Aggregate parts of a column in a matrix

June 13, 2023

I have a matrix like this one:

A = [[ 1.0,  2.0,  3.0,  4.0,  5.0],
     [ 6.0,  7.0,  8.0,  9.0, 10.0],
     [11.0, 12.0, 13.0, 14.0, 15.0],
     [16.0, 17.0, 18.0, 19.0, 20.0]]

And i want to aggregate parts of the matrix. For example the mean of 6 [1][0] and 11 [2][0].
My intuitiv idea was doing it like this: np.mean(A[1:2][0]). But this doesn’t work.

How can I aggregate intervals of a matrix?

>Solution :

You can use here Numpy to convert in in Numpy array

A = [[ 1.0,  2.0,  3.0,  4.0,  5.0],
     [ 6.0,  7.0,  8.0,  9.0, 10.0],
     [11.0, 12.0, 13.0, 14.0, 15.0],
     [16.0, 17.0, 18.0, 19.0, 20.0]]

import numpy as np
slice = np.array(A)[1:3,0]
mean = np.mean(slice)

In this example, we use the slicing notation A[1:3, 0] to select rows 1 to 2 (exclusive) and column 0 of matrix A. This creates a 1D array [6.0, 11.0], and then we calculate the mean using np.mean(), which gives us the result of 8.5.

Remember that when slicing arrays, the range is exclusive for the end index. So 1:3 selects rows 1 and 2, but not 3. Similarly, 0 selects only column 0.