Introduction to Data Structures in Python

Data structures are essential for organizing, managing, and storing data efficiently. Python provides several built-in data structures that are easy to use and highly versatile. This guide will cover:

Lists
Tuples
Sets
Dictionaries
Advanced Data Structures (Optional)

Data Structures in Python - Lists

A list is an ordered, mutable collection of items. It can hold items of different data types.

Creating a List:

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

Key Operations:

Operation	Example	Output
Access an item	`fruits[1]`	`"banana"`
Add an item	`fruits.append("orange")`	`["apple", "banana", "cherry", "orange"]`
Remove an item	`fruits.remove("banana")`	`["apple", "cherry"]`
Length of list	`len(fruits)`	`3`
Sort the list	`fruits.sort()`	`["apple", "banana", "cherry"]`

Example:

fruits = ["apple", "banana", "cherry"]
fruits.append("orange")
fruits.remove("banana")
print(fruits)  # Output: ['apple', 'cherry', 'orange']

Data Structures in Python -Tuples

A tuple is an ordered, immutable collection of items.

Creating a Tuple:

coordinates = (10, 20, 30)
print(coordinates)

Key Operations:

Operation	Example	Output
Access an item	`coordinates[1]`	`20`
Length of tuple	`len(coordinates)`	`3`
Tuple unpacking	`x, y, z = coordinates`	`x=10, y=20, z=30`

Example:

coordinates = (10, 20, 30)
x, y, z = coordinates
print(x, y, z)  # Output: 10 20 30

Data Structures in Python-Sets

A set is an unordered collection of unique items.

Creating a Set:

unique_numbers = {1, 2, 3, 3, 4}
print(unique_numbers)  # Output: {1, 2, 3, 4}

Key Operations:

Operation	Example	Output
Add an item	`unique_numbers.add(5)`	`{1, 2, 3, 4, 5}`
Remove an item	`unique_numbers.remove(3)`	`{1, 2, 4}`
Check membership	`2 in unique_numbers`	`True`
Union	`set1.union(set2)`	Combined sets
Intersection	`set1.intersection(set2)`	Common items

Example:

set1 = {1, 2, 3}
set2 = {3, 4, 5}
print(set1.union(set2))       # Output: {1, 2, 3, 4, 5}
print(set1.intersection(set2)) # Output: {3}

Data Structures in Python-Dictionaries

A dictionary is an unordered collection of key-value pairs.

Creating a Dictionary:

person = {"name": "Alice", "age": 25, "city": "New York"}
print(person)

Key Operations:

Operation	Example	Output
Access a value	`person["name"]`	`"Alice"`
Add a key-value pair	`person["job"] = "Developer"`	Adds `job` key
Remove a key-value	`del person["age"]`	Removes `age`
Get keys	`person.keys()`	`["name", "city", "job"]`
Get values	`person.values()`	`["Alice", "New York", "Developer"]`

Example:

person = {"name": "Alice", "age": 25}
person["city"] = "New York"
del person["age"]
print(person)  # Output: {'name': 'Alice', 'city': 'New York'}

Advanced Data Structures

Nested Lists and Dictionaries:

Nested List Example:

matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
print(matrix[1][2])  # Output: 6

Nested Dictionary Example:

employees = {
    "emp1": {"name": "Alice", "age": 25},
    "emp2": {"name": "Bob", "age": 30}
}
print(employees["emp1"]["name"])  # Output: Alice

Best Practices for Using Data Structures

Choose the Right Data Structure:
- Use lists for ordered collections.
- Use tuples for immutable data.
- Use sets for unique items.
- Use dictionaries for key-value mappings.
Avoid Nested Structures When Possible:
- Keep data structures simple to enhance readability.
Use List Comprehensions:
- Example:

squares = [x**2 for x in range(5)]
print(squares)  # Output: [0, 1, 4, 9, 16]

Handle Missing Keys in Dictionaries:

Use .get() to handle missing keys:

print(person.get("job", "Not Found"))  # Output: Not Found

Use Built-in Methods:

Take advantage of Python’s powerful built-in methods like sort(), keys(), and intersection().