List Comprehensions

# Basic syntax
# [expression for item in iterable]

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

# With condition (filter)
# [expression for item in iterable if condition]

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

# With if-else (transform)
# [expr_if_true if condition else expr_if_false for item in iterable]

labels = ["even" if x % 2 == 0 else "odd" for x in range(5)]
# ["even", "odd", "even", "odd", "even"]

# Flatten 2D list
matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
flat = [num for row in matrix for num in row]
# [1, 2, 3, 4, 5, 6, 7, 8, 9]

# Reading order: left to right (outer to inner)
# Equivalent loop:
# for row in matrix:
#     for num in row:
#         flat.append(num)

# Create 2D list (matrix)
grid = [[i * j for j in range(1, 4)] for i in range(1, 4)]
# [[1, 2, 3], [2, 4, 6], [3, 6, 9]]

# Cartesian product
pairs = [(x, y) for x in range(3) for y in range(3)]
# [(0,0), (0,1), (0,2), (1,0), (1,1), (1,2), (2,0), (2,1), (2,2)]

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

# From two lists
keys = ['a', 'b', 'c']
values = [1, 2, 3]
d = {k: v for k, v in zip(keys, values)}
# {'a': 1, 'b': 2, 'c': 3}

# Swap keys and values
original = {'a': 1, 'b': 2, 'c': 3}
swapped = {v: k for k, v in original.items()}
# {1: 'a', 2: 'b', 3: 'c'}

# Filter dict
scores = {'Alice': 85, 'Bob': 92, 'Charlie': 78}
passed = {name: score for name, score in scores.items() if score >= 80}
# {'Alice': 85, 'Bob': 92}

# Transform keys or values
upper_keys = {k.upper(): v for k, v in scores.items()}

# Unique values only
squares = {x**2 for x in range(-5, 6)}
# {0, 1, 4, 9, 16, 25}

# Extract unique first letters
words = ["apple", "banana", "apricot", "cherry"]
first_letters = {word[0] for word in words}
# {'a', 'b', 'c'}

# Generator expression (lazy evaluation)
gen = (x**2 for x in range(1000000))

# Use with functions that consume iterables
total = sum(x**2 for x in range(1000000))

# Memory comparison
import sys
list_comp = [x for x in range(10000)]
gen_expr = (x for x in range(10000))
print(sys.getsizeof(list_comp))  # ~87,000 bytes
print(sys.getsizeof(gen_expr))   # ~112 bytes

# GOOD: Simple transformations
squares = [x**2 for x in range(10)]

# GOOD: Filtering
adults = [p for p in people if p.age >= 18]

# BAD: Side effects (use regular loop)
# [print(x) for x in range(10)]  # Don't do this!

# BAD: Complex logic (use regular loop)
# Hard to read nested comprehensions with multiple conditions