# Syntax: [expression for item in iterable]

# Traditional loop
squares = []
for x in range(10):
    squares.append(x ** 2)

# List comprehension (same result)
squares = [x ** 2 for x in range(10)]
print(squares)  # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

# [expression for item in iterable if condition]

# Even numbers only
evens = [x for x in range(20) if x % 2 == 0]
print(evens)  # [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

# Positive numbers
numbers = [-3, -1, 0, 2, 5, -4, 8]
positive = [n for n in numbers if n > 0]
print(positive)  # [2, 5, 8]

# Filter strings
words = ["hello", "", "world", "", "python"]
non_empty = [w for w in words if w]
print(non_empty)  # ['hello', 'world', 'python']

# Multiple conditions
nums = range(100)
result = [x for x in nums if x % 2 == 0 if x % 3 == 0]
print(result)  # [0, 6, 12, 18, 24, 30, ...]  (divisible by both 2 and 3)

# [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(6)]
print(labels)  # ['even', 'odd', 'even', 'odd', 'even', 'odd']

# Absolute values
numbers = [-3, -1, 0, 2, -5, 8]
absolute = [x if x >= 0 else -x for x in numbers]
print(absolute)  # [3, 1, 0, 2, 5, 8]

# Grade classification
scores = [92, 45, 78, 88, 35, 67]
results = ["pass" if s >= 50 else "fail" for s in scores]
print(results)  # ['pass', 'fail', 'pass', 'pass', 'fail', 'pass']

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

# Equivalent nested loop:
# for row in matrix:
#     for num in row:
#         flat.append(num)

# All combinations
colors = ["red", "blue"]
sizes = ["S", "M", "L"]
combos = [(c, s) for c in colors for s in sizes]
print(combos)
# [('red', 'S'), ('red', 'M'), ('red', 'L'),
#  ('blue', 'S'), ('blue', 'M'), ('blue', 'L')]

# 3x3 matrix of zeros
matrix = [[0 for _ in range(3)] for _ in range(3)]
print(matrix)  # [[0, 0, 0], [0, 0, 0], [0, 0, 0]]

# Identity matrix
identity = [[1 if i == j else 0 for j in range(4)] for i in range(4)]
for row in identity:
    print(row)
# [1, 0, 0, 0]
# [0, 1, 0, 0]
# [0, 0, 1, 0]
# [0, 0, 0, 1]

# Multiplication table
table = [[i * j for j in range(1, 6)] for i in range(1, 6)]
for row in table:
    print(row)

# {key_expr: value_expr for item in iterable}

# Squares dictionary
squares = {x: x**2 for x in range(6)}
print(squares)  # {0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}

# From two lists
names = ["Alice", "Bob", "Charlie"]
ages = [25, 30, 35]
people = {name: age for name, age in zip(names, ages)}
print(people)  # {'Alice': 25, 'Bob': 30, 'Charlie': 35}

# Filter and transform
prices = {"apple": 1.20, "banana": 0.50, "cherry": 2.00, "date": 3.50}
affordable = {k: v for k, v in prices.items() if v < 2.0}
print(affordable)  # {'apple': 1.20, 'banana': 0.50}

# Invert a dictionary
inverted = {v: k for k, v in prices.items()}
print(inverted)

# Word lengths
sentence = "python list comprehensions are awesome"
lengths = {word: len(word) for word in sentence.split()}
print(lengths)
# {'python': 6, 'list': 4, 'comprehensions': 14, 'are': 3, 'awesome': 7}

# {expression for item in iterable}

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

# Unique first letters
words = ["apple", "avocado", "banana", "blueberry", "cherry"]
first_letters = {w[0] for w in words}
print(first_letters)  # {'a', 'b', 'c'}

# Generator expression (round brackets)
gen = (x**2 for x in range(10))
print(type(gen))  # <class 'generator'>

# Use in functions that accept iterables
total = sum(x**2 for x in range(10))
print(total)  # 285

# Memory efficient for large datasets
# List: stores all values in memory
big_list = [x**2 for x in range(1_000_000)]  # Uses lots of memory

# Generator: computes on demand
big_gen = (x**2 for x in range(1_000_000))   # Uses minimal memory

# Check if any/all
has_negative = any(x < 0 for x in [1, -2, 3])
print(has_negative)  # True

all_positive = all(x > 0 for x in [1, 2, 3])
print(all_positive)  # True

# Find max
longest = max(["apple", "banana", "cherry"], key=lambda x: len(x))
print(longest)  # banana

# Process a list of student records
students = [
    {"name": "Alice", "scores": [92, 88, 95]},
    {"name": "Bob", "scores": [78, 82, 75]},
    {"name": "Charlie", "scores": [95, 98, 92]},
    {"name": "Diana", "scores": [65, 70, 68]},
]

# Calculate averages
averages = {
    s["name"]: sum(s["scores"]) / len(s["scores"])
    for s in students
}
print(averages)
# {'Alice': 91.67, 'Bob': 78.33, 'Charlie': 95.0, 'Diana': 67.67}

# Honor roll (average >= 85)
honor_roll = [
    s["name"] for s in students
    if sum(s["scores"]) / len(s["scores"]) >= 85
]
print(f"Honor Roll: {honor_roll}")
# Honor Roll: ['Alice', 'Charlie']

# Clean and process text
raw_data = ["  Alice  ", "BOB", "  charlie", "", "  DIANA  ", "  "]

# Clean: strip, title case, filter empty
cleaned = [name.strip().title() for name in raw_data if name.strip()]
print(cleaned)  # ['Alice', 'Bob', 'Charlie', 'Diana']

# Extract emails from text
lines = [
    "Contact: alice@example.com",
    "No email here",
    "Send to: bob@test.org",
    "Info at charlie@demo.com"
]
emails = [
    word for line in lines
    for word in line.split()
    if "@" in word
]
print(emails)  # ['alice@example.com', 'bob@test.org', 'charlie@demo.com']

# DON'T: complex logic with side effects
# BAD - hard to read
result = [print(x) or x**2 for x in range(5) if x % 2 == 0]

# GOOD - use a regular loop
for x in range(5):
    if x % 2 == 0:
        print(x)

# DON'T: extremely long comprehensions
# BAD
r = [transform(x) for x in data if validate(x) and check_permission(x) and not is_deleted(x)]

# GOOD - break it down
valid = [x for x in data if validate(x)]
permitted = [x for x in valid if check_permission(x)]
active = [x for x in permitted if not is_deleted(x)]
result = [transform(x) for x in active]