Multiplying matrices is a common operation in linear algebra, and it can be easily performed in Python using the NumPy library. Here is a step-by-step guide on how to multiply two matrices in Python:
Import the NumPy library: Start by importing the NumPy library into your Python environment. This can be done by using the following command: import numpy as np
.
Define the matrices: Create two matrices using the np.array()
function. For example, you can create two matrices A and B as follows:
Examples:
A = np.array([[1,2],[3,4]]) B = np.array([[5,6],[7,8]])
Multiply the matrices: To multiply two matrices, simply use the np.dot()
function. For example, to multiply the two matrices A and B, you can use the following command: C = np.dot(A, B)
. The result will be stored in the matrix C.
Display the result: Finally, to display the result, simply print the matrix C. For example, you can use the following command: print(C)
.
That's it! You have successfully multiplied two matrices in Python using NumPy. You can now use this knowledge to perform matrix operations in your linear algebra projects.
Method 2: Matrix Multiplication Using Nested List. We use zip in Python.
# Program to multiply two matrices using list comprehension # take a 3x3 matrix A = [[12, 7, 3], [4, 5, 6], [7, 8, 9]] # take a 3x4 matrix B = [[5, 8, 1, 2], [6, 7, 3, 0], [4, 5, 9, 1]] # result will be 3x4 result = [[sum(a * b for a, b in zip(A_row, B_col)) for B_col in zip(*B)] for A_row in A] for r in result: print(r)
Output:
[114, 160, 60, 27] [74, 97, 73, 14] [119, 157, 112, 23]
Method 3: Matrix Multiplication (Vectorized implementation).
# Program to multiply two matrices (vectorized implementation) # Program to multiply two matrices (vectorized implementation) import numpy as np # take a 3x3 matrix A = [[12, 7, 3], [4, 5, 6], [7, 8, 9]] # take a 3x4 matrix B = [[5, 8, 1, 2], [6, 7, 3, 0], [4, 5, 9, 1]] # result will be 3x4 result= [[0,0,0,0], [0,0,0,0], [0,0,0,0]] result = np.dot(A,B) for r in result: print(r)
Output:
[114, 160, 60, 27] [74, 97, 73, 14] [119, 157, 112, 23]