Company: Pluralsight Author: Ian Ozsvald Full Title: Web Scraping: Python Data Playbook Year: 2019 Language: English Genre: Educational: Web Development Skill Level: Beginner Price: - - Files: MP4 (+ Exercise Files, Slides .PDF) Time: 01:17:05 Video: AVC, 1280 x 720 (1.778) at 30.000 fps, 150 kbps Audio: AAC at 94 Kbps, 2 channels, 44.1 KHz Learn how to tell a compelling graphical data story in a Jupyter Notebook with Seaborn having scraped information from a static web page with BeautifulSoup4 when no API is available. Scrape data from a static web page with BeautifulSoup4 and turn it into a compelling graphical data story in a Jupyter Notebook. In this course, Web Scraping: The Python Data Playbook, you will gain the ability to scrape data and present it graphically. First, you will learn to scrape using the requests module and BeautifulSoup4. Next, you will discover how to write a trustworthy scraping module backed by a unit test. Finally, you will explore how to turn the columns of data in a graphical story that will change the opinions of your colleagues. When you're finished with this course, you will have the skills and knowledge of web scraping needed to create a graphically compelling Jupyter Notebook without the use of an API. Lessons: 1. Course Overview 01. Course Overview 2. Setting Up BeautifulSoup 02. General Strategies for Scraping Web Pages 03. Reviewing Our Target Auto-MPG Web Page 04. The Complicated Difference between Dynamic and Static Web Pages 3. Understanding Your Scraped Data 05. A Primer on HTML and CSS 06. Understanding the HTML, CSS and Structure of Our Target Page 07. Coming up with a Strategy for a More Complicated Web Page 08. Using BeautifulSoup4 to Navigate Our Scraped Data 09. Extracting Information from a Scraped Division 10. Using Selectors as an Alternative to the Find Method 11. Advice and Strategy for Scraping 12. Building the Scraper Module Using PyCharm 13. Dealing with Missing Data during the Scrape 14. Refactoring Our Code and Caching Our Scraped Data 15. Adding a Test to Verify Our Regular Expression Processing 4. Making Scraped Data Usable 16. Exporting Scraped Data to a CSV File 17. Getting a Data Overview with Pandas 18. Exploratory Data Analysis Strategy 19. Reviewing Our Hypothesis 20. Investigating Relationships between MPG and Weight 21. Understanding How Cylinders and Displacement Are Related 22. Looking at MPG over the Years 23. Understanding Brands and Territories with Text Processing 24. Telling a Data Story to Explain Our Discoveries Our members see more. Join us! ------------- Our members see more. Join us!