Web scraping is an automated, programmatic process through which data can be constantly 'scraped' off webpages. Also known as screen scraping or web harvesting, web scraping can provide instant data from any publicly accessible webpage. On some websites, web scraping may be illegal.
- Python Web Scraping Tools
- Web Scraping Using Python Github Example
- Web Scraping Using Python Beautifulsoup Github
# Scraping using the Scrapy framework
First you have to set up a new Scrapy project. Enter a directory where you'd like to store your code and run:
The most popular web scraping extension. Start scraping in minutes. Automate your tasks with our Cloud Scraper. No software to download, no coding needed. I've recently had to perform some web scraping from a site that required login. It wasn't very straight forward as I expected so I've decided to write a tutorial for it. For this tutorial we will scrape a list of projects from our bitbucket account. The code from this tutorial can be found on my Github. We will perform the following steps. Hands-on Web Scraping with Python: Perform advanced scraping operations using various Python libraries and tools such as Selenium, Regex, and others, by Anish Chapagain (ISBN: 9392) Learning Selenium Testing Tools with Python: A practical guide on automated web testing with Selenium using Python, by Unmesh Gundecha (ISBN: 9506).
To scrape we need a spider. Spiders define how a certain site will be scraped. Here's the code for a spider that follows the links to the top voted questions on StackOverflow and scrapes some data from each page (source):
Save your spider classes in the projectNamespiders directory. In this case - projectNamespidersstackoverflow_spider.py.
Now you can use your spider. For example, try running (in the project's directory):
# Basic example of using requests and lxml to scrape some data
# Maintaining web-scraping session with requests
It is a good idea to maintain a web-scraping session to persist the cookies and other parameters. Additionally, it can result into a performance improvement because requests.Session reuses the underlying TCP connection to a host:
Python Web Scraping Tools
# Scraping using Selenium WebDriver
Some websites don't like to be scraped. In these cases you may need to simulate a real user working with a browser. Selenium launches and controls a web browser.
Selenium can do much more. It can modify browser's cookies, fill in forms, simulate mouse clicks, take screenshots of web pages, and run custom JavaScript.
# Scraping using BeautifulSoup4
# Modify Scrapy user agent
Sometimes the default Scrapy user agent ('Scrapy/VERSION (+http://scrapy.org)') is blocked by the host. To change the default user agent open settings.py, uncomment and edit the following line to what ever you want.
For example
# Simple web content download with urllib.request
The standard library module urllib.request can be used to download web content:
A similar module is also available in Python 2.
# Scraping with curl
imports:
Downloading:
-s: silent download
-A: user agent flag
Can you run microsoft office on chromebook. Parsing:
# Remarks
# Useful Python packages for web scraping (alphabetical order)
# Making requests and collecting data
A simple, but powerful package for making HTTP requests.
Caching for requests; caching data is very useful. In development, it means you can avoid hitting a site unnecessarily. While running a real collection, it means that if your scraper crashes for some reason (maybe you didn't handle some unusual content on the site..? maybe the site went down..?) you can repeat the collection very quickly from where you left off.
Useful for building web crawlers, where you need something more powerful than using requests Costco switch. and iterating through pages.
Python bindings for Selenium WebDriver, for browser automation. Using requests to make HTTP requests directly is often simpler for retrieving webpages. However, this remains a useful tool when it is not possible to replicate the desired behaviour of a site using requests alone, particularly when JavaScript is required to render elements on a page.
Web Scraping Using Python Github Example
-A: user agent flag
Can you run microsoft office on chromebook. Parsing:
# Remarks
# Useful Python packages for web scraping (alphabetical order)
# Making requests and collecting data
A simple, but powerful package for making HTTP requests.
Caching for requests; caching data is very useful. In development, it means you can avoid hitting a site unnecessarily. While running a real collection, it means that if your scraper crashes for some reason (maybe you didn't handle some unusual content on the site..? maybe the site went down..?) you can repeat the collection very quickly from where you left off.
Useful for building web crawlers, where you need something more powerful than using requests Costco switch. and iterating through pages.
Python bindings for Selenium WebDriver, for browser automation. Using requests to make HTTP requests directly is often simpler for retrieving webpages. However, this remains a useful tool when it is not possible to replicate the desired behaviour of a site using requests alone, particularly when JavaScript is required to render elements on a page.
Web Scraping Using Python Github Example
# HTML parsing
Query HTML and XML documents, using a number of different parsers (Python's built-in HTML Parser,html5lib, lxml or lxml.html)
Web Scraping Using Python Beautifulsoup Github
Processes HTML and XML. Can be used to query and select content from HTML documents via CSS selectors and XPath.
