Python Lesson 1 | BehindTheBall

The video introduces data scraping as a way to collect information directly from websites, giving analysts more flexibility than relying on pre-made CSV files. Using basketball-reference.com as an example, the instructor shows how to inspect a webpage's HTML to locate key data like player stats. Viewers learn the basics of HTML structure—especially table-related tags like <tr> for rows and <td> for data cells—and how data like a player’s age or points per game is embedded in this code. The instructor also previews two Python tools: requests, which fetches a page’s HTML, and beautifulsoup, which parses and extracts specific elements. Though no code is written yet, the focus is on building an understanding of how website structure underpins successful scraping.

What Data Scraping Is: Pulling data from websites instead of using pre-made CSV files.
Website Structure Basics:
- Websites are built using HTML (HyperText Markup Language).
- Important tags include:
  - <tr> = table row
  - <td> = table data
  - <a> = anchor (link)
Developer Tools Introduction:
- How to use the “Inspect” tool in your browser to explore HTML.
- Identifying where specific data (e.g., player stats) lives in the HTML.
Data Scraping Workflow Overview:
- Use the requests library to access a webpage’s HTML.
- Use BeautifulSoup to navigate and extract specific parts of that HTML.

Data Scraping: Lesson 1

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