Data Types & Structures

🔤 Data Types – The “kind” of data you're working with

These are the basic building blocks of data.

📦 Common Data Types:

Type	Example	Description
Integer	5, -20, 0	Whole numbers
Float	3.14, -0.99	Decimal numbers
Boolean	True, False	Logical values
String	"hello", '123'	Text data
Date/Time	2025-04-27, 10:30	Temporal data

🗂️ Data Structures – How you organize and store that data

They let you store, access, and manipulate data efficiently.

🔧 Common Data Structures:

Structure	Description	Example (Python-style)
List / Array	Ordered collection, can hold mixed types	[1, 2, 3] or ['a', 'b', 'c']
Tuple	Like a list but immutable (can’t be changed)	(3, 'x')
Dictionary / Hash Map	Key-value pairs	{"name": "Alice", "age": 25}
Set	Unordered, no duplicates	{1, 2, 3}
DataFrame	2D labeled table (used in pandas)	Like a spreadsheet of rows & columns
Matrix	2D array of numbers	[[1, 2], [3, 4]]
Tree / Graph	Hierarchical or network structures (advanced)	Used in file systems, social networks

🧠 How They Work Together:

Data types live inside data structures.
Example: A list of integers → [10, 20, 30]
In a DataFrame, each column has a specific data type.

📊 Real-World Example:

In a student dataset:

{
  "name": "Alex",             # String
  "age": 21,                  # Integer
  "grades": [88, 92, 75],     # List of Integers
  "passed": True              # Boolean
}

Would you like a visual chart or code example showing these in action?

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