A workshop with the Massive Data Institute, Georgetown University
When it comes to data collection, web-crawling (i.e., web-scraping, screen-scraping) is a common approach in our increasingly digital era--and a common stumbling block. With such a wide range of tools and languages available (Selenium, Requests, and HTML, to name just a few), developing and implementing a web-crawling pipeline is often a frustrating experience for researchers--especially those without a computer science background.
Whatever your background, this workshop will give you the foundation to use web-crawling in your research. We will tackle common problems including collecting web addresses/URLs (by automated Google search), downloading website copies (with wget), non-scalable website scraping (with requests), and scalable crawling of text (with scrapy). No web-crawling experience is required, but some Python know-how is expected.
- Understand how web-crawling and -scraping are useful for digital data collection
- Build intuitions around the uses and limits of:
- APIs (Application Programming Interfaces)
- Exploiting website structure (HTML/CSS)
- Scalable crawling
- Be familiar with common problems in web-crawling and their fixes, like:
- Nested websites --> vertical crawling (link extraction)
- Getting blocked --> polite pauses
- Gain practice with:
- Collecting domains to scrape
- Scalable and non-scalable website scraping
- Parsing website text (with BeautifulSoup)
- wget, Requests, and Scrapy
We will get our hands dirty implementing an assortment of simple web-crawling tools. To follow along with the code—which is the point—will need some familiarity with Python and Jupyter Notebooks. If you haven't programmed in Python or haven’t used Jupyter Notebooks, please do some self-teaching before this workshop using resources like those listed below.
For simplicity, just click the "Launch Binder" button (at the top of this Readme) to create a virtual environment ready for this workshop. It may take a few minutes; if it takes longer than 10, try again.
If you want to run the code on your computer, you have two options. You could use Anaconda to make installation easy: download Anaconda . Or if you already have Python 3.x installed with the full list of libraries listed under requirements.txt, you're welcome to clone this repository and follow along on your own machine. You can also install all the necessary packages like so:
pip3 install -r requirements.txt
- Slides for day 1 (also in folder above)
- Introduction to Jupyter Notebooks (Real Python)
- Quick Python intro (a Jupyter Notebook)
- Great book on Python (with exercises): Python for Everybody (Charles Severance)
- Official Python Tutorial
- Python tutorials for social scientists (Neal Caren)
- Official Scrapy tutorial
- Blog on using item pipelines in Scrapy
- Storing Scrapy output to MongoDB (humongous database!)
- Examples of using wget to download from websites
- Scale Scrapy with a pre-cooked Docker assemblage: Scrapy Cluster
- Video tutorial on APIs, RSS, and Scraping from PyCon
- Parse HTML, by author of Requests library: requests-HTML
- Extract different parts of URL: furl
- Parse CSS files: cssutils
- Parse common formats in newspaper data: newspaper
- Scrape from the past: Internet Archive Python Library
- Browser automation for handling interactive, JavaScript-heavy pages: Selenium
These are available free to Georgetown students/affiliates (log in here then search for books)
- Popular intro book with Ch. 12 on web-scraping: Automate the Boring Stuff with Python
- Complete scraping intro: Web-scraping with Python
- Detailed look at mechanics and approaches in Scrapy
If you spot a problem with these materials, please make an issue describing the problem or contact Jaren at [email protected]. If you want to suggest additional resources or materials, please branch and make a pull request!
- D-Lab at the University of California, Berkeley
- Summer Institute in Computational Social Science
- Geoff Bacon, especially his Introduction to web scraping workshop
- Rochelle Terman, especially her Web Scraping and Data Management in R summer course
