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Python Set Comprehension

Introduction​

Set comprehension is a powerful and elegant Python feature that allows you to create sets using a concise syntax. It follows a similar pattern to list comprehensions, but creates a set instead of a list. This feature was introduced in Python 2.7 and provides a more readable and efficient way to generate sets compared to traditional methods.

Set comprehensions are particularly useful when you need to:

  • Create a set from an existing iterable
  • Filter elements from an iterable
  • Transform elements while adding them to a set
  • Ensure uniqueness in your resulting collection

In this tutorial, we'll explore how set comprehensions work, their syntax, and practical applications.

Basic Syntax​

Here's the basic syntax of a set comprehension:

python
{expression for item in iterable}

This creates a set where each element is the result of the expression for each item in the iterable.

Let's start with a simple example:

python
# Creating a set of squares from 0 to 9
squares = {x**2 for x in range(10)}
print(squares)

Output:

{0, 1, 4, 9, 16, 25, 36, 49, 64, 81}

Notice that the output is enclosed in curly braces {}, indicating it's a set. Also, the order might appear different when you run the code since sets are unordered collections.

Filtering with Conditions​

You can add conditions to filter which items are included in the set:

python
# Creating a set of even squares from 0 to 9
even_squares = {x**2 for x in range(10) if x % 2 == 0}
print(even_squares)

Output:

{0, 4, 16, 36, 64}

Comparing with Traditional Methods​

Let's compare set comprehension with traditional methods:

python
# Traditional approach using a for loop
squares_traditional = set()
for x in range(10):
    squares_traditional.add(x**2)
print(squares_traditional)

# Set comprehension approach
squares_comprehension = {x**2 for x in range(10)}
print(squares_comprehension)

Both approaches yield the same result, but the set comprehension is more concise and often more readable.

Multiple Conditions and Nested Loops​

Set comprehensions can include multiple conditions and nested loops:

python
# Multiple conditions
numbers = {x for x in range(100) if x % 2 == 0 if x % 5 == 0}
print(numbers)

# Nested loops
coordinate_pairs = {(x, y) for x in range(3) for y in range(3)}
print(coordinate_pairs)

Output:

{0, 10, 20, 30, 40, 50, 60, 70, 80, 90}
{(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1), (2, 2)}

If-Else in Set Comprehensions​

You can use if-else constructs within the expression part of a set comprehension:

python
# If-else in the expression
parity = {"even" if x % 2 == 0 else "odd" for x in range(5)}
print(parity)

Output:

{'odd', 'even'}

Notice that the set only contains two elements: 'odd' and 'even'. This is because sets only store unique values. If we had used a list comprehension, we would have seen all five values.

Practical Examples​

Example 1: Extracting Unique Words from a Text​

python
text = "Python is powerful and Python is also easy to learn"
unique_words = {word.lower() for word in text.split()}
print(unique_words)

Output:

{'powerful', 'to', 'python', 'and', 'also', 'is', 'learn', 'easy'}

Example 2: Converting Case of Characters​

python
mixed_case = "PyThoN"
character_variants = {c.lower() for c in mixed_case} | {c.upper() for c in mixed_case}
print(character_variants)

Output:

{'P', 'Y', 'T', 'H', 'O', 'N', 'p', 'y', 't', 'h', 'o', 'n'}

Example 3: Finding Unique Vowels in a String​

python
sentence = "The quick brown fox jumps over the lazy dog"
vowels = {char for char in sentence.lower() if char in 'aeiou'}
print(vowels)

Output:

{'a', 'e', 'i', 'o', 'u'}

Example 4: Generating a Set of Prime Numbers​

python
def is_prime(n):
    if n <= 1:
        return False
    if n <= 3:
        return True
    if n % 2 == 0 or n % 3 == 0:
        return False
    i = 5
    while i * i <= n:
        if n % i == 0 or n % (i + 2) == 0:
            return False
        i += 6
    return True

primes = {x for x in range(100) if is_prime(x)}
print(primes)

Output:

{2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97}

Performance Considerations​

Set comprehensions are not just more concise but often more efficient than equivalent loop-based approaches. Here's a simple benchmark:

python
import time

# Using for loop
start = time.time()
result_loop = set()
for i in range(1000000):
    result_loop.add(i * 2)
loop_time = time.time() - start

# Using set comprehension
start = time.time()
result_comp = {i * 2 for i in range(1000000)}
comp_time = time.time() - start

print(f"Loop time: {loop_time:.5f} seconds")
print(f"Comprehension time: {comp_time:.5f} seconds")
print(f"Set comprehension is {loop_time/comp_time:.2f}x faster")

Output (will vary by machine):

Loop time: 0.12345 seconds
Comprehension time: 0.09876 seconds
Set comprehension is 1.25x faster

Common Pitfalls​

1. Creating Empty Sets​

Remember that empty curly braces {} create an empty dictionary, not an empty set. To create an empty set, you should use set().

python
empty_dict = {}
empty_set = set()

print(type(empty_dict))
print(type(empty_set))

Output:

<class 'dict'>
<class 'set'>

2. Mutable Elements​

Sets can only contain hashable (immutable) elements. This means you cannot have lists or dictionaries as elements in a set.

python
# This will cause an error
# error_set = {[1, 2], [3, 4]}

# This is fine
valid_set = {(1, 2), (3, 4)}
print(valid_set)

Output:

{(1, 2), (3, 4)}

Summary​

Set comprehensions offer a concise and efficient way to create sets in Python. They follow a similar syntax to list comprehensions but produce sets, which are unordered collections of unique elements. Key points to remember:

  • Basic syntax: {expression for item in iterable}
  • You can add conditions with if statements
  • Multiple nested loops are supported
  • Set comprehensions are often more readable and efficient than traditional loops
  • Sets only store unique values, which can be useful or a limitation depending on your needs

By mastering set comprehensions, you'll be able to write more Pythonic code that is both concise and efficient.

Exercises​

  1. Create a set comprehension that generates the set of all perfect squares up to 100.
  2. Write a set comprehension that extracts all characters that appear in both string A and string B.
  3. Create a set of all possible combinations of two dice rolls that sum to 7.
  4. Write a set comprehension that finds all unique file extensions in a list of filenames.
  5. Create a set of tuples (x, y) where x ranges from 1 to 5, y ranges from 1 to 5, and x ≤ y.

Additional Resources​


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