Is it beneficial to use OOP on large datasets in Python? : <p>I'm implementing Kalman Filter on two types of measurements. I have GPS measurement every second (1Hz) and 100 measurment of accelration in one second (100Hz).
So basically I have two huge tables and they have to be fused at some point. My aim is: I really want to write readable and maintainable code. </p>

<p>My first approach was: there is a class for both of the datatables (so an object is a datatable), and I do bulk calculations in the class methods (so almost all of my methods include a for loop), until I get to the actual filter. I found this approach a bit too stiff. It works, but there is so much data-type transformation, and it is just not that convenient.</p>

<p>Now I want to change my code. If I would want to stick to OOP, my second try would be: every single measurment is an object of either the GPS_measurment or the acceleration_measurement. This approach seems better, but this way thousands of objects would have been created.</p>

<p>My third try would be a data-driven design, but I'm not really familiar with this approach.</p>

<p>Which paradigm should I use? Or perhaps it should be solved by some kind of mixture of the above paradigms? Or should I just use procedural programming with the use of pandas dataframes? </p>
