In this program, you'll learn to add two matrices using Nested loop and Next list comprehension, and display it.
To understand this example, you should have the knowledge of the following Python programming topics:
- Python for Loop
- Python List
In Python, we can implement a matrix as a nested list [list inside a list]. We can treat each element as a row of the matrix.
For example X = [[1, 2], [4, 5], [3, 6]]
would represent a 3x2 matrix. First row can be selected as X[0]
and the element in first row, first column can be selected as X[0][0]
.
We can perform matrix addition in various ways in Python. Here are a couple of them.
Source code: Matrix Addition using Nested Loop
# Program to add two matrices using nested loop
X = [[12,7,3],
[4 ,5,6],
[7 ,8,9]]
Y = [[5,8,1],
[6,7,3],
[4,5,9]]
result = [[0,0,0],
[0,0,0],
[0,0,0]]
# iterate through rows
for i in range[len[X]]:
# iterate through columns
for j in range[len[X[0]]]:
result[i][j] = X[i][j] + Y[i][j]
for r in result:
print[r]
Output
[17, 15, 4] [10, 12, 9] [11, 13, 18]
In this program we have used nested for
loops to iterate through each row and each column. At each point, we add the corresponding elements in the two matrices and store it in the result.
Source Code: Matrix Addition using Nested List Comprehension
# Program to add two matrices using list comprehension
X = [[12,7,3],
[4 ,5,6],
[7 ,8,9]]
Y = [[5,8,1],
[6,7,3],
[4,5,9]]
result = [[X[i][j] + Y[i][j] for j in range[len[X[0]]]] for i in range[len[X]]]
for r in result:
print[r]
The output of this program is the same as above. We have used nested list comprehension to iterate through each element in the matrix.
List comprehension allows us to write concise codes and we must try to use them frequently in Python. They are very helpful.
Aim : Program to compute the sum of two matrices and then print it in Python.
Examples:
Input : X= [[1,2,3], [4 ,5,6], [7 ,8,9]] Y = [[9,8,7], [6,5,4], [3,2,1]] Output : result= [[10,10,10], [10,10,10], [10,10,10]]
We can perform matrix addition in following ways in Python.
Using for loop:
Implementation:
Python
X
=
[[
1
,
2
,
3
],
[
4
,
5
,
6
],
[
7
,
8
,
9
]]
Y
=
[[
9
,
8
,
7
],
[
6
,
5
,
4
],
[
3
,
2
,
1
]]
result
=
[[
0
,
0
,
0
],
[
0
,
0
,
0
],
[
0
,
0
,
0
]]
for
i
in
range
[
len
[X]]:
for
j
in
range
[
len
[X[
0
]]]:
result[i][j]
=
X[i][j]
+
Y[i][j]
for
r
in
result:
print
[r]
Output
[10, 10, 10] [10, 10, 10] [10, 10, 10]
Time Complexity: O[len[X] * len[X[0]]], as we are using nested loop for traversing the matrix.
Auxiliary Space: O[len[X] * len[X[0]]], as we are using a result matrix which is extra space.
Another Approach:
- Explanation :- In this program we have used nested for loops to iterate through each row and each column. At each point we add the corresponding elements in the two matrices and store it in the result.
- Using nested list comprehension : In Python, we can implement a matrix as nested list [list inside a list]. We can treat each element as a row of the matrix.
Implementation:
Python
X
=
[[
1
,
2
,
3
],
[
4
,
5
,
6
],
[
7
,
8
,
9
]]
Y
=
[[
9
,
8
,
7
],
[
6
,
5
,
4
],
[
3
,
2
,
1
]]
result
=
[[X[i][j]
+
Y[i][j]
for
j
in
range
[
len
[X[
0
]]]]
for
i
in
range
[
len
[X]]]
for
r
in
result:
print
[r]
Output
[10, 10, 10] [10, 10, 10] [10, 10, 10]
Time Complexity: O[len[X] * len[X[0]]], as we are using nested loop for traversing the matrix.
Auxiliary Space:
O[len[X] * len[X[0]]], as we are using a result matrix which is extra space.
Another Approach:
- Explanation:-
The output of this program is the same as above. We have used nested list comprehension to iterate through each element in the matrix. List comprehension allows us to write concise codes and we must try to use them frequently in Python. They are very helpful. - Using zip[] and sum
Implementation:
Python
X
=
[[
1
,
2
,
3
],
[
4
,
5
,
6
],
[
7
,
8
,
9
]]
Y
=
[[
9
,
8
,
7
],
[
6
,
5
,
4
],
[
3
,
2
,
1
]]
result
=
[
map
[
sum
,
zip
[
*
t]]
for
t
in
zip
[X, Y]]
print
[result]
Output
[[10, 10, 10], [10, 10, 10], [10, 10, 10]]
Time Complexity: O[len[X] * len[X[0]]], as we are using
zip function.
Auxiliary Space: O[len[X] * len[X[0]]], as we are using extra space.
Explanation :- The zip function accepts iterator i of each element[list] of matrix, mapping them, adding them using sum[] and storing them in the map form.
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