# 傳統(tǒng)做法
word_counts = {}
sentence = "the quick brown fox jumps over the lazy dog"
for word in sentence.split():
    if word not in word_counts:
        word_counts[word] = 0
    word_counts[word] += 1

from collections import defaultdict

# defaultdict 的優(yōu)雅實踐
word_counts = defaultdict(int) # 當key不存在時，默認值為int()即0
sentence = "the quick brown fox jumps over the lazy dog"
for word in sentence.split():
    word_counts[word] += 1 # 直接操作，無需檢查

from collections import deque

# 創(chuàng)建一個deque
tasks = deque(['Task A', 'Task B', 'Task C'])

# 頭部添加任務（高效）
tasks.appendleft('Task D') # ['Task D', 'Task A', 'Task B', 'Task C']

# 完成頭部任務（高效）
completed_task = tasks.popleft() # 'Task D'

import dataclasses

@dataclasses.dataclass(frozen=True) # frozen=True使實例不可變，更安全
class UserProfile:
    user_id: int
    username: str
    is_active: bool = True # 可以提供默認值
    email: str | None = None # 結合類型提示，清晰明了

# 自動擁有了__init__, __repr__, __eq__等方法
user = UserProfile(user_id=101, username='alex')
print(user) # 輸出: UserProfile(user_id=101, username='alex', is_active=True, email=None)

# 讀取一個巨大的日志文件，傳統(tǒng)方式可能耗盡內存
def process_large_file_list(path):
    with open(path, 'r') as f:
        return f.readlines() # 一次性讀取所有行到列表

# 使用生成器，內存占用極低
def process_large_file_generator(path):
    with open(path, 'r') as f:
        for line in f:
            yield line.strip().upper() # 每次只處理一行，并交出結果

# 使用生成器
log_processor = process_large_file_generator('huge.log')
for processed_line in log_processor:
    # 每次循環(huán)，只在內存中保留一行數(shù)據(jù)
    print(processed_line)

# 列表推導式（占用大量內存）
squared_numbers_list = [x*x for x in range(10_000_000)]

# 生成器表達式（幾乎不占用內存）
squared_numbers_gen = (x*x for x in range(10_000_000))

# 只有在迭代時才計算和消耗值
total = sum(squared_numbers_gen)

import time

# 方式一：基于類的實現(xiàn)
class Timer:
    def __enter__(self):
        self.start_time = time.perf_counter()
        return self # 返回實例自身，使其可以被as賦值

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.end_time = time.perf_counter()
        elapsed = self.end_time - self.start_time
        print(f"代碼塊執(zhí)行耗時: {elapsed:.4f} 秒")
        # 如果返回True，則抑制異常

# 方式二：基于`contextlib`的優(yōu)雅實現(xiàn)
from contextlib import contextmanager

@contextmanager
def elegant_timer():
    try:
        start_time = time.perf_counter()
        yield# yield之前的代碼是__enter__部分，yield是代碼塊執(zhí)行的分界點
    finally:
        # yield之后的代碼是__exit__部分，finally確保總能執(zhí)行
        end_time = time.perf_counter()
        elapsed = end_time - start_time
        print(f"代碼塊執(zhí)行耗時: {elapsed:.4f} 秒")

# 使用我們自己的上下文管理器
with Timer():
    # 模擬耗時操作
    sum(x for x in range(1000000))

with elegant_timer():
    # 模擬另一個耗時操作
    time.sleep(1)

import functools

# 這是一個裝飾器
def logging_decorator(original_func):
    @functools.wraps(original_func) # 保留原函數(shù)的元信息（如函數(shù)名、文檔字符串）
    def wrapper(*args, **kwargs):
        print(f"調用函數(shù): {original_func.__name__}")
        result = original_func(*args, **kwargs) # 調用原始函數(shù)
        print(f"函數(shù) {original_func.__name__} 執(zhí)行完畢")
        return result
    return wrapper

# 應用裝飾器
@logging_decorator
def say_hello(name):
    """一個簡單的問候函數(shù)"""
    print(f"你好, {name}!")

say_hello("世界")
# 等價于: say_hello = logging_decorator(say_hello)
# say_hello("世界")

def repeat(num_times):
    def decorator_repeat(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            for _ in range(num_times):
                func(*args, **kwargs)
        return wrapper
    return decorator_repeat

@repeat(num_times=3)
def greet(name):
    print(f"歡迎, {name}!")

greet("Alice") # 將會打印三次歡迎語

# 無類型提示的版本
def calculate_price(base, tax_rate, discount):
    return base * (1 + tax_rate) - discount

# 帶有類型提示的專業(yè)版本
def calculate_price_typed(base: float, tax_rate: float, discount: float = 0.0) -> float:
    """
    計算最終價格。
    :param base: 基礎價格
    :param tax_rate: 稅率 (例如 0.05 代表 5%)
    :param discount: 折扣金額
    :return: 包含稅和折扣的最終價格
    """
    ifnot0 <= tax_rate <= 1:
        raise ValueError("稅率必須在0和1之間")
    return base * (1 + tax_rate) - discount

# 如果我們錯誤地傳入一個字符串
# price = calculate_price_typed(100.0, "0.05") # 這是一個錯誤

# 運行Mypy (mypy your_script.py)，它會在運行前就報告錯誤:
# error: Argument 2 to "calculate_price_typed" has incompatible type "str"; expected "float"