Data Structures

Introduction

Data structures are fundamental to programming, providing efficient ways to organize and manage data. Python offers various data structures tailored to specific use cases.

Practical Steps

• Choose the Right Structure: Identify the type of data you're working with and select the appropriate structure.
• Create Structures: Use built-in functions (e.g., list(), tuple(), dict(), set()) to create instances of data structures.
• Operate on Structures: Perform operations like adding, removing, and accessing elements using standard methods.
• Access Specific Data: Use indexing (e.g., []) or key lookup (e.g., get()) to retrieve data from structures.

Key Concepts

• Python Lists: Ordered collections of elements that can be modified (mutable).
• Python Tuples: Immutable ordered collections of elements that cannot be changed.
• Python Dictionaries: Collections of key-value pairs that map unique keys to values.
• Python Sets: Unordered collections of unique elements that have no duplicates.

Simple Python Example

# Create data structures
my_list = ['apple', 'banana', 'cherry']
my_tuple = ('apple', 'banana', 'cherry')
my_dict = {'apple': 'red', 'banana': 'yellow', 'cherry': 'red'}
my_set = {'apple', 'banana', 'cherry'}

# Operate on structures
my_list.append('mango')
my_dict['orange'] = 'orange'

# Access data
print(my_list[2])  # Output: 'cherry'
print(my_dict['banana'])  # Output: 'yellow'