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👍 Fluent Python — practical examples from one of the best Python books!

Here, the language's capabilities are explained, which many only know superficially. The material focuses not on basic syntax, but on a deep understanding and proper use of its capabilities in real projects. There are many examples of working with objects, collections, functions, decorators, generators, async code, and Python's internal logic.

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What is the difference between copy() and deepcopy() in Python?

copy() creates a shallow copy of the object: nested elements remain shared between both copies. In contrast, deepcopy() recursively copies all nested objects, making the new structure completely independent. This is particularly important when working with mutable data

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The guide Path to Senior Engineer Handbook has gathered resources for developers who want to advance to the level of Senior Engineer. 🚀

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When applying multiple filters to a series in Pandas, it's better to break the condition into several lines:

s = pd.Series([10, 15, 20, 25, 30])

s.loc[
    (s > 20) &
    (s % 2 == 1)
]

Such code is easier to read, write, and maintain. 🛠️✨

As a result, the value:
25
will be selected,
since it is both greater than 20 and an odd number. 🎯

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# How to Implement a Decorator with Parameters Using a Class in Python

You can create a parameterized decorator in Python by using a class that implements the __call__ method. The class acts as the decorator factory, where the __init__ method stores the parameters, call__call__ method applies the decorator logic to the target function.

## Example Implementation

class my_decorator:
    def __init__(self, param):
        self.param = param

    def __call__(self, func):
        def wrapper(*args, **kwargs):
            print(f"Decorator parameter: {self.param}")
            return func(*args, **kwargs)
        return wrapper

@my_decorator("Hello")
def say_hello(name):
    print(f"Hello, {name}!")

say_hello("World")

## Explanation

1. Class Definition: Define a class that will serve as the decoratorinit.
2. **__init__**: Store the parameters passed to the decoracall.
3. **__call__**: Make the instance callable. This method receives the function being decorated and returns the wrappWrapperrapper**: The inner function executes the actual decorator logic before or after calling the original function.

Cheat sheet on Python Data Types 🐍

Python Data Types — the main data types in Python.

🔢 Numbers — numerical types (int, float, complex)
⚖️ Bool — logical values (True, False)
📝 String — text strings
📋 List — mutable ordered collections
🔒 Tuple — immutable ordered collections
🎲 Set — unique unordered elements
🗂️ Dict — collections of "key-value" pairs.

Helps to quickly orient yourself in data types and choose the appropriate structure for storing information 💡

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Python is like New Year. 🎉 You can pass a tuple to startswith and endswith. 💻🐍

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Variables in Python are stickers, not boxes.

A variable doesn't store a value, but points to it. If you change a list, both variables will see the change. This makes sense with stickers. But with boxes, it's magic: two boxes mysteriously change simultaneously.

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What useful functions are there in the functools module?

Among the most popular ones: lru_cache for caching results, partial for partial application of arguments, wraps for preserving metadata in decorators and cmp_to_key for transforming a comparison function into a sorting key 🐍✨

# functools module usage
from functools import lru_cache, partial, wraps, cmp_to_key

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How to create your own context manager in Python?

There are two main ways: to create a class with methods enter() and exit(), or to use the decorator @contextmanager from the module contextlib. The second option is usually more compact and is based on generators.

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