This lab will ask you to perform some simple matrix creation and manipulation exercises based on what we have covered so far in this section. The key takeaway here for you is to be able to understand how to using indexing with matrices and vectors while applying some basic operations.
You will be able to:
- Define vectors and matrices in NumPy
- Check the shape of vectors and matrices
- Access and manipulate individual scalar components of a matrix.
A = 1402, 191
1371, 821
949, 1437
147, 1448
B = 1, 2, 3
4, 5, 6
```
```python
# Code Here
A=
[[1402 191]
[1371 821]
[ 949 1437]
[ 147 1448]]
B=
[[1 2 3]
[4 5 6]]
# Code Here
Shape of A: (4, 2)
Shape of B: (2, 3)
- first row and first column
- first row and second column
- third row and second column
- fourth row and first column
# Code Here
1402
191
1437
147
- Create an 3x3 numpy array with all zeros (use
np.zeros()
) - Access each location i,j of this matrix and fill in random values between the range 1 and 10.
# Code Here (due to random data , your output might be different)
before random data:
[[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]]
after random data:
[[2. 7. 5.]
[7. 9. 3.]
[6. 5. 9.]]
- Create two 4x4 zero valued matrices and fill with random data using the function
- Add the matrices together in numpy
- Show the results
# Code Here (due to random data , your output might be different)
Final output
[[12. 4. 13. 8.]
[13. 5. 9. 9.]
[11. 11. 11. 17.]
[16. 14. 8. 10.]]
In this lab, we saw how to create and manipulate vectors and matrices in numpy. We shall now move forward to learning more complex operations including dot products and inverses.