Sorting and filtering are fundamental operations when working with data in Python. Sorting arranges elements in a specific order, while filtering extracts elements that meet a condition. Python provides built-in functions and custom approaches to perform these operations efficiently.

Why Sorting and Filtering?

Organizes data for better readability
Improves search efficiency
Helps in data analysis and processing

1. Sorting Data in Python

Python provides several ways to sort data:

Using sorted() (returns a new sorted list)
Using .sort() (modifies the original list)
Sorting with a custom key
Sorting dictionaries
Sorting tuples and lists of tuples

1.1 Sorting Lists

Using `sorted()` – Returns a New Sorted List

numbers = [5, 2, 9, 1, 5, 6]
sorted_numbers = sorted(numbers)
print(sorted_numbers)  # Output: [1, 2, 5, 5, 6, 9]

Using `.sort()` – Sorts In-Place

numbers.sort()
print(numbers)  # Output: [1, 2, 5, 5, 6, 9]

Sorting in Descending Order

numbers = [5, 2, 9, 1, 5, 6]
sorted_numbers = sorted(numbers, reverse=True)
print(sorted_numbers)  # Output: [9, 6, 5, 5, 2, 1]

1.2 Sorting with a Custom Key

You can use the key parameter to define a sorting criterion.

Sorting Strings by Length

words = ["apple", "banana", "kiwi", "grape"]
sorted_words = sorted(words, key=len)
print(sorted_words)  # Output: ['kiwi', 'grape', 'apple', 'banana']

1.3 Sorting Lists of Tuples

Sorting by the Second Element

students = [("Alice", 22), ("Bob", 21), ("Charlie", 23)]
sorted_students = sorted(students, key=lambda x: x[1])
print(sorted_students)  
# Output: [('Bob', 21), ('Alice', 22), ('Charlie', 23)]

1.4 Sorting Dictionaries

Sorting Dictionary Keys

student_scores = {"Alice": 85, "Bob": 90, "Charlie": 78}
sorted_keys = sorted(student_scores)
print(sorted_keys)  # Output: ['Alice', 'Bob', 'Charlie']

Sorting by Dictionary Values

sorted_students = sorted(student_scores.items(), key=lambda x: x[1])
print(sorted_students)  
# Output: [('Charlie', 78), ('Alice', 85), ('Bob', 90)]

2. Filtering Data in Python

Filtering extracts elements that satisfy a condition.
You can filter data using:

List comprehensions
The filter() function
Custom filtering conditions

2.1 Filtering with List Comprehensions

Filtering Even Numbers

numbers = [1, 2, 3, 4, 5, 6]
even_numbers = [num for num in numbers if num % 2 == 0]
print(even_numbers)  # Output: [2, 4, 6]

Filtering Words Longer Than 4 Characters

words = ["apple", "hi", "banana", "cat"]
long_words = [word for word in words if len(word) > 4]
print(long_words)  # Output: ['apple', 'banana']

2.2 Filtering with `filter()` Function

The filter() function applies a function to each element and keeps only the elements that return True.

Filtering Odd Numbers

numbers = [1, 2, 3, 4, 5, 6]
odd_numbers = list(filter(lambda x: x % 2 != 0, numbers))
print(odd_numbers)  # Output: [1, 3, 5]

Filtering Words That Start with “A”

words = ["Apple", "Banana", "Avocado", "Cherry"]
a_words = list(filter(lambda word: word.startswith("A"), words))
print(a_words)  # Output: ['Apple', 'Avocado']

2.3 Filtering Dictionaries

Filtering Students with Scores Above 80

student_scores = {"Alice": 85, "Bob": 90, "Charlie": 78}
top_students = {k: v for k, v in student_scores.items() if v > 80}
print(top_students)  # Output: {'Alice': 85, 'Bob': 90}

3. Sorting and Filtering Combined

Sorting and Filtering a List of Dictionaries

students = [
    {"name": "Alice", "age": 22, "grade": 85},
    {"name": "Bob", "age": 21, "grade": 90},
    {"name": "Charlie", "age": 23, "grade": 78}
]

# Filter students with grade > 80
top_students = [s for s in students if s["grade"] > 80]

# Sort by age
sorted_top_students = sorted(top_students, key=lambda s: s["age"])

print(sorted_top_students)

Output:

[{'name': 'Bob', 'age': 21, 'grade': 90}, {'name': 'Alice', 'age': 22, 'grade': 85}]

4. Performance Considerations

sorted() is preferred over .sort() when you need an unchanged original list
Use list comprehensions for filtering when speed matters
Use filter() when working with large datasets

Method	Best Use Case	Performance
`sorted()`	Sorting new lists	✅ Faster
`.sort()`	In-place sorting	✅ Efficient
List comprehension	Filtering	✅ Best for small data
`filter()`	Filtering	✅ Better for large data

5. When to Use Sorting and Filtering?

✅ Use sorting when ordering is important (e.g., ranking, leaderboards).
✅ Use filtering when removing unwanted data (e.g., filtering low scores).
✅ Combine sorting and filtering for optimized data processing.

🚫 Avoid unnecessary sorting/filtering for very large datasets to improve efficiency.