Vectors and Matrices in Numpy - Lab

Introduction

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.

Objectives

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.

1. Define two arrays A (4x2) and B (2x3)

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]]

2. Print the dimensions of A and B

# Code Here

Shape of A: (4, 2)
Shape of B: (2, 3)

3. Print some of the elements from A at following locations

first row and first column
first row and second column
third row and second column
fourth row and first column

# Code Here

4. Write a routine to populate matrix with random data

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.]]

5. Turn above routine into a function.

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.]]

Summary

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.

rvent / dsc-2-13-08-linalg-vector-matrices-numpy-lab-nyc-ds-career-012819