Python – K Matrix Initialization

Sometimes in the world of competitive programming, we need to initialise the matrix, but we don’t wish to do it in a longer way using a loop. We need a shorthand for this. This type of problem is quite common in dynamic programming domain. Let’s discuss certain ways in which this can be done. 

Method #1: Using List comprehension List comprehension can be treated as a shorthand for performing this particular operation. In list comprehension, we can initialise the inner list with K and then extend this logic to each row again using the list comprehension. 

Python3

# Python3 code to demonstrate
# K Matrix Initialization
# using list comprehension

# Declaring rows
N = 5

# Declaring columns
M = 4

# initializing K
K = 7

# using list comprehension
# to initializing matrix
res = [ [ K for i in range(N) ] for j in range(M) ]

# printing result
print("The matrix after initializing with K : " + str(res))

Output : 

The matrix after initializing with K : [[7, 7, 7, 7, 7], [7, 7, 7, 7, 7], [7, 7, 7, 7, 7], [7, 7, 7, 7, 7]]

Time Complexity: O(n * m) where n is the number of rows and m is the number of columns
Auxiliary Space: O(n * m) where n is the number of rows and m is the number of columns

Method #2: Using list comprehension + “*” operator This problem can also be simplified using the * operator which can slightly reduce the tedious way task is done and can simply use multiply operator to extent the initialization to all N rows. 

Python3

# Python3 code to demonstrate
# K Matrix Initialization
# using list comprehension
# and * operator

# Declaring rows
N = 5

# Declaring columns
M = 4

# initializing K
K = 7

# using list comprehension
# to initializing matrix
res = [ [K for i in range(M)] * N]

# printing result
print("The matrix after initializing with K : " + str(res))

Output : 

The matrix after initializing with K : [[7, 7, 7, 7, 7], [7, 7, 7, 7, 7], [7, 7, 7, 7, 7], [7, 7, 7, 7, 7]]

Method #3: Using Numpy

Note: Install numpy module using command “pip install numpy”

One can also use the Numpy library to initialise the matrix. Numpy library provides a convenient way to initialise the matrix of given size and fill it with any given value.

Python3

import numpy as np

# Declaring rows
N = 5

# Declaring columns
M = 4

# initializing K
K = 7

# using numpy library
# to initializing matrix
res = np.full((N, M), K)

# printing result
print("The matrix after initializing with K :\n", res)
#This code is contributed by Edula Vinay Kumar Reddy

Output:

The matrix after initializing with K :
[[7 7 7 7]
[7 7 7 7]
[7 7 7 7]
[7 7 7 7]
[7 7 7 7]]

Time Complexity: O(n * m) where n is the number of rows and m is the number of columns
Auxiliary Space: O(n * m) where n is the number of rows and m is the number of columns

Method 4: using a nested for loop. 

Step-by-step approach:

• Declare the number of rows and columns N and M.
• Initialize the value K.
• Create an empty list res to store the matrix.
• Use a nested for loop to iterate over the rows and columns and append K to the matrix.
• Print the matrix.

Below is the implementation of the above approach:

Python3

# Python3 code to demonstrate
# K Matrix Initialization
# using nested for loop

# Declaring rows
N = 5

# Declaring columns
M = 4

# initializing K
K = 7

# creating an empty matrix
res = []

# using nested for loop
for i in range(M):
    row = []
    for j in range(N):
        row.append(K)
    res.append(row)

# printing result
print("The matrix after initializing with K : " + str(res))

Output

The matrix after initializing with K : [[7, 7, 7, 7, 7], [7, 7, 7, 7, 7], [7, 7, 7, 7, 7], [7, 7, 7, 7, 7]]

Time Complexity: O(NM)
Auxiliary Space: O(NM) to store the matrix.

Method #5: Using list multiplication

Step-by-step Approach:

1. Declare the number of rows and columns in the matrix as N and M respectively.
2. Initialize the value of K that will be used to initialize the matrix.
3. Create an empty list res that will be used to store the matrix.
4. Create a list of K values with length N using the list multiplication operator *. This will be used to initialize each row of the matrix.
5. Use a list comprehension to create the matrix by repeating the row M times.
6. Print the resulting matrix.

Python3

# Python3 code to demonstrate K Matrix
# Initialization using list multiplication

# Declaring rows
N = 5

# Declaring columns
M = 4

# initializing K
K = 7

# creating a list of K values with length N
row = [K] * N

# using list multiplication to create the matrix
res = [row for _ in range(M)]

# printing result
print("The matrix after initializing with K : " + str(res))

Output

The matrix after initializing with K : [[7, 7, 7, 7, 7], [7, 7, 7, 7, 7], [7, 7, 7, 7, 7], [7, 7, 7, 7, 7]]

Time Complexity: O(N*M), as each element in the matrix is initialized once.
Auxiliary Space: O(N*M), as the entire matrix is stored in memory.