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I am using numpy arrays extensively in my codebase. The different columns of the array relate to different data. I want to create a class that enforces some schema and allows custom methods to be added. I want to keep the methods and properties of the numpy array though.

I'm looking for something similar to a dataclass or pydantic object, that lets me keep the ability to index and interact with the the data the same as if it were a numpy array.

Here's what i've tried: Example 1

@dataclass
class MyCoord:
    x: int
    y: int

    
my_data = np.array([[0,0],[1,1]], dtype=MyCoord)
print(f"The y coords from my data {my_data[:,1]}, which is correct")
print(f"The y coord is not an attribute though {my_data[0].y}, will error")

I can subclass the numpy array, but thats not recommended for maintainability

Improve this question
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  • "The different columns of the array relate to different data" This is generally not a good idea performance-wise. You should use multiple arrays to better benefits from SIMD instruction boost. See AoS vs SoA. Commented Nov 21, 2024 at 14:40
  • 1
    Sounds a bit like you want a structured array. Computationally it has few benefits compared to separate arrays. As written your array is object dtype. Commented Nov 21, 2024 at 17:01
  • perhaps the pandas package would be helpful? Commented Nov 22, 2024 at 1:19
  • structured arrays are a step towards my objective, and i looked into them. I'm looking to enforce stricter rules. i.e. column 1 must have values between 0-1, column 2 must have positive integers etc. Structured arrays can enforce broad types but i don't see a way to enforce those rules. I'm not worried about performance concerns, but thank you @JérômeRichard for the link. Pandas also doesn't enforce the schema either. In the end i've created a dataclass with numpy array as the data store, then added len and get_item methods to support numpy-like indexing. Commented Dec 19, 2024 at 16:12
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