We're data scientists ourselves, and have very often found web scraping to be a powerful tool to have in your arsenal, as many data science projects start with the first step of obtaining an appropriate data set, so why not utilize the treasure trove of information the web provides. As such, we've strived to offer a guide that. Web scraping is one of the most powerful things you can learn, so let's Learn to scrape some data from some websites using Python! Basic introduction you could probably skip that I copied from my other article. First things first, we will need to have Python installed. Web Scraping for Data science. The final chapter in the book contains fifteen larger, 'real-life' examples of web scrapers, showing you how the concepts seen throughout the book 'fall together' and interact, as well as to hint towards some interesting data science oriented use cases using web scraped data.
The final chapter in the book contains fifteen larger, 'real-life' examples of web scrapers, showing you how the concepts seen throughout the book 'fall together' and interact, as well as to hint towards some interesting data science oriented use cases using web scraped data.
The following examples are included and explained in the book:
Data Scraping Tools
- Scraping Hacker News
- Using the Hacker News API
- Quotes to Scrape
- Books to Scrape
- Scraping GitHub Stars
- Scraping Mortgage Rates
- Scraping and Visualizing IMDB Ratings
- Scraping IATA Airline Information
- Scraping and Analyzing Web Forum Interactions
- Collecting and Clustering a Fashion Data Set
- Sentiment Analysis of Scraped Amazon Reviews
- Scraping and Analyzing News Articles
- Scraping and Analyzing a Wikipedia Graph
- Scraping and Visualizing a Board Members Graph
- Breaking CAPTCHA's Using Deep Learning
Web Scraping Data Mining
Web Scraping Data Science
The source code for the fifteen real-life examples included in the book can be found at this GitHub repository.
