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Python for AILesson 3
Lesson 38 min

Lists & Dicts

Python's two workhorse data structures, plus comprehensions.

What you will learn
  • ✓Lists vs dictionaries
  • ✓List comprehensions
  • ✓When to use each

Explanation

A list is an ordered collection: [82, 95, 71]. A dictionary maps keys to values: {'name': 'Mirza', 'score': 95} — perfect for labelled data.

List comprehensions transform or filter a list in one readable line, which you will see constantly in data code.

These two structures cover most everyday data handling before you reach NumPy and Pandas.

Code Example

python
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scores = [82, 95, 71, 60]
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high = [s for s in scores if s >= 80]   # [82, 95]
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student = {'name': 'Mirza', 'score': 95}
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print(student['name'], student['score'])
Real-world use

Cleaning a dataset often starts as plain list/dict work before you load it into a DataFrame.

Common mistakes
  • • Using a list when a dictionary (key lookup) would be far clearer and faster.
Practice

Given a list of prices, use a comprehension to keep only those under 50.

Knowledge check
0/2 answered

1. Which structure is best for labelled key/value data?

2. What does [s for s in scores if s >= 80] do?

Answer all questions to check.
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