# numpy Matrix for linear algebra

## Hello readers, I welcome you to this second part of the tutorial series where I talk about the next important topic numpy matrix .

First of let me share the the different items that we are going to cover here in this page. As our focus is matrix using the numpy library, I shall divide the topic in below sections.

- First I shall share an Introduction
- Then I shall talk about the different matrix operations using numpy
- Finally I shall show how we can access the values from a matrix

**Introduction**

### What is a `matrix`

?

Firstly a matrix is a data structure to store value in a row/column manner. And here if the collection contains “m” rows and “n” columns, then we say that the size of the matrix is `m*n`

Therefore if you take a look at the below image you will see that the matrix contains total 4 elements ( 2 rows and 2 columns ) . Thus the size is `2*2 = 4`

Next what are the `i`

and `j`

doing here ?

Well to answer to that, `i`

and` j`

are simple the index to access the `row`

and `column`

positions respectively

And just to make things clear, the `R`

represents real number

### What is a `vector`

then ?

To answer the question, it is sufficient to just mention that a `vector`

has just one column. Therefore the size of any `vector`

is always `m*1`

**What is a Python matrix ?**

Well there is no data-type in python named matrix, but we can represent a matrix using list.

And here is how a matrix is represented in python

**Original matrix**

**Python version of the original matrix**

Here we are representing each row with a `List`

data type.

**Why we need **`numpy`

if python list can represent a matrix ?

`numpy`

if python list can represent a matrix ?Well this is a very good point, and the simple answer would be “it’s complicated”.

As working with `List`

data-type may appear easy for simple and smaller dimension matrix. But as soon as the dimension grows or the size of the matrix grows, using `List`

also becomes complicated. And that’s why there is `numpy`

for rescue.Check here for more details

**How to create a matrix using numpy ?**

First I need to mention that `numpy`

leaverages a function called `array()`

for both `vector`

and `matrix`

,

**Let’s create a vector with numpy **

import numpy as np a = np.array([1, 2, 3]) print(a) # Output: [1, 2, 3] print(type(a)) # Output: <class 'numpy.ndarray'>

And you spotted the `ndarray`

precisely. The datatype from numpy is `ndarray`

**Let’s create a matrix with numpy **

Well as might have already guessed, the same array() function can be used for matrix too. Here is how it is done .

import numpy as np A = np.array([[1, 2, 3], [ 4, 5, 6]]) print(A)

### More on numpy array

Let’s see a few more utilities of the `numpy array() `

function.

I shall show how we can create arrays with different data types ( for example -> float type , as float type array is used vastly in machine learning applications), and also ways to create array of `zeros`

and `ones`

**create np array with float type**

import numpy as np A = np.array([[1.1, 2, 3], [3, 4, 5]]) # Array of floats print(A)

**Output of the above would be **

[[1.1 2. 3. ]

[3. 4. 5. ]]

**create np array with complex type**

import numpy as np A = np.array([[1, 2, 3], [3, 4, 5]], dtype = complex) # Array of complex numbers print(A)

**Output of the above would be **

[[1.+0.j 2.+0.j 3.+0.j]

[3.+0.j 4.+0.j 5.+0.j]]

**Array of zeros and ones:**

import numpy as np zeors_array = np.zeros( (2,4) ) print(zeors_array) ''' Output: [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] ''' ones_array = np.ones( (2, 3), dtype=np.int32 ) // specifying dtype print(ones_array) # Output: [[1 1 1 1 1 1]]

### Using `arange() `

and `shape()`

import numpy as np A = np.arange(4) print('A =', A) B = np.arange(12).reshape(2, 6) print('B =', B) ''' Output: A = [0 1 2 3] B = [[ 0 1 2 3 4 5] [ 6 7 8 9 10 11]] '''

**matrix operations**

First to talk about operations, I want to mention the ones that are going to be very useful.

And the most common ones are

- Addition
- Multiplication
- Transpose

### Addition

### Multiplication

Element wise multiplication

Let’s first see how the element wise multiplication works. And for this we will be using `multiply()`

function from `numpy`

library

import numpy as np arr1 = np.array([[1, 2], [3, 4]]) arr2 = np.array([[5, 6], [7, 8]]) arr_result = np.multiply(arr1, arr2) print(arr_result)

If you have guessed already, the result will be like this,

[[ 5 12]

[21 32]]

matrix multiplication using

`matmul()`

function

First take a look at the below diagram that shows how the multiplication is performed

Here you see how the multiplication is done actually.

Now take an example and illustrate how the multiplication would look like. For this let’s assume that we the below two matrices.

Now from the illustration you may be already guessing how the values would be for each position !

Here is how they would look like

So now you may be getting the final result of the multiplication :

**do you notice something ? Like a pattern . Actually the result is called and identity matrix and the two matrices A and B are called Inverse matrix.**

Let’s see how can we code the above example

import numpy as np arr1 = np.array([[1, 2, 1], [4, 4, 5], [6, 7, 7]]) arr2 = np.array([[-7, -7, 6], [2, 1, -1], [4, 5, -4]]) arr_result = np.matmul(arr1, arr2) print(f'Matrix Product of arr1 and arr2 is:\n{arr_result}')

### Transpose

Now let’s focus on what a transpose matrix is !

Let’s consider that we have the below metrix.

Now if we apply the `transpose()`

function from the `numpy`

library, then the matrix will become like this

So let’s see how can we code the same

import numpy as np A = np.array([[1, 4], [-2, 3]]) print(A.transpose()) ''' Output: [[ 1 -2] [ 4 3]] '''

**Access the values from a matrix**

elements

Simillarly, just like in the case of python lists, we can access matrix elements using index. Let’s start with a one-dimensional NumPy array.

import numpy as np A = np.array([1, 2, 3, 4, 5]) print("A[0] =", A[0]) # First element print("A[2] =", A[2]) # Third element print("A[-1] =", A[-1]) # Last element

Now if I run this code, it will give an output like this

A[0] = 1

A[2] = 3

A[-1] = 5

Now let’s see how we can access values from a 2-dimensional array

2D array

import numpy as np A = np.array([[1, 4, 5, 12], [-5, 8, 9, 0], [-6, 7, 11, 19]]) # First element of first row print("A[0][0] =", A[0][0]) # Third element of second row print("A[1][2] =", A[1][2]) # Last element of last row print("A[-1][-1] =", A[-1][-1])

And we run the program, the output will be:

A[0][0] = 1

A[1][2] = 9

A[-1][-1] = 19

### Access rows of a Matrix

import numpy as np A = np.array([[1, 4, 5, 12], [-5, 8, 9, 0], [-6, 7, 11, 19]]) print("A[0] =", A[0]) # First Row print("A[2] =", A[2]) # Third Row print("A[-1] =", A[-1]) # Last Row (3rd row in this case)

When we run the program, the output will be:

A[0] = [1, 4, 5, 12]

A[2] = [-6, 7, 11, 19]

A[-1] = [-6, 7, 11, 19]

### Access columns of a Matrix

import numpy as np A = np.array([[1, 4, 5, 12], [-5, 8, 9, 0], [-6, 7, 11, 19]]) print("A[:,0] =",A[:,0]) # First Column print("A[:,3] =", A[:,3]) # Fourth Column print("A[:,-1] =", A[:,-1]) # Last Column (4th column in this case)

When we run the program, the output will be:

A[:,0] = [ 1 -5 -6]

A[:,3] = [12 0 19]

A[:,-1] = [12 0 19]

If you don’t know how this above code works, read slicing of a matrix section of this article.

### Slicing of a Matrix

Slicing of a one-dimensional NumPy array is similar to a list. If you don’t know how slicing for a list works, visit Understanding Python’s slice notation.

Let’s take an example:

import numpy as np letters = np.array([1, 3, 5, 7, 9, 7, 5]) # 3rd to 5th elements print(letters[2:5]) # Output: [5, 7, 9] # 1st to 4th elements print(letters[:-5]) # Output: [1, 3] # 6th to last elements print(letters[5:]) # Output:[7, 5] # 1st to last elements print(letters[:]) # Output:[1, 3, 5, 7, 9, 7, 5] # reversing a list print(letters[::-1]) # Output:[5, 7, 9, 7, 5, 3, 1] [/pyhton] Now, let's see how we can slice a matrix. import numpy as np A = np.array([[1, 4, 5, 12, 14], [-5, 8, 9, 0, 17], [-6, 7, 11, 19, 21]])

print(A[:2, :4]) # two rows, four columns ''' Output: [[ 1 4 5 12] [-5 8 9 0]] '''

print(A[:1,]) # first row, all columns ''' Output: [[ 1 4 5 12 14]] '''

print(A[:,2]) # all rows, second column ''' Output: [ 5 9 11] '''

print(A[:, 2:5]) # all rows, third to the fifth column '''Output: [[ 5 12 14] [ 9 0 17] [11 19 21]] '''

As you can see, using `NumPy`

(instead of nested lists) makes it a lot easier to work with matrices, and we haven’t even scratched the basics. We suggest you to explore NumPy package in detail especially if you trying to use Python for data science/analytics.