[expression for item in iterable if condition]

numbers = [1, 2, 3, 4, 5]
squares = [x**2 for x in numbers]
print(squares)  # [1, 4, 9, 16, 25]

squares = []
for x in numbers:
    squares.append(x**2)

matrix = [
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
]

flattened = [num for row in matrix for num in row]
print(flattened)  # [1, 2, 3, 4, 5, 6, 7, 8, 9]

even_squares = [num**2 for row in matrix for num in row if num % 2 == 0]
print(even_squares)  # [4, 16, 36, 64]

result = []
for row in matrix:
    for num in row:
        if num % 2 == 0:
            result.append(num**2)

cube = [
    [[1, 2, 3], [4, 5, 6]],
    [[7, 8, 9], [10, 11, 12]]
]

large_nums = [val for layer in cube for row in layer for val in row if val > 5]
print(large_nums)  # [6, 7, 8, 9, 10, 11, 12]

class_data = [
    # 班級1
    [
        {"name": "張三", "chinese": 85, "math": 90, "english": 78},
        {"name": "李四", "chinese": 92, "math": 88, "english": 95}
    ],
    # 班級2
    [
        {"name": "王五", "chinese": 76, "math": 82, "english": 80},
        {"name": "趙六", "chinese": 90, "math": 94, "english": 88}
    ]
]

top_math_students = [
    student["name"] 
    for class_list in class_data 
    for student in class_list 
    if student["math"] > 90
]

print(top_math_students)  # ['李四', '趙六']

# 所有科目的總分和人數(shù)
total_chinese = sum(student["chinese"] for class_list in class_data for student in class_list)
total_math = sum(student["math"] for class_list in class_data for student in class_list)
total_english = sum(student["english"] for class_list in class_data for student in class_list)

count = sum(len(class_list) for class_list in class_data)

avg_chinese = total_chinese / count
avg_math = total_math / count
avg_english = total_english / count

print(f"語文平均分: {avg_chinese:.1f}")
print(f"數(shù)學平均分: {avg_math:.1f}")
print(f"英語平均分: {avg_english:.1f}")

語文平均分: 85.5
數(shù)學平均分: 88.5
英語平均分: 87.5

def is_pass(score):
    return score >= 60

# 提取所有及格的科目分數(shù)，并轉為百分制字符串
pass_scores = [
    f"{subject}:{score} (及格)" 
    for class_list in class_data 
    for student in class_list 
    for subject, score in student.items() 
    if subject != "name" and is_pass(score)
]

print(pass_scores)

['chinese:85 (及格)', 'math:90 (及格)', 'english:78 (及格)', 'chinese:92 (及格)', 'math:88 (及格)', 'english:95 (及格)', 'math:82 (及格)', 'english:80 (及格)', 'chinese:90 (及格)', 'math:94 (及格)', 'english:88 (及格)']

import timeit

# 準備測試數(shù)據(jù)
data = [[i * j for j in range(100)] for i in range(100)]

# 傳統(tǒng)方式
def traditional():
    result = []
    for row in data:
        for num in row:
            if num % 2 == 0:
                result.append(num * 2)
    return result

# 列表推導式
def comprehension():
    return [num * 2 for row in data for num in row if num % 2 == 0]

# 性能測試
time_traditional = timeit.timeit(traditional, number=1000)
time_comprehension = timeit.timeit(comprehension, number=1000)

print(f"傳統(tǒng)循環(huán)耗時: {time_traditional:.4f}s")
print(f"列表推導式耗時: {time_comprehension:.4f}s")

# 錯誤：內外層順序反了
wrong = [num for num in row for row in matrix]  # 報錯！row未定義

correct = [num for row in matrix for num in row]

# 十層嵌套？不推薦！
bad = [
    f"{a}--{c}"
    for a in range(10)
    for b in range(10)
    for c in range(10)
    for d in range(10)
    for e in range(10)
    for f in range(10)
    for g in range(10)
    for h in range(10)
    for i in range(10)
    if a + b + c == 15
]

# 生成器：惰性求值，內存友好
gen = (num * 2 for row in matrix for num in row if num % 2 == 0)

# 只有需要時才計算
for value in gen:
    print(value)

texts = [
    ["apple", "banana", "cherry"],
    ["dog", "cat", "elephant"],
    ["red", "blue", "green"]
]

# 使用 map + filter + 推導式
long_words = list(
    map(lambda x: x.upper(), 
        filter(lambda x: len(x) > 4, 
               [word for row in texts for word in row]))
)

print(long_words)  # ['BANANA', 'CHERRY', 'ELEPHANT', 'GREEN']

logs = [
    {"timestamp": "2024-04-01", "level": "ERROR", "msg": "DB connection failed"},
    {"timestamp": "2024-04-01", "level": "INFO", "msg": "User login success"},
    {"timestamp": "2024-04-02", "level": "WARN", "msg": "High CPU usage"}
]

error_logs = [
    log["msg"] 
    for log in logs 
    if log["level"] == "ERROR"
]

print(error_logs)  # ['DB connection failed']

# 三維像素數(shù)組：[行][列][RGB]
image = [
    [[255, 0, 0], [0, 255, 0]],  # 紅綠
    [[0, 0, 255], [255, 255, 0]]  # 藍黃
]

# 提取所有紅色通道值
red_values = [pixel[0] for row in image for pixel in row]
print(red_values)  # [255, 0, 0, 255]

user_actions = [
    {"user": "Alice", "action": "login", "time": 10},
    {"user": "Bob", "action": "purchase", "time": 15},
    {"user": "Alice", "action": "logout", "time": 20},
]

# 獲取 Alice 的所有操作
alice_actions = [
    action["action"] 
    for action in user_actions 
    if action["user"] == "Alice"
]

print(alice_actions)  # ['login', 'logout']

# ? 每次都重新計算
bad = [func(x) for x in data if func(x) > 10]

# ? 提前計算
filtered = [x for x in data if func(x) > 10]
result = [func(x) for x in filtered]

# 先過濾，再展開
filtered_rows = [row for row in matrix if sum(row) > 10]
flattened = [num for row in filtered_rows for num in row]

# 通用模板：多維數(shù)據(jù)處理
def extract_from_nested(data, condition=None, transform=None):
    """
    通用嵌套提取函數(shù)
    """
    result = [
        transform(item) if transform else item
        for sublist in data
        for item in sublist
        if not condition or condition(item)
    ]
    return result

# 用法
matrix = [[1, 2], [3, 4]]
even_doubled = extract_from_nested(matrix, 
                                   condition=lambda x: x % 2 == 0,
                                   transform=lambda x: x * 2)

print(even_doubled)  # [4, 8]