# Numpy Determinant | What is NumPy.linalg.det()

Hello geeks, and welcome to this article. We will cover NumPy.linalg.det(), also known as numpy determinant. We will also look at its syntax and parameters for a better overall understanding.

Then, we will see some examples to understand the topic better. But at first, let us try to get a brief understanding of the function through its definition. The determinant is an important topic of linear algebra.

Only the square matrices have determinant values. The NumPy determinant function helps us calculate the determinant value of the input array. It becomes instrumental because the determinant has applications ranging from science, engineering, and economics. Up next, let us look at the syntax associated with this function.

Contents

## Syntax Of Numpy Determinant

``numpy.linalg.det(a)``

Above, we can see the syntax associated with the NumPy determinant. Also, we can see this is a pretty simple syntax with just one parameter. Next, we will discuss the parameter and associated return value.

## Parameter Of Numpy Determinant

a:array_like

This parameter represents the input array over which the operation needs to be performed. Moreover, the input must be similar to that of a square matrix like 2*2,3*3, and so on. Dealing with a 1*1 matrix is not advised. In that case, you will get the same value as that of the matrix.

## Return

det:array_like

It represents the determinant value calculated for the input array.

## Examples Of Numpy Determinant

Now, we are done with all the theory parts. We have covered its syntax and parameters in detail. Now, it’s time to see these in action. By this, I mean to see various examples to help understand the topic better. Let us start with an elementary-level example, and as we move ahead, we will gradually increase the level of the example.

```#import
import numpy as ppool
a=[[2,4],
[3,7]]
print(ppool.linalg.det(a))
```

Output:

```#output
1.9999999999999984
```

In the above example, we have first imported the NumPy module. Afterward, we have defined a 2*2 matrix. Then, we used our syntax with a print statement to get the desired output.

A 2*2 matrix may not be as complicated as a problem and can also be done manually. But now, let us look at a more complicated problemâ€”a bigger matrix, which is far more difficult to calculate manually.

```#input
import numpy as ppool
a=[[2,4,8,9],
[3,7,11,12],
[11,1,34,20],
[39,6,44,56]
]
print(ppool.linalg.det(a))
```

Output:

```#output
-8342.999999999996
```

We have taken a 4*4 cross matrix for observation in the above example. We have followed a procedure similar to the above example by importing the NumPy module. Then declare the input array and, after that, use our syntax to get the desired output. Here, we can see our output justifies our input.

Now, let us look at an example that will teach us what not to do when using this syntax.

```#input
import numpy as ppool
a=[[2,4,8,9],
[3,7,11,12]
]
print(ppool.linalg.det(a))
```

Output:

```#output
---ERROR---
```

As stated above, we should always use a square matrix when dealing with this function. In the above example, we have used a 4*2 matrix. Which is not a square matrix, and we can see that we get an error as output.