webscraping.pro
Question: What is Selenium web scraping?
Answer: A picture is better than 1000 words:
So, you make a program with Python, PHP, JAVA, Ruby and whatever language you use in order to browse(), select(), click(), submit(), save(), etc., target web pages.
The LinkedIn crawl success rate is low; one request that a bot makes might require several retries to be successful. So, here we share the crucial Linkedin scraping guide lines.
import sys
import requests
URL = 'https://portal.bitcasa.com/login'
client = requests.session()
# Retrieve the CSRF token first
client.get(URL) # sets cookie
if 'csrftoken' in client.cookies:
# Django 1.6 and up
csrftoken = client.cookies['csrftoken']
else:
# older versions
csrftoken = client.cookies['csrf']
# Pass CSRF token both in login parameters (csrfmiddlewaretoken)
# and in the session cookies (csrf in client.cookies)
login_data = dict(username=EMAIL, password=PASSWORD, csrfmiddlewaretoken=csrftoken, next='/')
r = client.post(URL, data=login_data, headers=dict(Referer=URL))
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 today’s web 2.0 many business websites utilize JavaScript to protect their content from web scraping or any other undesired bot visits. In this article we share with you the theory and practical fulfillment of how to scrape js-dependent/js-protected websites.
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.