Data Analytics and Data Scientists are very fond of Python - user-friendly object-oriented language, which contains a variety of built-in functions and allows you to create your own. According to the DRY principle (Don't Repeat Yourself), the modern standard of development is the code packaging in classes that provide API to use their methods. In practice, creating their own classes in Python has often quite often, with the subsequent addition and expansion of them as work. This will make it faster, the standard Dataclasses module will help, which first should be imported. Then you need to reorganize the user class by a special DataClass function, and within this class, list the necessary attributes, previously annotating them, i.e. Setting types. For example, for user class Person with an integer identifier and a string name it will look like this:

From Dataclass Import Dataclass

The DataClasses module provides a decorator and functions to automatically add generated special methods, such as init () and REPR (), in user-defined classes. However, you need to use the DataClasses module with the mind and know about the following features:

• By default, the module does not check the type specified in the abstract variable;

• Dataclass () decorator adds some methods to the user class that may be superfluous and duplicate the behavior of existing in the class;

• When using the Dataclass () decorator, all the basic classes are viewed in the reverse order, starting from the object and for each found class, the attributes of its parent are added to an ordered field display. All generated methods will use this combined calculated ordered field matching. And, since the fields are located in the order of their inserts, derived classes override the basic.

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