Advanced python usage

The following is a summary of advanced Python usages compiled by multiple documents, from syntax sugar to complex feature layered presentation, with key features attached code examples and source references:

1. Grammatical sugar and concise writing method (from simple to traditional)

Variable exchange
No temporary variable implementation value swap is required:
```
a, b = b, a # The traditional way requires temp variables
```

walrus operator (:=)
Python 3.8+ allows assignment of values in expressions:

if (n := len(data)) > 100:
     print(f"Data quantity: {n}bars") # Reduce repeated calculations

F-strings Advanced
Supports multi-line formats and expressions:

html = f"""<div>{()}</div>""" # Multi-line string
 print(f"{():%Y-%m-%d}") # Format directive

Dictionary merger and derivation
Quickly merge dictionary and generate key-value pairs:

merged = {**d1, **d2} # Merged Dictionary (3.5+)
 squares = {k: v**2 for k, v in zip(keys, values)} # Dictionary derivation

Pattern Matching (3.10+)
Structured branch processing:

match:
     case 200: print("Success")
     case 404: print("Not Found")

2. Advanced functional programming

Closure Factory
Create a function with state:

def power_factory(exponent):
    def power(base): return base ** exponent
    return power
square = power_factory(2)

Parameterized decorator
Nested implementation of decorator with parameters:

def retry(max_attempts):
     def decorator(func):
         def wrapper(*args, **kwargs):
             for _ in range(max_attempts):
                 try: return func(*args, **kwargs)
                 except: pass
             raise Exception("Retry exhausted")
         Return wrapper
     Return decorator

Advanced function chain processing
Map/filter/reduce combination:

from functools import reduce
sum_sq = reduce(lambda x,y: x+y, map(lambda n: n**2, range(10)))

3. Data structure optimization

Generator lazy calculation
Save memory when processing large files:

def read_large_file():
     with open('') as f:
         yield from (() for line in f) # Processing line by line

Named tuples and data classes
Enhanced data structure readability:

from collections import namedtuple
Point = namedtuple('Point', ['x', 'y'])

from dataclasses import dataclass
@dataclass
class User:
    name: str
    age: int

Two-way queue and sliding window
Efficient insertion and deletion operation:
```
from collections import deque
 dq = deque(maxlen=3) # Fixed length queue
```

4. Concurrent and asynchronous programming

Thread pool processing
CPU-intensive tasks parallel:

from  import ThreadPoolExecutor
with ThreadPoolExecutor() as executor:
    results = list((process, data_list))

Coroutines and asynchronous IO
High concurrent network request processing:

async def fetch(url):
    async with () as session:
        async with (url) as response:
            return await ()

Shared memory acceleration
Multi-process sharing data:

from multiprocessing import Value
 num = Value('i', 0) # Shared integer variable

5. Metaprogramming and reflection

Dynamic class creation
Runtime Generate Class:

MyClass = type('MyClass', (Base,), {'attr': 42}) # Dynamically create class

Metaclass control properties
Use in ORM and other scenarios:

class Meta(type):
    def __new__(cls, name, bases, dct):
        dct['version'] = 1.0
        return super().__new__(cls, name, bases, dct)

Monkey patch
Modify object behavior at runtime:

import requests
  = lambda: "Mock Data" # Replace method

6. Debugging and performance optimization

Memory analysis tool
Optimize objects with __slots__:

class Point:
     __slots__ = ('x', 'y') # Reduce memory by 40%

Cache Accelerated Recursion
lru_cache optimization and repeated calculation:

from functools import lru_cache
@lru_cache(maxsize=128)
def fib(n): return fib(n-1)+fib(n-2) if n>1 else n

Asynchronous debugging skills
Interactive debugging using breakpoint():

def divide(a, b):
     breakpoint() # Enter pdb debugging
     return a / b

7. Modern Type System (3.5+)

Type prompts and static checks
Improve code robustness:

def greet(name: str) -> str:
    return f"Hello, {name}"

Generic type support
Define a common container:

from typing import TypeVar, Generic
T = TypeVar('T')
class Box(Generic[T]):
    def __init__(self, item: T): ...

Best Practice Recommendations

• Syntactic sugar selection: Use f-strings instead of % formatting first, use | merge dictionary
• Concurrent scenarios: Asyncio for I/O intensive use, and CPU intensive use multi-process
• Use metaprogramming with caution: Recommended use of metaclasses for framework development, ordinary business codes avoid over-design
• Performance bottleneck: Use memoryview to process binary data, and use a generator to replace the list

For the complete 50 tips, please refer to:Python Magic Manual、Efficient Programming Guide、Analysis of new features in Python 3.12

Quote Source: Closure and decorator implementation; Data structure optimization skills; Syntactic sugar practice case; Analysis of the principle of grammatical glycost; Walrus operator description; Advanced function application; Functional programming paradigm; Metaprogramming in-depth analysis; Dynamic Feature Case; Performance optimization strategy; Concurrent processing scheme; Memory management skills; Cache acceleration method; Type system details; Advanced data structures; Data Applications; Debug Tips Guide; Python 3.12 Features; Concurrent programming practice