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!

How I would initialize a matrix from lists in python:

w = 3
h = 3
a = []
for i in range(h):
    a.append([0]*w)

The syntax you found on the internet is incorrect as what I'm assuming it's trying to do is create a list of lists of lists, the innermost lists containing just one integer each. However trying to run it produces a syntax error as the brackets aren't even closed properly. Moreover, trying to initialize a matrix in one line would result in the exact same 1D list being copied multiple times in the 2D matrix, so attempting to modify one will modify every row.

Here are my methods for extending number of rows:

a.append([0]*len(a[0]))

number of columns:

for i in range(len(a)):
    a[i].append(0)

However I would strongly, strongly suggest numpy in large projects as there is nothing stopping you here from modifying the length of just one row, hence corrupting your entire matrix.

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,:])

Like this:

numrows = len(input)    # 3 rows in your example
numcols = len(input[0]) # 2 columns in your example

Assuming that all the sublists have the same length (that is, it's not a jagged array).