What are lambda functions in Python?

Lambda functions — are anonymous functions, which are created using the lambda keyword. They can accept any number of arguments, but contain only one expression, the result of which is automatically returned.

They are most often used for short operations, for example during sorting, filtering or data processing.

# Example:
sorted_data = sorted(data, key=lambda x: x[1])
filtered_data = list(filter(lambda x: x > 0, numbers))

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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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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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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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