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

Hello geeks and welcome in this article, we will cover NumPy.linalg.det(), also known as numpy determinant. Along with that, for an overall better understanding, we will look at its syntax and parameter. Then we will see a couple of examples for a better understanding of the topic. 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 value. The function NumPy determinant helps us by calculating 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.

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## 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. Up next, we will discuss the parameter and return value associated with it.

## 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. It is not advised to deal with a 1*1 matrix. As in that case, you will get the same value as that of the matrix.

## RETURN

det:array_like

It represent the determinant value calculated for the input array.

## EXAMPLES OF NUMPY DETERMINANT

Now we are done with all the theory part. 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 that will help in understanding the topic better. Let us start with an elementary level example, and as we move ahead, we will gradually increase the level of 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 problem—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
```

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

Now let us look at an example which 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, when dealing with this function, we should always use a square matrix. 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. 