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:
- Is there any scrape protection set at that site?
- How much data is in that web business directory?
- What kind of queries can I run to find all the directory’s items?
- Online marketplaces
In the marketplaces people offer their products for sale. Similar to garage sales, but online. (eg. eCrater, www.1188.no).
Easy to scrape since they are usually free and do not tend to protect their data. - Business directories
The usually huge online directories targeted at the general audience. (eg. Yellow Pages). They do protect their data to avoid duplication and loss of audience. See some posts on this.
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DataFlowKit review
Recently we encountered a new service that helps users to scrape the modern web 2.0. It’s a simple, comfortable, easy to learn service – https://dataflowkit.com
Let’s first highlight some of its outstanding features:
- Visual online scraper tool: point, click and extract.
- Javascript rendering; any interactive site scrape by headless Chrome run in the cloud
- Open-source back-end
- Scrape a website behind a login form
- Web page interactions: Input, Click, Wait, Scroll, etc.
- Proxy support, incl. Geo-target proxying
- Scraper API
- Follow the direction of robots.txt
- Export results to Google drive, DropBox, MS OneDrive.
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!
If you are considering the Linkedin data scrape legal issues, please refer to the following post: Linkedin lost in court to data analytic company that scrapes Linkedin’s public profiles info
Our brand new version Octoparse 8 (OP 8) just came out a few weeks ago. To help you get a better understanding of what the differences between OP 8 and 7 are, we have included all the updates in this article.
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Oxylabs.io at a glance
Oxylabs.io is an experienced player in the proxy market. In the past few years, they have significantly expanded their proxy pool.
Right now they have a residential proxy pool with over 60M IPs and over 2M datacenter proxies. Their residential proxies cover every country in the world (!) and offer city-level targeting. Oxylabs datacenter proxies come from 82 locations and feature 7850 subnets.
Oxylabs is mainly focused on businesses and it is reflected in their product subscription plans. But recently they have introduced a Fast-Checkout feature, where customers can purchase residential proxies in a few clicks. Together with a recently added smaller plan ($300/month for 20GB of traffic) Oxylabs becomes much more attractive for smaller customers as well.
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.
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NetNut.io Review
The most successful enterprises are always the ones which manage to stay a step ahead of their rivals. And to remain ahead, you have to be able to access the industry information faster and more consistently than anybody else. This is especially true for e-commerce and online retail industries, where the pricing contest is extremely fierce. Thus, the smallest developments in information processes can result in large changes in the outcomes.