The post will share with you the difference between the production and development builds of PWA. If you are not familiar to PWA (Progressive Web Application) please visit that blog post.
I’ve met with the challenge that composer failed to load guzzle library file:
https://packagist.org/p/guzzlehttp/guzzle%241f150aaa79afd8bc5d6f08f730634a0d60 f5dfcd1dd4a6fc5263fb4b1cefeb16.json" file could not be downloaded (HTTP/1.1 404 Not Found)
The solution has been the following:
Let me tell you what you already know! Octoparse is a great web scraping tool! But like every great tool, it’s got its limitations. At times, you may wonder if there are any alternatives to Octoparse. We wondered the same and put together this blog to provide you a short list of Octoparse alternatives along with their features and distinguishing factors. Let’s get started!
…we use your personal data so we can provide the best service, tell you about products and services you may be interested in…
These or similar statements are often “tiny printed” at most of modern sites as part of Terms of Service (ToS). Below we share with you what particular data are collected from web users or app users.
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.
- Rate limit
Limit the crawling rate for LinkedIn. The acceptable approximate frequency is: 1 request every second, 60 requests per minute.
- Public pages only
LinkedIn allows for bots only public pages; pages that are private cannot be crawled.
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.
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.
- 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.