Given a set of bounding boxes in an 2D space, group them into rows

Given a set of N bounding boxes with vertex coordinates:

"vertices": [
    {
      "y": 486, 
      "x": 336
    }, 
    {
      "y": 486, 
      "x": 2235
    }, 
    {
      "y": 3393, 
      "x": 2235
    }, 
    {
      "y": 3393, 
      "x": 336
    }
  ]

I would like to group the bounding boxes into rows. In other words, given the pictorial representation of bounding boxes in this image:

I would like an algorithm that returns:

[1,2,3]
[4,5,6]
[7,8]

[Edit: Clarification] The grouping decisions (e.g. [4,5,6] and [7,8]) should be based on some kind of error minimisation such as least squares.

Is there an algorithm or library (preferably in python) that does this?

I think this is a clustering problem. In fact, because you can ignore the x co-ordinates, I think this is a 1-dimensional clustering problem. Some standard clustering algorithms such as k-means are good for minimising sums of squares from cluster centres, which amounts to what you are looking for. Unfortunately, they don't guarantee to find the globally best solution. One-dimensional clustering is a special case for which there are exact algorithms - see Cluster one-dimensional data optimally?.