l = [[98, 25, 33,  9, 41],
[67, 32, 67, 27, 85],
[38, 79, 52, 40, 58],
[84, 76, 44,  9,  2]]

def fnd(l,value):
    for i,v in enumerate(l):
        if value in v:
            return {'row':i+1,'col':v.index(value)+1}
    return {'row':-1,'col':-1}
print(fnd(l,40))

{'row': 3, 'col': 4}

The idiomatic way to create a 2D array in Python is:

rows,cols = 5,10
A = [[0]*cols for _ in range(rows)]

Explanation:

>>> A = [0] * 5  # Multiplication on a list creates a new list with duplicated entries.
>>> A
[0, 0, 0, 0, 0]
>>> A = [[0] * 5 for _ in range(2)] # Create multiple lists, in a list, using a comprehension.
>>> A
[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
>>> A[0][0] = 1
>>> A
[[1, 0, 0, 0, 0], [0, 0, 0, 0, 0]]

Note you do not want to create duplicate lists of lists. It duplicates the list references so you have multiple references to the same list:

>>> A = [[0] * 5] * 2
>>> A
[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]
>>> A[0][0] = 1
>>> A
[[1, 0, 0, 0, 0], [1, 0, 0, 0, 0]]  # both rows changed!

Such arrays are sometimes called tensors, although you can see there are other definitions that become more relevant in general relativity and abstract algebra. Specifically in the literature on tensor decomposition we find a more specialized lexicon.

For a 3-mode tensor (analogous to 3d array) there are special names:

• Mode 1: columns
• Mode 2: rows
• Mode 3: tubes

The following diagram from Kolda and Bader 2009 is quite clarifying.

But since such tensors are often $n$-modal it is desirable to have the terminology of a mode-$k$ fiber for the $k$th mode of the tensor.

We can access the ith, jth tube in the example array given as follows:

import numpy as np

X = np.array([[[ 0,  1],
        [ 2,  3],
        [ 4,  5]],

       [[ 6,  7],
        [ 8,  9],
        [10, 11]],

       [[12, 13],
        [14, 15],
        [16, 17]]])

# Let us pick (i,j) = (0,1)
i = 0
j = 1

print(X[i,j,:])

>>> import numpy as np
>>> A = np.array([[1,2,3,4],[5,6,7,8]])

>>> A
array([[1, 2, 3, 4],
    [5, 6, 7, 8]])

>>> A[:,2] # returns the third columm
array([3, 7])

See also: "numpy.arange" and "reshape" to allocate memory

Example: (Allocating a array with shaping of matrix (3x4))

nrows = 3
ncols = 4
my_array = numpy.arange(nrows*ncols, dtype='double')
my_array = my_array.reshape(nrows, ncols)