Python Lesson 3 | BehindTheBall

In this lesson, the instructor teaches how to extract actual player statistics from an NBA stats webpage using Python and BeautifulSoup. The focus is on understanding the HTML structure of the site and organizing the scraped data into a format that can later be saved or analyzed.

The instructor introduces the idea of nested lists, where each player’s stats are stored in a list, and all player lists are stored together in one larger list.

What is a Soup Object?
- Created using BeautifulSoup(html, 'html.parser')
- It turns raw HTML into something Python can easily search and work with.
HTML Structure Matters
- <tr> = Table Row → each one represents a new player
- <td> = Table Data → each one holds one stat (like points, age, etc.)
- <td> elements are found inside <tr> elements
Step-by-Step Scraping Plan:
- Find all <tr> tags – each is a player row.
- Loop through each row (<tr>).
- Inside each row, find all <td> tags – the stats.
- Extract the text from each <td> using .text.
- Store the stats in a list, then add that list to a larger list.
Nested Lists for Clean Data:
- Each player’s stats = one list.
- All player lists = one big list (all_player_stats).
- This structure makes it easy to turn into a table or CSV later.
Why Use .text?
- .text pulls just the number or text inside the HTML tags.
- Without it, the raw HTML (like <td>33.6</td>) would be included, which is messy.

Data Scraping: Lesson 3

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