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.
This 3-week course covers the fundamentals of interacting with web sites using Python. Students will learn how to fetch web pages and parse useful information out of HTML code. To accomplish this, the requests and beautifulsoup libraries will be covered in some depth, and the pandas library will be used to wrangle the scraped data. Before reading it, please read the warnings in my blog Learning Python: Web Scraping. Scrapy is an application framework for crawling web sites and extracting structured data which can be used for a wide range of useful applications, like data mining, information processing or historical archival. Requests# Well known library for most of the Python developers as a fundamental tool to get raw. This is a step-by-step hands-on tutorial explaining how to scrape websites for information. PROTIP: If an API is not available, scrape (extract/mine) specific information by parsing HTML from websites using the Scrapy web scraping (Spider) framework. Inside a virtual environment.
# 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:
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 -
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:
# 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.
Python Scrapy Github
# 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.
# 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
-s: silent download
-A: user agent flag
# Useful Python packages for web scraping (alphabetical order)
# Making requests and collecting data
Web Scraping Github Python Example
A simple, but powerful package for making HTTP requests.
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.
Web Scraping Python Github
Useful for building web crawlers, where you need something more powerful than using
requests 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
Web Scraping Amazon Using Python Github
# HTML parsing
Web Scraping In Python Github
Query HTML and XML documents, using a number of different parsers (Python's built-in HTML Parser,
Web Scraping Using Selenium Python Github
Processes HTML and XML. Can be used to query and select content from HTML documents via CSS selectors and XPath.