Two 3D matrix of different sizes but similar composition, compare the likelihood that a given item is present in a column

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Overview

Given that 3D Matrix 1 and Matrix 2 have the same number of items "P" randomly dispersed, how can it be shown that the liability of "P" in any given column <A, B, C, D> is less when using a Matrix B that has 9 additional rows? By liability, stating fewer "P" items are impacted/lost if a given column < A, B, C, D> is removed.

Context

Matrix 1 has six(6) rows and Matrix 2 has fifteen (15) rows. Matrix 1 and Matrix 2, both have four columns and each column has a z-axis with "n" items (for simplicity start with 3, but the final number 128)

matrix Row Count Columns z-axis
Matrix 1 6 4 3
Matrix 2 15 4 3

Attempt with Python

My inaugural approach with python was to generate each matrix and inserting placeholders P / e(empty). However I realize that won't prove, though it may show/imply that same number of "P" in Matrix B is less liable.