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36. Comprehensions

1. Introduction​

Comprehensions provide a concise and readable way to create lists, sets, or dictionaries from existing iterables. They often replace simple loops and temporary containers.

2. List Comprehensions: Basic Example​

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

3. List Comprehensions with Condition​

evens = [x for x in range(10) if x % 2 == 0]
print(evens)  # [0, 2, 4, 6, 8]

4. Nested Comprehensions​

pairs = [(i, j) for i in range(2) for j in range(3)]
print(pairs)  # [(0,0), (0,1), (0,2), (1,0), (1,1), (1,2)]

5. Set Comprehensions​

unique_mod3 = {x % 3 for x in range(10)}
print(unique_mod3)  # {0, 1, 2}

6. Dictionary Comprehensions​

squares = {x: x * x for x in range(5)}
print(squares)  # {0:0, 1:1, 2:4, 3:9, 4:16}

7. Generator Expressions​

Generator expressions use () and produce values on demand (lazy evaluation):

gen = (x * x for x in range(3))
for value in gen:
    print(value)

Output:

0
1
4

8. Readability and When to Use​

Comprehensions are great for simple transformations and filters. Avoid stuffing too much logic in a single comprehension — prefer readable code over cleverness.

9. Common Pitfalls​

  • Overly complex comprehensions (hard to read).
  • Reusing the same variable name inside comprehension leading to confusion.
  • Generator expressions are single-use (they are iterators).

10. Next Steps​

✅ You now understand comprehensions for lists, sets, dicts, and generators.
Next chapter: Defining and calling functions.