Python can make a dictionary immutable without copying data!

Usually, to protect configurations and the overall state, a copy of the dictionary is made, which creates unnecessary memory allocations.

safe = dict(config)

MappingProxyType creates a read-only proxy over a dictionary — writing through it becomes impossible, but the data is not copied.

readonly["debug"] = True  # TypeError

At the same time, the proxy remains alive: if the original dictionary changes, the changes will automatically be reflected in the read-only view.

config["debug"] = True

This is especially useful for configurations, internal APIs, overall state, and data protection within libraries.

def get_settings():
    return MappingProxyType(settings)

🔥 MappingProxyType allows you to provide a read-only view of the dictionary without copying and without the risk of mutation through the returned object.

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The ternary operator in Python looks appealing, but it's easy to overdo it with it ::

'A' if s>=90 else 'B' if s>=80 else 'C' if s>=70 else 'F'

Just because the code can be compressed into one line, doesn't mean it's a good idea for readability.

When the logic starts to branch out (3+ conditions) — the usual if-elif-else becomes much more understandable and easier to maintain.

It's better to leave the ternary operator for simple and short cases:
• compact expressions in comprehensions
• small lambda functions
• simple one-line returns

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📂 Reminder on Python set — set methods!

For example, add() adds an element to the set, update() combines several elements, and intersection() helps quickly find common values between data sets.

In the picture — the main methods and operations for working with set: addition, removal, union, intersection, difference, and set checks.

Save it to not lose it!

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10 GitHub Repositories for Web Development in Python 🐍

Explore the best Python web development repositories for building APIs, full-stack web apps, dashboards, machine learning demos, internal tools, and interactive Python-based user interfaces. 🔥

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Guessing numbers

This project for beginners in Python is a fun game that generates a random number (within a certain range) that the user must guess after receiving hints.

For each incorrect guess, the user receives additional hints, but at the cost of reducing their overall score.

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I often see people say that it's impossible to enter the IT field without expensive courses.

However, there's a huge amount of high-quality materials available for free:

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If you have internet and a bit of free time, you can learn computer science, algorithms, system design, DevOps, clouds, security, and machine learning for free.

The problem now isn't a lack of information. The problem is regularly opening these repositories and actually working on them.

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How to create an object from a dictionary with dot access — without classes and dataclasses?

When you're working with JSON, configurations, or APIs, constant access via dict['key'] clutters the code and worsens readability:

data = {"host": "localhost", "port": 5432}
data["host"]

SimpleNamespace gives the same result, but with dot access:

cfg = SimpleNamespace(**data)
print(cfg.host)

In this case, the object remains dynamic, and you can add fields:

cfg.debug = True

However, the keys must be valid attribute names, and this only works for flat dictionaries (nesting is not converted).

🔥Convenient for prototyping, testing, and simple data.

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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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😍 learnxinyminutes — a brief and informative reference guide!

A convenient cheat sheet that allows you to quickly get acquainted with the basics of Python or refresh your knowledge before work. On one page, the key constructs of the language are collected: variables, functions, classes, collections, exception handling, etc. The format is as compact as possible, with a minimum of theory and a maximum of practical examples.

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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. 🚀

Inside: 📚

More than 50 newsletters on professional growth, system design, leadership, and web development. 📈

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A good starting point for those who want to improve not only their technical skills, but also their architectural thinking, communication, and leadership competencies. 💡

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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.