Week 3: Data Types and Structures in Python

Welcome to Week 3!

Theme: Data Types and Structures in Python

This week we’ll explore how Python stores and organizes data.

You’ll learn:

• The building blocks: strings, integer, float, booleans

• How to use lists, tuples, dictionaries, and sets

• Real-world examples in data science

Why Data Types Matter

Every variable in Python has a type that determines:

• What kind of data it holds

• What operations you can perform
  

Knowing the type helps you avoid errors and write better code.

Python’s Built-in Data Types

Common types in Python:

We use these constantly in Python scripts and data analysis.

Python’s Data Structures

Python has 4 main ways to group multiple items:

Each has its own strengths!

Lists

fruits = ["apple", "banana", "cherry"]
print(fruits[1])  # banana

You can:

• Add: fruits.append("orange")

• Remove: fruits.remove("apple")

• Loop: for f in fruits: print(f)
  

Great for datasets and sequences!

Tuples and When to Use Them

Tuples are like lists but immutable (unchangeable):

coordinates = (10.0, 20.5)

We use tuples for:

• Fixed values (e.g. coordinates, settings)

• Keys in dictionaries
  

You can’t append() or remove() from a tuple.

Dictionaries

person = {
  "name": "Alice",
  "age": 25,
  "city": "Lagos"
}

Access with keys:

print(person["name"])  

Ideal for:

• JSON-like data

• Mapping IDs to values

• Storing structured info

Sets

colors = {"red", "green", "blue"}

We use sets to:

• Remove duplicates

• Compare groups: A & B, A | B

• Check membership quickly