I could probably run experiments to measure this myself, but I'm hoping somebody can provide a detailed answer.

Suppose I have two Python arrays:

```
small_array = [{"x": i, "foo": "hamburger"} for i in range(0,10000)]
big_array = [{"x": i, "foo": "hamburger", "bar": "hotdog"} for i in range(0,10000)]

```

My question is: will basic array operations (such as iteration or accessing by index) that involve only the "x" parameter be faster for `small_array` compared to `big_array`?

I'm asking because I often find myself building a complex data structure X on which I will perform expensive operations A and B such that the overlap between the attributes of X used by A and B is small. So I'm wondering if there are performance advantages associated with separating X into Y and Z so that A can operate on Y and B can operate on Z.