How to generate a matrix of random values with zeros on the diagonal?

Question

I want get a matrix of random values with zero on main diagonal. Then the following codes work to integer values, but I actually want float value.

def matriz_custo(n):
    numeros = sample(range(35,120+1),(n-1)*n)
    c = np.array([numeros[i:i+n] for i in range(0,len(numeros),n)])
    np.fill_diagonal(c, 0)
    c[0][n-1] = 0
    return (c)

so I tried:

def matriz_custo_new(n):
    numeros = np.random.uniform(low=27.32, high=37.25, size=(n-1,n))
    c = np.array([numeros[i:i+n] for i in range(0,len(numeros),n)])
    r = np.arange(n-1)
    c[:,r,r] = np.inf
    c[0][n-1] = 0
    return (c)

then happen to create a extra dimension. I want to access it like c[0][0], otherwise I have to change all of my code.

[EDIT]

I get it now! :

    def matriz_custo_new(n):
        numeros = np.random.uniform(low=27.32, high=37.25, size=((n-1)*n,))
        c = np.array([numeros[i:i+n] for i in range(0,len(numeros),n)])
        r = np.arange(n-1)
        c[r,r] = 0
        c[0][n-1] = 0
        return (c)

Answer 1

The fill_diagonal function in the NumPy package you're using will allow you to fill the diagonal of any array regardless of its dimension.

import numpy as np

# Generates a 5x5 matrix/nested list of random float values
matrix = np.random.rand(5, 5)

# Sets diagonal values to zero
np.fill_diagonal(matrix, 0)

print(matrix)

# Example Matrix
[[0.         0.50388405 0.62069521 0.93842045 0.75608882]
 [0.35300814 0.         0.48357286 0.59268458 0.15213251]
 [0.63591576 0.61143272 0.         0.91271144 0.53997033]
 [0.01979816 0.66435396 0.03416005 0.         0.02524777]
 [0.62694323 0.02307084 0.32458185 0.7663526  0.        ]]

# Accessing specific elements
matrix[1][3]
0.5926845768187102

Documentation here on the functionality. Cheers!