Advanced in Python

Advanced Python

In this section you’ll find Python tutorials that teach you advanced concepts so you can be on your way to become a master of the Python programming language. Once you’re past the intermediate-level you can start digging into these tutorials that will teach you advanced Python concepts and patterns.

Certainly! Let’s explore a few more advanced concepts and techniques in Python:

1)Descriptors:
Descriptors are a powerful and flexible way to customize attribute access in Python. They allow you to define how attributes are get or set on an object. This is often used for creating properties with custom behavior.

python
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class DescriptorExample:
def init(self):
self._value = 0

def __get__(self, instance, owner):
    return self._value

def __set__(self, instance, value):
    if value < 0:
        raise ValueError("Value must be non-negative")
    self._value = value

class MyClass:
descriptor_attr = DescriptorExample()

obj = MyClass()
obj.descriptor_attr = 42
print(obj.descriptor_attr) # 42

2)Metaprogramming:
Metaprogramming involves writing code that manipulates code during runtime. This can include dynamically creating classes, modifying functions, or even generating code.

python
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def add_method(cls, method_name):
def method(self):
return f”{method_name} called”
setattr(cls, method_name, method)

class MyClass:
pass

add_method(MyClass, “my_method”)
obj = MyClass()
print(obj.my_method()) # “my_method called”

3) Functional Programming Tools:
Python supports functional programming paradigms, and there are tools like functools, itertools, and operator that provide powerful functions for working with functions and iterators.

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from functools import reduce
from itertools import chain
from operator import add

numbers = [1, 2, 3, 4, 5]

Using reduce to calculate the sum

sum_result = reduce(add, numbers)
print(sum_result) # 15

Using chain to flatten nested lists

nested_lists = [[1, 2], [3, 4], [5, 6]]
flattened = list(chain(*nested_lists))
print(flattened) # [1, 2, 3, 4, 5, 6]

4) Asyncio and Asynchronous Programming:
The asyncio library provides support for writing asynchronous code using the async and await keywords. This is particularly useful for handling I/O-bound operations concurrently.

python
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import asyncio

async def async_function():
print(“Start”)
await asyncio.sleep(2)
print(“End”)

asyncio.run(async_function())

5) Data Classes:
The dataclasses module simplifies the creation of classes primarily used for storing data. It automatically generates special methods like init, repr, and others.

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from dataclasses import dataclass

@dataclass
class Point:
x: int
y: int

p = Point(1, 2)
print(p) # Point(x=1, y=2)

6) Type Hints and Annotations:
Type hints allow you to add information about the expected types of function arguments and return values. While Python is dynamically typed, type hints can be used by tools like mypy for static type checking.

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def add(a: int, b: int) -> int:
return a + b

result = add(3, 5)

7) Multiple Inheritance and Mixins:
Python supports multiple inheritance, allowing a class to inherit from more than one base class. Mixins are a common use of multiple inheritance, providing a way to reuse code in a modular way

python
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class LoggerMixin:
def log(self, message):
print(f”Log: {message}”)

class MyClass(LoggerMixin, AnotherClass):
pass

obj = MyClass()
obj.log(“This is a log message”)
These advanced concepts showcase the flexibility and expressiveness of Python. While they may not be used in every project, having an understanding of these features can empower you to write more efficient and elegant code when the need arises.

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