Web Crawler in Python – TopCoder
With the advent of the era of big data, the need for network information has increased widely. Many different companies collect external data from the Internet for various reasons: analyzing competition, summarizing news stories, tracking trends in specific markets, or collecting daily stock prices to build predictive models. Therefore, web crawlers are becoming more important. Web crawlers automatically browse or grab information from the Internet according to specified assification of web crawlersAccording to the implemented technology and structure, web crawlers can be divided into general web crawlers, focused web crawlers, incremental web crawlers, and deep web workflow of web crawlersBasic workflow of general web crawlersThe basic workflow of a general web crawler is as follows:Get the initial URL. The initial URL is an entry point for the web crawler, which links to the web page that needs to be crawled;While crawling the web page, we need to fetch the HTML content of the page, then parse it to get the URLs of all the pages linked to this these URLs into a queue;Loop through the queue, read the URLs from the queue one by one, for each URL, crawl the corresponding web page, then repeat the above crawling process;Check whether the stop condition is met. If the stop condition is not set, the crawler will keep crawling until it cannot get a new URL. Environmental preparation for web crawlingMake sure that a browser such as Chrome, IE or other has been installed in the wnload and install PythonDownload a suitable IDLThis article uses Visual Studio CodeInstall the required Python packagesPip is a Python package management tool. It provides functions for searching, downloading, installing, and uninstalling Python packages. This tool will be included when downloading and installing Python. Therefore, we can directly use ‘pip install’ to install the libraries we need. 1
2
3
pip install beautifulsoup4
pip install requests
pip install lxml
• BeautifulSoup is a library for easily parsing HTML and XML data. • lxml is a library to improve the parsing speed of XML files. • requests is a library to simulate HTTP requests (such as GET and POST). We will mainly use it to access the source code of any given following is an example of using a crawler to crawl the top 100 movie names and movie introductions on Rotten p100 movies of all time –Rotten TomatoesWe need to extract the name of the movie on this page and its ranking, and go deep into each movie link to get the movie’s introduction. 1. First, you need to import the libraries you need to use. 1
4
import requests
import lxml
from bs4
import BeautifulSoup
2. Create and access URLCreate a URL address that needs to be crawled, then create the header information, and then send a network request to wait for a response. 1
url = ”
f = (url)
When requesting access to the content of a webpage, sometimes you will find that a 403 error will appear. This is because the server has rejected your access. This is the anti-crawler setting used by the webpage to prevent malicious collection of information. At this time, you can access it by simulating the browser header information. 1
5
headers = {
‘User-Agent’: ‘Mozilla/5. 0 (Windows NT 6. 1; WOW64) AppleWebKit/537. 36 (KHTML, like Gecko) Chrome/63. 0. 3239. 132 Safari/537. 36 QIHU 360SE’}
f = (url, headers = headers)
3. Parse webpageCreate a BeautifulSoup object and specify the parser as = BeautifulSoup(ntent, ‘lxml’)4. Extract informationThe BeautifulSoup library has three methods to find elements:findall():find all nodesfind():find a single nodeselect():finds according to the selector CSS SelectorWe need to get the name and link of the top100 movies. We noticed that the name of the movie needed is under. After extracting the page content using BeautifulSoup, we can use the find method to extract the relevant = (‘table’, {‘class’:’table’}). find_all(‘a’)Get an introduction to each movieAfter extracting the relevant information, you also need to extract the introduction of each movie. The introduction of the movie is in the link of each movie, so you need to click on the link of each movie to get the code is:1
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
movies_lst = []
soup = BeautifulSoup(ntent, ‘lxml’)
movies = (‘table’, {
‘class’: ‘table’}). find_all(‘a’)
num = 0
for anchor in movies:
urls = ” + anchor[‘href’]
(urls)
num += 1
movie_url = urls
movie_f = (movie_url, headers = headers)
movie_soup = BeautifulSoup(ntent, ‘lxml’)
movie_content = (‘div’, {
‘class’: ‘movie_synopsis clamp clamp-6 js-clamp’})
print(num, urls, ‘\n’, ‘Movie:’ + ())
print(‘Movie info:’ + ())
The output is:Write the crawled data to ExcelIn order to facilitate data analysis, the crawled data can be written into Excel. We use xlwt to write data into the xlwt xlwt import *Create an empty table. 1
workbook = Workbook(encoding = ‘utf-8’)
table = d_sheet(‘data’)
Create the header of each column in the first row.
(0, 0, ‘Number’)
(0, 1, ‘movie_url’)
(0, 2, ‘movie_name’)
(0, 3, ‘movie_introduction’)
Write the crawled data into Excel separately from the second row.
(line, 0, num)
(line, 1, urls)
(line, 2, ())
(line, 3, ())
line += 1
Finally, save (”)The final code is:1
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
from xlwt
import *
line = 1
(”)
The result is:Click to show preference! Click to show preference!
Web Crawler in Python – TopCoder
With the advent of the era of big data, the need for network information has increased widely. Many different companies collect external data from the Internet for various reasons: analyzing competition, summarizing news stories, tracking trends in specific markets, or collecting daily stock prices to build predictive models. Therefore, web crawlers are becoming more important. Web crawlers automatically browse or grab information from the Internet according to specified assification of web crawlersAccording to the implemented technology and structure, web crawlers can be divided into general web crawlers, focused web crawlers, incremental web crawlers, and deep web workflow of web crawlersBasic workflow of general web crawlersThe basic workflow of a general web crawler is as follows:Get the initial URL. The initial URL is an entry point for the web crawler, which links to the web page that needs to be crawled;While crawling the web page, we need to fetch the HTML content of the page, then parse it to get the URLs of all the pages linked to this these URLs into a queue;Loop through the queue, read the URLs from the queue one by one, for each URL, crawl the corresponding web page, then repeat the above crawling process;Check whether the stop condition is met. If the stop condition is not set, the crawler will keep crawling until it cannot get a new URL. Environmental preparation for web crawlingMake sure that a browser such as Chrome, IE or other has been installed in the wnload and install PythonDownload a suitable IDLThis article uses Visual Studio CodeInstall the required Python packagesPip is a Python package management tool. It provides functions for searching, downloading, installing, and uninstalling Python packages. This tool will be included when downloading and installing Python. Therefore, we can directly use ‘pip install’ to install the libraries we need. 1
2
3
pip install beautifulsoup4
pip install requests
pip install lxml
• BeautifulSoup is a library for easily parsing HTML and XML data. • lxml is a library to improve the parsing speed of XML files. • requests is a library to simulate HTTP requests (such as GET and POST). We will mainly use it to access the source code of any given following is an example of using a crawler to crawl the top 100 movie names and movie introductions on Rotten p100 movies of all time –Rotten TomatoesWe need to extract the name of the movie on this page and its ranking, and go deep into each movie link to get the movie’s introduction. 1. First, you need to import the libraries you need to use. 1
4
import requests
import lxml
from bs4
import BeautifulSoup
2. Create and access URLCreate a URL address that needs to be crawled, then create the header information, and then send a network request to wait for a response. 1
url = ”
f = (url)
When requesting access to the content of a webpage, sometimes you will find that a 403 error will appear. This is because the server has rejected your access. This is the anti-crawler setting used by the webpage to prevent malicious collection of information. At this time, you can access it by simulating the browser header information. 1
5
headers = {
‘User-Agent’: ‘Mozilla/5. 0 (Windows NT 6. 1; WOW64) AppleWebKit/537. 36 (KHTML, like Gecko) Chrome/63. 0. 3239. 132 Safari/537. 36 QIHU 360SE’}
f = (url, headers = headers)
3. Parse webpageCreate a BeautifulSoup object and specify the parser as = BeautifulSoup(ntent, ‘lxml’)4. Extract informationThe BeautifulSoup library has three methods to find elements:findall():find all nodesfind():find a single nodeselect():finds according to the selector CSS SelectorWe need to get the name and link of the top100 movies. We noticed that the name of the movie needed is under. After extracting the page content using BeautifulSoup, we can use the find method to extract the relevant = (‘table’, {‘class’:’table’}). find_all(‘a’)Get an introduction to each movieAfter extracting the relevant information, you also need to extract the introduction of each movie. The introduction of the movie is in the link of each movie, so you need to click on the link of each movie to get the code is:1
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
movies_lst = []
soup = BeautifulSoup(ntent, ‘lxml’)
movies = (‘table’, {
‘class’: ‘table’}). find_all(‘a’)
num = 0
for anchor in movies:
urls = ” + anchor[‘href’]
(urls)
num += 1
movie_url = urls
movie_f = (movie_url, headers = headers)
movie_soup = BeautifulSoup(ntent, ‘lxml’)
movie_content = (‘div’, {
‘class’: ‘movie_synopsis clamp clamp-6 js-clamp’})
print(num, urls, ‘\n’, ‘Movie:’ + ())
print(‘Movie info:’ + ())
The output is:Write the crawled data to ExcelIn order to facilitate data analysis, the crawled data can be written into Excel. We use xlwt to write data into the xlwt xlwt import *Create an empty table. 1
workbook = Workbook(encoding = ‘utf-8’)
table = d_sheet(‘data’)
Create the header of each column in the first row.
(0, 0, ‘Number’)
(0, 1, ‘movie_url’)
(0, 2, ‘movie_name’)
(0, 3, ‘movie_introduction’)
Write the crawled data into Excel separately from the second row.
(line, 0, num)
(line, 1, urls)
(line, 2, ())
(line, 3, ())
line += 1
Finally, save (”)The final code is:1
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
from xlwt
import *
line = 1
(”)
The result is:Click to show preference! Click to show preference!
How to build a URL crawler to map a website using Python
by Ahad SheriffA simple project for learning the fundamentals of web scrapingBefore we start, let’s make sure we understand what web scraping is:Web scraping is the process of extracting data from websites to present it in a format users can easily make sense this tutorial, I want to demonstrate how easy it is to build a simple URL crawler in Python that you can use to map websites. While this program is relatively simple, it can provide a great introduction to the fundamentals of web scraping and automation. We will be focusing on recursively extracting links from web pages, but the same ideas can be applied to a myriad of other program will work like this:Visit a web pageScrape all unique URL’s found on the webpage and add them to a queueRecursively process URL’s one by one until we exhaust the queuePrint resultsFirst Things FirstThe first thing we should do is import all the necessary libraries. We will be using BeautifulSoup, requests, and urllib for web bs4 import BeautifulSoupimport requestsimport requests. exceptionsfrom import urlsplitfrom import urlparsefrom collections import dequeNext, we need to select a URL to start crawling from. While you can choose any webpage with HTML links, I recommend using ScrapeThisSite. It is a safe sandbox that you can crawl without getting in = “Next, we are going to need to create a new deque object so that we can easily add newly found links and remove them once we are finished processing them. Pre-populate the deque with your url variable:# a queue of urls to be crawled nextnew_urls = deque([url])We can then use a set to store unique URL’s once they have been processed:# a set of urls that we have already processed processed_urls = set()We also want to keep track of local (same domain as the target), foreign (different domain as the target), and broken URLs:# a set of domains inside the target websitelocal_urls = set()# a set of domains outside the target websiteforeign_urls = set()# a set of broken urlsbroken_urls = set()Time To CrawlWith all that in place, we can now start writing the actual code to crawl the want to look at each URL in the queue, see if there are any additional URL’s within that page and add each one to the end of the queue until there are none left. As soon as we finish scraping a URL, we will remove it from the queue and add it to the processed_urls set for later use. # process urls one by one until we exhaust the queuewhile len(new_urls): # move url from the queue to processed url set url = new_urls. popleft() (url) # print the current url print(“Processing%s”% url)Next, add an exception to catch any broken web pages and add them to the broken_urls set for later use:try: response = (url)except(requests. exceptions. MissingSchema, nnectionError, validURL, validSchema): # add broken urls to it’s own set, then continue (url) continueWe then need to get the base URL of the webpage so that we can easily differentiate local and foreign addresses:# extract base url to resolve relative linksparts = urlsplit(url)base = “{}”(parts)strip_base = place(“”, “”)base_url = “{}{}”(parts)path = url[(‘/’)+1] if ‘/’ in else urlInitialize BeautifulSoup to process the HTML document:soup = BeautifulSoup(, “lxml”)Now scrape the web page for all links and sort add them to their corresponding set:for link in nd_all(‘a’): # extract link url from the anchor anchor = [“href”] if “href” in else ‘’if artswith(‘/’): local_link = base_url + anchor (local_link) elif strip_base in anchor: (anchor) elif not artswith(‘’): local_link = path + anchor (local_link) else: (anchor)Since I want to limit my crawler to local addresses only, I add the following to add new URLs to our queue:for i in local_urls: if not i in new_urls and not i in processed_urls: (i)If you want to crawl all URLs use:if not link in new_urls and not link in processed_urls: (link)Warning: The way the program currently works, crawling foreign URL’s will take a VERY long time. You could possibly get into trouble for scraping websites without permission. Use at your own risk! Sample outputHere is all my code:And that should be it. You have just created a simple tool to crawl a website and map all URLs found! In ConclusionFeel free to build upon and improve this code. For example, you could modify the program to search web pages for email addresses or phone numbers as you crawl them. You could even extend functionality by adding command line arguments to provide the option to define output files, limit searches to depth, and much more. Learn about how to create command-line interfaces to accept arguments you have additional recommendations, tips, or resources, please share in the comments! Thanks for reading! If you liked this tutorial and want more content like this, be sure to smash that follow button. ❤️Also be sure to check out my website, Twitter, LinkedIn, and Github.
Learn to code for free. freeCodeCamp’s open source curriculum has helped more than 40, 000 people get jobs as developers. Get started
Frequently Asked Questions about crawler with python
How do you use a crawler in Python?
The basic workflow of a general web crawler is as follows:Get the initial URL. … While crawling the web page, we need to fetch the HTML content of the page, then parse it to get the URLs of all the pages linked to this page.Put these URLs into a queue;More items…•Jan 25, 2021
How do you crawl a URL in Python?
How to build a URL crawler to map a website using PythonVisit a web page.Scrape all unique URL’s found on the webpage and add them to a queue.Recursively process URL’s one by one until we exhaust the queue.Print results.Apr 15, 2019
What is Web crawling and scraping in Python?
Web crawling is a component of web scraping, the crawler logic finds URLs to be processed by the scraper code. A web crawler starts with a list of URLs to visit, called the seed. For each URL, the crawler finds links in the HTML, filters those links based on some criteria and adds the new links to a queue.Dec 11, 2020