Web scraping & data mining
My goal was to retrieve data from a web business directory.
Since the business directories scrape is the most challenging task (beside SERP scrape) there are some basic questions for me to answer:
In this post we want to share with you a new useful JAVA library that helps to crawl and scrape Linkedin companies. Get business directories scraped!
The JS loading page is usually scraped by Selenium or another browser emulator. Yet, for a certain shopping website we’ve
found a way to perform a pure Python requests scrape.
Recently I needed to make a bulk insert into db with prepared statement query. The task was to do it so that if one record failed one can rollback all records and return an error. That way no data is affected by faulty code and/or wrong data provided.
I am trying to scrape the page https://tienda.mercadona.es/categories/112 and I have installed the docker and followed all the required steps given in the post. Splash works well, but the spyder does not and I don’t know why. The IP of the splash_url is correct but I can’t see in the response object when I write scrapy shell “webpage” the complete page, ie, the page has not rendered correctly.
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})
The code was provided by Ahmed Soliman.
I recently got a question and it looked like this : how to download a file from a link in Python?
“I need to go to every link which will open a website and that would have the download file “Export offers to XML”. This link is javascript enabled.”
The DOMXPath class is a convenient and popular means to parse HTML content with XPath.
After I’ve done a simple PHP/cURL scraper using Regex some have reasonably mentioned a request for a more efficient scrape with XPath. So, instead of parsing the content with Regex, I used DOMXPath class methods.
We’ve done the Linkedin scraper that downloades the free study courses. They include text data, exercise files and 720HD videos. The code does not represent the pure Linkedin scraper, a business directory data extractor. Yet, you might grasp the main thoughts and useful techniques for your Linkedin scraper development. 