How Do I Find The Minimum Of A Numpy Matrix? (In This Particular Case)

I have a numpy matrix as follows [['- A B C D E'] ['A 0 2 3 4 5'] ['B 2 0 3 4 5'] ['C 3 3 0 4 5'] ['D 4 4 4 0 5'] ['E 5 5 5 5 0']] How do I find the minimum in this matrix along

Solution 1:

You need to go back to the drawing board with your 'numpy' matrix, that is not an matrix, but a list of list of (single) string.

x =['- A B C D E',
'A 0 2 3 4 5',
'B 2 0 3 4 5',
'C 3 3 0 4 5',
'D 4 4 4 0 5',
'E 5 5 5 5 0']

# Preprocess this matrix to make it a matrix
x = [e.split() for e in x]
numbers = set("0123456789")
xr = [[float(e) if all(c in numbers for c in e) and e != "0" else float("inf") for e in l] for l in x]

Everything that's not a number or 0 is marked as float(inf) to not get into the way of minimum calculation:

[[inf, inf, inf, inf, inf, inf],
 [inf, inf, 2.0, 3.0, 4.0, 5.0],
 [inf, 2.0, inf, 3.0, 4.0, 5.0],
 [inf, 3.0, 3.0, inf, 4.0, 5.0],
 [inf, 4.0, 4.0, 4.0, inf, 5.0],
 [inf, 5.0, 5.0, 5.0, 5.0, inf]]

You can then easily use numpy's argmin and unravel_index to get what you want.

xrn = np.array(xr)
index = np.unravel_index(np.argmin(xrn), xrn.shape)

# RESULT: (1, 2)