dsc-linalg-vector-matrices-numpy-lab-houston-ds-082619's Introduction

Vectors and Matrices in Numpy - Lab

Introduction

In this lab, you'll solve some simple matrix creation and manipulation exercises based on what you've learned so far in this section. The key takeaway here is to be able to understand how to use indexing with matrices and vectors while applying some basic operations.

Objectives

In this lab you will:

Find the shape of vectors and matrices
Access and manipulate individual scalar components of a matrix
Create vectors and matrices using Numpy and Python

1. Define two arrays $A$ with shape $ (4 \times 2)$ and $B$ with shape $(2 \times 3)$

So $A =
\left[ {\begin{array}{cc} 1402 & 191 \ 1371 & 821\ 949 & 1437 \ 147 & 1448 \ \end{array} }\right] $ and $ B =
\left[ {\begin{array}{ccc} 1 & 2 & 3 \ 4 & 5 & 6\ \end{array} }\right] $

# Code here

2. Print the dimensions of $A$ and $B$

# Code here

3. Print elements from $A$

Print the following elements from $A$:

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 a matrix with random data

Create an $(3 \times 3)$ 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

5. Turn the above routine into a function

Create two $(4 \times 4)$ zero-valued matrices and fill with random data using the function
Add the matrices together in Numpy
Print the results

# Code here

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 matrix inversions.

dsc-linalg-vector-matrices-numpy-lab-houston-ds-082619's People