1. 超越文件处理的内容管理器

大多数开发人员都熟悉使用 with 语句进行文件操作：

with open('file.txt', 'r') as file:
    content = file.read()
# File is automatically closed after this block

但是， 内容管理器 可以做更多更多。它们是所有类型资源管理的完美选择：

from contextlib import contextmanager
import time

@contextmanager
def timer():
    """Measure execution time of a code block."""
    start = time.time()
    try:
        yield  # This is where the code within the 'with' block executes
    finally:
        end = time.time()
        print(f"Elapsed time: {end - start:.4f} seconds")

# Usage
with timer():
    # Some time-consuming operation
    result = sum(range(10_000_000))

contextlib 模块还提供了方便的工具，比如 suppress 用于抑制特定的异常 ：

from contextlib import suppress

# Instead of:
try:
    os.remove('temp_file.txt')
except FileNotFoundError:
    pass

# You can write:
with suppress(FileNotFoundError):
    os.remove('temp_file.txt')

2. 部分函数

functools.partial （?) 函数允许你创建带有预填充参数的新函数 ：

from functools import partial

# Instead of writing a new function
def power_of_two(x):
    return pow(x, 2)

# You can use partial
power_of_two = partial(pow, exp=2)

# Create a base-2 logarithm function
import math
log2 = partial(math.log, base=2)

print(log2(8))  # Outputs: 3.0

这对于回调函数或在处理 高阶函数 时特别有用。

3. 解构泛化

解包运算符 * 和 ** 比我们大多数人都意识到的要更通用：

# Merging dictionaries (Python 3.5+)
defaults = {"colour": "red", "size": "medium"}
user_settings = {"size": "large", "mode": "advanced"}
settings = {**defaults, **user_settings}
print(settings)  # {'colour': 'red', 'size': 'large', 'mode': 'advanced'}

# Extended unpacking (Python 3.0+)
first, *middle, last = [1, 2, 3, 4, 5]
print(middle)  # [2, 3, 4]

# Unpacking in function calls
def tag(name, **attributes):
    attr_str = ' '.join(f'{k}="{v}"' for k, v in attributes.items())
    return f'<{name} {attr_str}>'

props = {"class": "button", "id": "submit-btn", "disabled": True}
print(tag("button", **props))  # <button class="button" id="submit-btn" disabled="True">

4. 省略号（...）的意外用途

省略号字面量不只是用于类型提示：

# As a placeholder for future code
def function_to_implement_later():
    ...  # More explicit than 'pass'

# In multidimensional NumPy slicing
import numpy as np
array = np.random.rand(4, 4, 4)
# Select the middle column from all rows in all matrices
middle_column = array[:, 1, ...]

5. 函数属性

Python 函数是对象，可以有属性 :

def process_data(data, verbose=False):
    """Process the given data."""
    if verbose or process_data.always_verbose:
        print("Processing data...")
    # Processing logic here
    return data

# Add an attribute to the function
process_data.always_verbose = False

# Later in your code
process_data.always_verbose = True  # Enable verbose mode globally

这可以是在某些情况下作为全局变量的一个酷替代方案。

6. 自定义排序键使用key=参数

排序函数中的 key 参数比大多数人意识到的要强大得多：

# Sort strings by length
words = ["apple", "pear", "banana", "strawberry", "fig"]
sorted_by_length = sorted(words, key=len)
print(sorted_by_length)  # ['fig', 'pear', 'apple', 'banana', 'strawberry']

# Sort complex objects
from operator import attrgetter, itemgetter

# For a list of dictionaries
users = [
    {"name": "Alice", "age": 30},
    {"name": "Bob", "age": 25},
    {"name": "Charlie", "age": 35}
]
sorted_users = sorted(users, key=itemgetter("age"))

# For a list of objects
from collections import namedtuple
Person = namedtuple("Person", ["name", "age"])
people = [Person("Alice", 30), Person("Bob", 25), Person("Charlie", 35)]
sorted_people = sorted(people, key=attrgetter("age"))

7. 默认字典和计数器集合

collections 模块包含可以替代常见模式的数据结构 ：

from collections import defaultdict, Counter

# Instead of:
word_count = {}
for word in text.split():
    if word not in word_count:
        word_count[word] = 0
    word_count[word] += 1

# You can use:
word_count = defaultdict(int)
for word in text.split():
    word_count[word] += 1

# Or even simpler:
word_count = Counter(text.split())
print(word_count.most_common(5))  # Shows the 5 most common words

8. 枚举类型用于更好的常量

枚举模块有助于定义有意义的常量：

from enum import Enum, auto

class Status(Enum):
    PENDING = auto()
    RUNNING = auto()
    COMPLETED = auto()
    FAILED = auto()

def process_job(job, status):
    if status == Status.RUNNING:
        print(f"Job {job} is still running")
    elif status == Status.COMPLETED:
        print(f"Job {job} completed successfully")
    # ...

# So much more readable than numeric constants
current_status = Status.RUNNING
process_job("backup", current_status)

9. 数据类用于更简洁的代码

Python 3.7 引入了 数据类 （通过 PEP-557），它们减少了主要用于存储数据的类中的样板代码：

from dataclasses import dataclass, field
from typing import List

@dataclass
class Student:
    name: str
    student_id: int
    courses: List[str] = field(default_factory=list)
    active: bool = True

    def enroll(self, course):
        self.courses.append(course)

# No need to write __init__, __repr__, __eq__, etc.
student = Student("Jane Smith", 12345)
student.enroll("Computer Science 101")
print(student)  # Student(name='Jane Smith', student_id=12345, courses=['Computer Science 101'], active=True)

10. 使用__slots__提高内存效率

对于具有固定属性集的类，__slots__ 可以显著减少内存使用：

class RegularPoint:
    def __init__(self, x, y):
        self.x = x
        self.y = y

class MemoryEfficientPoint:
    __slots__ = ['x', 'y']
    
    def __init__(self, x, y):
        self.x = x
        self.y = y

# The __slots__ version uses significantly less memory when many instances are created
import sys
regular = RegularPoint(3, 4)
efficient = MemoryEfficientPoint(3, 4)

print(sys.getsizeof(regular))  # Typically larger
print(sys.getsizeof(efficient))  # Typically smaller

11. f 字符串调试（Python 3.8+）

在 Python 3.8 中，f 字符串增加了一个方便的调试功能： 已添加

x = 10
y = 20
print(f"{x=}, {y=}, {x+y=}")
# Outputs: x=10, y=20, x+y=30

通过显示变量名及其值，在调试时节省时间。

12. pathlib 用于现代文件操作

pathlib 模块为文件系统路径提供了面向对象的方法：

from pathlib import Path

# Create paths
data_dir = Path("data")
file_path = data_dir / "output.txt"  # Path joining with / operator

# Create directory if it doesn't exist
data_dir.mkdir(exist_ok=True)

# Write to a file
file_path.write_text("Hello, world!")

# Read from a file
content = file_path.read_text()

# Iterate over files in a directory
for python_file in data_dir.glob("*.py"):
    print(f"Found Python file: {python_file.name}")

。