# Numpy – Get the Upper Triangular Matrix (With Examples)

The Numpy library in Python comes with a number of useful functions to work with and manipulate the data in arrays. In this tutorial, we will look at how to get the upper triangular matrix from a 2d array in Numpy.

## How to get the upper triangular matrix in Numpy?

You can use the numpy built-in `numpy.triu()` function to get the upper triangular matrix from a 2d Numpy array. Pass the array as an argument to the function.

The following is the syntax –

`numpy.triu(m, k)`

The `numpy.tril()` function takes the following parameters –

1. `m` – The input array for which you want to get the upper triangular matrix. For arrays with dimensions greater than 2, the function will apply to the final two axes.
2. `k` – The diagonal below which to zero the elements. It is `0` (the main diagonal) by default. Diagonals below the main diagonal have `k < 0` and the ones above the main diagonal have `k > 0`.

It returns a numpy array (the upper triangular matrix of the passed array) with elements below the specified diagonal as 0.

## Examples

Let’s now look at examples of using the above syntax to get the upper triangular matrix from a 2d array.

First, we will create a Numpy array that we will use throughout this tutorial.

```import numpy as np

# create a 2D numpy array
arr = np.array([
[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[10, 11, 12]
])
# display the matrix
print(arr)```

Output:

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```[[ 1  2  3]
[ 4  5  6]
[ 7  8  9]
[10 11 12]]```

Here, we used the `numpy.array()` function to create a 2d array of shape 4×3 (having 4 rows and 3 columns).

### Example 1 – Get the upper triangular matrix with the default diagonal

Let’s now use the `numpy.triu()` function to get the upper triangular matrix for the 2d array created above. We will use the default diagonal (`k = 0`).

```# get the upper triangular matrix
utm_arr = np.triu(arr)
# display the matrix
print(utm_arr)```

Output:

```[[1 2 3]
[0 5 6]
[0 0 9]
[0 0 0]]```

We get the upper triangular matrix as a numpy array. You can see that the values below the main diagonal are zero in the returned matrix.

### Example 2 – Get the upper triangular matrix with a custom diagonal

In the above example, we used the main diagonal to compute our upper triangular matrix.

The `numpy.triu()` function comes with an optional parameter, `k` that you can use to specify the diagonal you want to use for computing the upper triangular matrix.

The below image better illustrates the different values of `k` (representing different diagonals) for our input array.

`k` is `0` by default. The diagonals below the main diagonal have `k < 0` and the diagonals above it have `k > 0`.

Let’s use `k = -1` to get the upper triangular matrix.

```# get the upper triangular matrix
utm_arr = np.triu(arr, k=-1)
# display the matrix
print(utm_arr)```

Output:

```[[ 1  2  3]
[ 4  5  6]
[ 0  8  9]
[ 0  0 12]]```

The resulting upper triangular matrix has values below the diagonal, `k = -1` as zeros.

## Summary

In this tutorial, we looked at how to get the upper triangular matrix of a 2d array in Numpy. The following are the key takeaways from this tutorial.

• Use the `numpy.triu()` function to get the upper triangular matrix of an array. Pass the array as an argument.
• You can specify the diagonal below which you want to keep the values zero using the optional parameter `k`. By default, it represents the main diagonal, `k = 0`.

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