We want to share with you how to scrape text and store it as Pandas data frame using BeautifulSoup (Python). The code below works to store html li items in the ‘engine, ‘trans’, ‘colour’ and ‘interior’ columns.
from bs4 import BeautifulSoup
import pandas as pd
import requests
main_url = "https://www.example.com/"
def getAndParseURL(url):
result = requests.get(url)
soup = BeautifulSoup(result.text, 'html.parser')
return(soup)
soup = getAndParseURL(main_url)
ul = soup.select('ul[class="list-inline lot-breakdown-list"] li', recursive=True)
lis_e = []
for li in ul:
lis = []
lis.append(li.contents[1])
lis_e.extend(lis)
engine.append(lis_e[0])
trans.append(lis_e[1])
colour.append(lis_e[2])
interior.append(lis_e[3])
scraped_data = pd.DataFrame({'engine': engine,
'transmission': trans, 'colour': colour,
'interior': interior})
By default, Beautiful Soup searches through all of the child elements. So, setting recursive = False (line 13) will restrict the search to the first found element and its child only.
The code was provided by Ahmed Soliman.
I came across this tool a few weeks ago, and wanted to share it with you. So far I have not tested it myself, but it is a simple concept- Safely download web pages without the fear of overloading websites or getting banned. You write a crawler script using scruping hub, and they will run through there IP proxies and take care of the technical problems of crawling.
In this post I want to let you how I’ve managed to complete the challenge of scraping a site with Google Apps Script (GAS).