It's not allowed to initialize a list directly like that with dataclasses. What you can do instead is rely on the _post_init_ method, and initialize the list there, as follows:

@dataclass
class Test:
my_list: list[str] = field(init=False)

def __post_init__(self):
self.my_list = ["item1", "item2", "item3"]

By adding the "field(init=False)" you don't have to provide a value for the list when you create an instance, so now you can do this:

test = Test()
print(test)
# prints: Test(my_list=['item1', 'item2', 'item3'])

Hi @zacky7862! Great question. Your approach would work, but you might run into issues if you want unique lists for each instance of your class. When you declare the list within the class scope like that, all instances of the class will share the same list because it's essentially a class variable and not an instance variable.

An alternative could be to define your list in the __init__ method or use `field(default_factory=list)` in your data class definition. That ensures each instance of your class gets its own unique list. Hope that helps! Do chime in if you need further clarification. Happy coding!

Yes, Pydantic is really nice. I still like dataclasses if I don’t need Pydantic’s features, because it’s built in.

Hi @mcalister1911Ab, thanks for engaging! Pydantic comes in handy when you're dealing with data validation and settings management in Python, given how it simplifies the complexity of data parsing. Nonetheless, data classes, being a built-in module in Python, have their distinctive ease of use and directness especially when dealing with data-oriented classes. They also assist in making code concise and readable. I'd love to know what specific Pydantic features made you change preferences. Remember, using one doesn't exclude the benefits of the other. Keep exploring and happy coding!

I'm not completely sure what kinds of functionalities a Pandas DataFrame has - don't have that much experience with them (yet). Dataclasses are built-in which is an advantage if you don't yet use Pandas and want to keep things simple. Dataclasses also automatically add things like default initializers, easy printing out of an object's representation, object comparison and a few other things. Perhaps Pandas DataFrames can do the same things, but I'd have to look into it in more detail (I'll probably do this in a video actually :) ).

Hi @spankadin5616, great question! Data classes in Python and DataFrames in Pandas aren't exactly comparable as they serve different purposes. Think of data classes as a streamlined way to create classes specifically for storing data, alongside some additional functionality that make them more convenient to work with. On the other hand, Panda's DataFrames are used predominantly for data manipulation and analysis.

Data classes are great for structuring and managing simple data objects in your code. They can simplify your code and make it more readable, without the need for additional libraries.

Pandas DataFrame, however, is a whole different beast tailored towards data analysis. It shines when you need functionality like advanced filtering, grouping, merging, statistical functions, and more on larger sets of data.

For simple data structures and handling where you don't need sophisticated data manipulation functions, Python data classes might be the better, more lightweight choice. For advanced data manipulation and statistical analysis, particularly with larger data sets, Pandas DataFrames are a brilliant tool.

Of course, these are not hard and fast rules, and ultimately you have to choose the best tool that matches your specific use case. Keep exploring and happy coding!

You might also like Pydantic. It’s very similar but adds a few extras like data validation and nested models. It is a third-party package though.

Thanks @gregoryfenn1462! Indeed, Python continues to evolve with handy features like data classes, making it a robust tool for both software development and data manipulation. It allows our code to be more self-documented, and type hints are a great feature to ensure we're writing cleaner, less error-prone code. Keep enjoying Python and its growing capabilities! Stay tuned for more!

Thank you, Thomas! Pydantic was mentioned by someone on the Discord server as well. I think I’ll do a follow-up of this video soon and dive more into Pydantic as it’s a nice extension of dataclasses.

Hey @cr4h4n, glad you found the video helpful! It's great that you're keeping an active eye on emerging Python modules like Pydantic. I believe that it offers added boon for validation of data types and easy error handling, making the development process smoother. Stay tuned in for more such content! Remember, the key is to keep exploring and innovating. Happy coding!

Thanks, glad you liked it! One example where using a regular class is better is if the class is very behavior-focused and doesn’t have many instance variables. Also, if you need a very different initializer and don’t need the other features of dataclasses, a regular class is better. Finally, you shouldn’t use a dataclass for classes that act more like interfaces in your system (e.g. abstract classes or protocol classes).

Hi @victorh.torres2713, great question! Yes, there certainly are times when it might be better to use regular classes over dataclasses. Dataclasses are mainly designed for storing data and automatically providing common special functions. If you need your class to have a lot of behavior (methods), a regular class would probably be more appropriate. For instance, if you are creating a class that's primarily implementing complex business logic or behavior, you might want to stick with regular classes due to their flexibility. Similarly, if you need instances of your class to be mutable or if memory efficiency is a concern, you might lean towards using regular classes. Remember, the choice between dataclasses and regular classes mostly depends on the specific needs of your program. Thanks for your engaging question, happy coding!

Thanks! Actually, data classes are exactly the same as regular classes. The only thing the decorator does is already add dunder methods to make the class more suitable for dealing with representing data. So it's basically a shorter version of adding dunder methods yourself. Obviously, if what the data class decorator adds doesn't fit with what you need, then it is better to define those methods yourself, there is no particular performance gain to data classes.

Thanks for your thoughtful comment, @manonthedollar. I can certainly see where you're coming from, and your point is valid. The functionality provided by data classes has indeed been available in other forms, such as dunder methods and @property decorators. However, the built-in convenience of data classes affords us a more streamlined, read-friendly, and error-resistant strategy for initializing, comparing, and printing data.

In terms of performance, data classes are not necessarily faster. Their primary benefit lies in their consistency and code readability, which invariably result in less room for bugs and improved maintainability. This can indirectly 'improve performance' in terms of development time. Stay tuned for more videos and don't hesitate to bring up such engaging points for discussion. Cheers!

Thank you, glad to hear you find the videos helpful. I have to dive into pandas more to find out how it fits in. It’s a good suggestion though for a topic to explore in of of my videos in the future.

Hey @virolana, thank you for being a part of our coding community. It's great to hear that my content inspires you to explore new possibilities in your codebase! I totally agree that pandas is extremely convenient for data analysis tasks.

Considering your situation, dataclasses can indeed be beneficial for structuring and managing your data, especially when you find yourself overwhelmed by complex operations on dataframes. They can represent your data in an organized way, making your code cleaner and more efficient.

Deciding when to switch comes down to the task complexity and what your primary objectives are. If your focus is more about data manipulation and analysis, sticking with pandas might be more convenient due to its built-in functions and tools.

However, if you're dealing with data that represent objects having defined attributes, or when you need to create many instances of the same structured data, dataclasses could be your go-to tool. For example, you may have users with specific attributes (name, age, etc.). Instead of managing this in pandas, you could utilize a dataclass 'User', making it easier to handle and less prone to errors.

Finally, you might find it effective to use a combination of both when dealing with large datasets. You can handle and analyze the data with pandas, but when you need to start applying more logic to individual items, a switch to dataclasses could be effective.

Remember, always choose the tool best suited to your needs and that enhances your code readability and manageability. Keep experimenting and happy coding!

The sort index approach is mainly useful if you’d like to precompute how data should be sorted (you could even store this as a sorting hash with the data itself). The dunder methods are a bit simpler to use (I probably should have mentioned them in the video as an alternative). I think I’ll revisit dataclasses in another video, because there’s still a lot left to talk about.

Great question, @nanopython4189! Both approaches allow you to customize the comparison of your data class objects. The `sort_index` approach offers a slightly more explicit way to determine the order criteria, as you can directly assign the attribute you want to sort by to `sort_index`.

The dunder methods like `__lt__`, `__gt__`, provide more control and flexibility, as you can implement any custom logic you want inside them. For instance, you could compare multiple attributes or use complex calculations. However, these may require you to write more code.

Choosing between them often depends on the complexity of your data class and its comparison requirements. For regular, simpler use cases or when you need a quick and clean method for comparisons, using the `sort_index` approach can be beneficial. But if you are working with more complex scenarios, you might want to consider using the dunder methods. Always remember, the best approach depends on the specific requirements of your project. Keep coding!

Thanks! And yes - quite a few people from Reddit also over here :).

Hi @nerdvananc! Thanks for your kind words and for connecting on Reddit too. Stay tuned for more interesting content on Python programming. Happy Coding! :D