🧾 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.)
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