**How to Create a Matrix in Python** | Here, we will discuss how to create a matrix in python? A matrix is a rectangular table arranged in the form of rows and columns. In the programming world, we implement a matrix by using arrays by classifying them as 1D and 2D arrays. In this section, there are some examples to create a matrix in python. Also See:- List in Python MCQ

**We will see these below Python program examples to create a Matrix:**–

- How To Create Matrix In Python Using Numpy
- How To Create A Matrix In Python Without Numpy
- How To Create A Matrix In Python Using For Loop
- How To Create An Empty Matrix In Python
- How To Create A Zero Matrix In Python
- How To Create A 2d Matrix In Python
- How To Create A 3×3 Identity Matrix In Python

## How to Create a Matrix in Python using NumPy

In the Python programming language, NumPy is a library that supports single and multi-dimensional arrays, matrices, and a large collection of high-level mathematical functions to operate on these arrays and matrices. It contains lots of pre-defined functions which can be called on these matrices and it will simplify our task.

### Make a Matrix in Python Using NumPy.array()

Now let us see a simple program to create/make a matrix in Python using NumPy. In this code, we will use **numpy.array()** to **make/create a matrix**. NumPy.array() returns an array object satisfying the specified requirements given in the parameter.

```
import numpy as np
num = np.array([[1, 1, 2], [3, 5, 3], [5, 6, 9]])
print(num)
```

Output:-

[ [1, 1, 2]

[3, 5, 3]

[5, 6, 9] ]

In the above code, we have imported NumPy as

, then in variable **np**

, we stored an array by using the function numpy.array(). However, we can do this in a mathematical way also but it would be a long sequence. That is why using the NumPy library can be a better way of coding in the Python programming language, and makes our task very easy.**num**

### Python Create a Matrix Using NumPy.matrix()

The **numpy.matrix() **is also a function in NumPy that is used to **return an array, it works very similarly to numpy.array()**.

```
import numpy as np
num = np.matrix(([1, 1, 1], [15, 34, 3], [50, 69, 99]))
print(num)
```

Output:-

`[[1 1 1]`

[15 34 3]

[50 69 99]]

### Create a Matrix in Python Using NumPy.reshape()

The **numpy.reshape() is a function in NumPy that converts a 1D array to 2D. **

```
import numpy as np
num = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9]]).reshape(3, 3)
print(num)
```

Output:-

[ [1, 2, 3]

[4, 5, 6]

[7, 8, 9] ]

The reshape() function takes 2 parameters that are row and column. It is basically used to alter the size of the array. In the above example reshape() takes 2 parameters 3 and 3 so it converts 1D array to 2D array 3X3 elements.

### Make a Matrix in Python Using NumPy.append()

The **numpy.append() function** appends an array into the row. It will **append values to the end of an array.**

```
import numpy as np
num = np.array([[5, 7, 8]])
new = np.append(num, [[4, 5, 6], [4, 9, 0], [2, 3, 1]], axis=0)
print(new)
```

Output:-

[ [5, 7, 8]

[4, 5, 6]

[4, 9, 0]

[2, 3, 1] ]

In the above code, the array num is appended to new so the array gets altered. The axis parameter is set 0 as we wish to append the elements as rows. We have also used shape as it displays the dimension of the matrix. In the above example, we have created a matrix with 4 rows and 3 columns hence dimension is (4,3).

## How to Create a Matrix in Python Without NumPy

NumPy library contains various methods to create a matrix but it also can be created without using NumPy. The below code creates a 2D matrix using the nested list. A nested list is a list within a list.

```
m = [[5, 3, 7], [7, 6, 7], [1, 6, 0]]
for i in m:
print(i)
```

Output:-

[5, 3, 7]

[7, 6, 7]

[1, 6, 0]

In the example shown we have declared variable m as a nested list and then used a for loop to print all the elements, without a for loop the elements have been printed in a single line.

**We can do this by using a join function**

```
m = ([5, 3, 7], [7, 6, 7], [1, 6, 0])
for i in m:
print("". join(str(i)))
```

Output:-

[5, 6, 7]

[7, 6, 7]

[1, 6, 0]

The above shown both examples give the same output but the difference is join() function joins the elements of a row into a single string before printing it.

## How to Create a Matrix in Python Using For Loop

We can also use for loop to create a matrix.

**Step 1:** We have first a single list **mat****Step 2:** Then we iterate by for loop to print it twice using a range within the list it will change into nested list acting as a matrix

```
mat = [[3, 8, 9] for i in range(2)]
for i in mat:
print("".join(str(i)))
```

Output:-

[3, 8, 9]

[3, 8, 9]

In the above output, we have printed the list twice by giving the range parameter as 2.

## How to Create an Empty Matrix in Python

In the below-shown example we have used a library called NumPy, there are many inbuilt functions in NumPy which makes coding easy. Here we have used one such function called empty() which creates an empty matrix.

```
import numpy as np
emp = np.empty((0, 4))
print(emp)
```

Output:-

[ ]

The above code has created a matrix with 0 rows and 4 columns.

## How to Create a Zero Matrix in Python

A zero matrix is a matrix that contains all 0 elements. The zeros() is a function in the NumPy library that creates a zero matrix, here

is used to specify the data type of the elements.**dtype**

```
import numpy as np
mat = np.zeros([2, 2], dtype=int)
print(mat)
```

Output:-

[[0, 0]

[0, 0]]

The above code creates a zero matrix with 2 rows and 2 columns.

## How to Create a 2D Matrix in Python

We can create a 2D matrix in python by using a nested list.

```
m = [[4, 1, 2], [7, 5, 3], [9, 6, 9]]
for i in m:
print("".join(str(i)))
```

Output:-

[4, 1, 2]

[7, 5, 3]

[9, 6, 9]

The above output shows a 2-dimensional matrix.

## How to Create a 3X3 Identity Matrix in Python

A 3X3 matrix is a matrix that has 3 rows and 3 columns, and an identity matrix is a matrix whose diagonal elements are always 1. The function np.identity() itself creates a identity matrix of 3 rows and 3 columns.

```
import numpy as np
a = np.identity(3)
print(a)
```

Output:-

[[1. 0. 0.]

[0. 1. 0.]

[0. 0. 1.]]

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