Python Data Structures – Strings, Lists, Tuples, Sets, Dictionaries & Arrays

What Are Data Structures in Python?

Data structures are containers that organize and store data efficiently so it can be accessed and modified as needed. Python offers several built-in data structures, each optimized for specific use cases.

Strings – Immutable Text Containers

Strings are sequences of characters used to store text data.

Common operations:

• Indexing & slicing (s[0], s[1:4])
• String methods (upper(), replace(), find())
• Formatting (f"Hello, {name}")

Lists – Ordered, Mutable Collections

Lists are dynamic arrays that can hold items of any type and are mutable.

Key Features:

• Indexing and slicing
• Methods: append(), extend(), pop(), sort()
• Supports nesting and comprehension

Tuples – Ordered, Immutable Collections

Tuples are like lists but immutable, meaning their values cannot change.

Use Cases:

• Storing fixed data
• Returning multiple values from functions
• Hashable for dictionary keys and sets

Sets – Unordered Collections with Unique Elements

Sets are unordered, mutable collections of unique items.

Use Cases:

• Removing duplicates
• Mathematical operations: union, intersection, difference
• Fast membership testing

Dictionaries – Key-Value Mappings

Dictionaries map keys to values and are unordered (ordered as of Python 3.7+).

Real-World Use:

• JSON-like data representation
• Fast lookups
• Methods: get(), items(), update(), pop()

Arrays – Efficient Fixed-Type Sequences

Python arrays (from the array module or NumPy) store homogeneous data more efficiently than lists.

When to Use:

• Numerical computations
• Memory efficiency
• Fixed data types (int, float, etc.)