Advanced web scraping tools Scrapy is a Python framework for large scale web scraping. It gives you all the tools you need to efficiently extract data from websites, process them as you want,. Scraping websites using the requests library to make GET and POST requests, and the lxml library to process HTML is a good way to learn basic web scraping techniques. It is a good choice for small to medium size projects. What Is Web Scraping? The automated gathering of data from the Internet is nearly as old as the Internet itself. Although web scraping is not a new term, in years past the practice has been more commonly known as screen scraping, data mining, web harvesting, or similar variations. General consensus today seems to favor web scraping, so that is.
Mar 05, 2021 Scraping dynamic HTML in Python with Selenium. When a web page is opened in a browser, the browser will automatically execute JavaScript and generate dynamic HTML content. It is common to make HTTP request to retrieve the web pages. However, if the web page is dynamically generated by JavasSript, a HTTP request will only get source codes of the.
| '' |
| Example of web scraping using Python and BeautifulSoup. |
| Sraping ESPN College Football data |
| http://www.espn.com/college-sports/football/recruiting/databaseresults/_/sportid/24/class/2006/sort/school/starsfilter/GT/ratingfilter/GT/statuscommit/Commitments/statusuncommit/Uncommited |
| The script will loop through a defined number of pages to extract footballer data. |
| '' |
| frombs4importBeautifulSoup |
| importrequests |
| importos |
| importos.path |
| importcsv |
| importtime |
| defwriterows(rows, filename): |
| withopen(filename, 'a', encoding='utf-8') astoWrite: |
| writer=csv.writer(toWrite) |
| writer.writerows(rows) |
| defgetlistings(listingurl): |
| '' |
| scrap footballer data from the page and write to CSV |
| '' |
| # prepare headers |
| headers= {'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:52.0) Gecko/20100101 Firefox/52.0'} |
| # fetching the url, raising error if operation fails |
| try: |
| response=requests.get(listingurl, headers=headers) |
| exceptrequests.exceptions.RequestExceptionase: |
| print(e) |
| exit() |
| soup=BeautifulSoup(response.text, 'html.parser') |
| listings= [] |
| # loop through the table, get data from the columns |
| forrowsinsoup.find_all('tr'): |
| if ('oddrow'inrows['class']) or ('evenrow'inrows['class']): |
| name=rows.find('div', class_='name').a.get_text() |
| hometown=rows.find_all('td')[1].get_text() |
| school=hometown[hometown.find(',')+4:] |
| city=hometown[:hometown.find(',')+4] |
| position=rows.find_all('td')[2].get_text() |
| grade=rows.find_all('td')[4].get_text() |
| # append data to the list |
| listings.append([name, school, city, position, grade]) |
| returnlistings |
| if__name__'__main__': |
| '' |
| Set CSV file name. |
| Remove if file alreay exists to ensure a fresh start |
| '' |
| filename='footballers.csv' |
| ifos.path.exists(filename): |
| os.remove(filename) |
| '' |
| Url to fetch consists of 3 parts: |
| baseurl, page number, year, remaining url |
| '' |
| baseurl='http://www.espn.com/college-sports/football/recruiting/databaseresults/_/page/' |
| page=1 |
| parturl='/sportid/24/class/2006/sort/school/starsfilter/GT/ratingfilter/GT/statuscommit/Commitments/statusuncommit/Uncommited' |
| # scrap all pages |
| whilepage<259: |
| listingurl=baseurl+str(page) +parturl |
| listings=getlistings(listingurl) |
| # write to CSV |
| writerows(listings, filename) |
| # take a break |
| time.sleep(3) |
| page+=1 |
| ifpage>1: |
| print('Listings fetched successfully.') |
Web Scraping Python Code
Python Scrapy Github