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Let’s create Python data objects in a few lines of code!
If you’re a Python coder, you’re probably familiar with Zen. Three of its 19 guideline principles state that “explicit is better than implicit,” “readability counts,” and “simple is better than complex.” When you’re creating or integrating an existing Python package, you aim to find the most Pythonic way to do your task, both functionally and efficiently. Python’s dataclasses library provides an attractive approach to quickly and easily creating objects. This package includes a suite of tools that help speed up and make your code legible, whether you’re working on a data science or software development project. However, given that there is no magic wand without a wizard, the dataclass wizard package provides dataclasses with additional powers that can enhance your code in a Pythonic style. In this post, we will dive into these two packages to take our work to the next level.
To use dataclasses, we import and apply the @dataclass decorator. This decorator enables us to specify if the instance should be frozen (frozen=True), initialized (init=True), or slotted (slots=True). Moreover, although the field object is not required for creating dataclasses objects, we can use it to provide powers to the attributes, such as indicating default values, default initializers for non-primitive data types like dictionaries, and whether the attribute is part of the constructor (__init__), and/or part of the class representation (__repr__).
For our exploration, we’ll use the dataclasses package to generate slotted classes. If you are unfamiliar with Python’s slot mechanism, don’t worry; you can still follow the post. Please feel free to explore the concept of slots in the following post ⬇️:
For example, let’s create the slotted class ClassA. This class’s public attributes attr1, attr2, attr3, and attr4 will be used by the constructor. attr1 and attr2 will be…
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