package.

Let’s see some features these packages use.

Request package:

Parsel package

 

Crawler service using Request and Parsel

The code:

import requests
import time
import random
from urllib.parse import urlparse
import logging

logger = logging.getLogger(__name__)

LOG_PREFIX = 'RequestManager:'


class RequestManager:
def __init__(self):
self.set_user_agents(); # This is to keep user-agent same throught out one request

crawler_name = None
session = requests.session()
# This is for agent spoofing...
user_agents = [
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.246',
'Mozilla/4.0 (X11; Linux x86_64) AppleWebKit/567.36 (KHTML, like Gecko) Chrome/62.0.3239.108 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/601.3.9 (KHTML, like Gecko) Version/9.0.2 Safari/601.3.9',
'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko'
]

headers = {}

cookie = None
debug = True

def file_name(self, context: RequestContext, response, request_type: str = 'GET'):
url = urlparse(response.url).path.replace("/", "|")
return f'{time.time()}_{context.get("key")}_{context.get("category")}_{request_type}_{response.status_code}_{url}'

# write a file, safely
def write(self, name, text):
if self.debug:
file = open(f'logs/{name}.html', 'w')
file.write(text)
file.close()

def set_user_agents(self):
self.headers.update({
'user-agent': random.choice(self.user_agents)
})

def set_headers(self, headers):
logger.info(f'{LOG_PREFIX}:SETHEADER set headers {self.headers}')
self.session.headers.update(headers)

def get(self, url: str, withCookie: bool = False, context):
logger.info(f'{LOG_PREFIX}-{self.crawler_name}:GET making get request {url} {context} {withCookie}')
cookies = self.cookie if withCookie else None
response = self.session.get(url=url, cookies=cookies, headers=self.headers)
self.write(self.file_name(context, response), response.text)
return response

def post(self, url: str, data, withCookie: bool = False, allow_redirects=True, context: RequestContext = {}):
logger.info(f'{LOG_PREFIX}:POST making post request {url} {data} {context} {withCookie}')
cookies = self.cookie if withCookie else None
response = self.session.post(url=url, data=data, cookies=cookies, allow_redirects=allow_redirects)
self.write(self.file_name(context, response, request_type='POST'), response.text)
return response

def set_cookie(self, cookie):
self.cookie = cookie
logger.info(f'{LOG_PREFIX}-{self.crawler_name}:SET_COOKIE set cookie {self.cookie}')

Request = RequestManager()

context = {
"key": "demo",
"category": "history"
}
START_URI = "DUMMY_URL" # URL OF SIGNUP PORTAL
LOGIN_API = "DUMMY_LOGIN_API"
response = Request.get(url=START_URI, context=context)

Request.set_cookie('SOME_DUMMY_COOKIE')
Request.set_headers('DUMMY_HEADERS')

response = Request.post(url=LOGIN_API, data = {'username': '', 'passphrase': ''}, context=context)

 

Class “RequestManager” offers few functionalities listed below:

Scraping with AsyncIO

All we have to do is scrap the Recurship site and extract all the links, later we navigate each link asynchronously and extract information from the images.

Demo code

import requests
import aiohttp
import asyncio
from parsel import Selector
import time

start = time.time()
all_images = {} # website links as "keys" and images link as "values"

async def fetch(session, url):
try:
async with session.get(url) as response:
return await response.text()
except Exception as exp:
return '<html> <html>' #empty html for invalid uri case

async def main(urls):
tasks = []
async with aiohttp.ClientSession() as session:
for url in urls:
tasks.append(fetch(session, url))
htmls = await asyncio.gather(*tasks)
for index, html in enumerate(htmls):
selector = Selector(html)
image_links = selector.xpath('//img/@src').getall()
all_images[urls[index]] = image_links
print('*** all images : ', all_images)


response = requests.get('http://recurship.com/')
selector = Selector(response.text)
href_links = selector.xpath('//a/@href').getall()
loop = asyncio.get_event_loop()
loop.run_until_complete(main(href_links))


print ("All done !")
end = time.time()
print("Time taken in seconds : ", (end-start))

By AsyncIO, scraping took almost 21 seconds. We can achieve more good performance with this task.

Open-Source Python Frameworks for spiders

Python has multiple frameworks which take care of the optimization

It gives us different patterns. There are three popular frameworks, namely:

  1. Scrapy
  2. PySpider
  3. Mechanical soup

Let’s use Scrapy for further demo.

Scrapy

Scrapy is a framework used for scraping and is supported by an active community. We can build our own scraping tools.

There are few features which scrapy provides:

Now we have to do is scrap the Recurship site and extract all the links, later we navigate each link asynchronously and extract information from the images.

Demo Code

import scrapy


class AuthorSpider(scrapy.Spider):
name = 'Links'

start_urls = ['http://recurship.com/']
images_data = {}
def parse(self, response):
# follow links to author pages
for img in response.css('a::attr(href)'):
yield response.follow(img, self.parse_images)

# Below commented portion is for following all pages
# follow pagination links
# for href in response.css('a::attr(href)'):
# yield response.follow(href, self.parse)

def parse_images(self, response):
#print "URL: " + response.request.url
def extract_with_css(query):
return response.css(query).extract()
yield {
'URL': response.request.url,
'image_link': extract_with_css('img::attr(src)')
}

Commands

scrapy run spider -o output.json spider.py

The JSON file got export in 1 second.

Conclusion

We can see that the scrapy performed an excellent job. If we have to perform simple crawling, scrapy will give the best results.

Enjoy scraping!!

 

 

 

 

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