PLU | Web Scraping: Python Data Playbook (2019 EN)

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  1. Kanka

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    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


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