To compute the pairwise Euclidean distance between elements of a NumPy array, excluding the distance with self, you can use the `scipy.spatial.distance.pdist`

function. The `pdist`

function calculates the pairwise distances between the rows of a given array. You can then reshape the output into a square distance matrix without the distances to self. Here's how you can do it:

`python````
import numpy as np
from scipy.spatial.distance import pdist, squareform
# Sample data as a 2D NumPy array
data = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[10, 11, 12]])
# Compute pairwise Euclidean distance using pdist
pairwise_distances = pdist(data)
# Convert the output to a square distance matrix
distance_matrix = squareform(pairwise_distances)
# Set the diagonal values to a large value (e.g., infinity) to exclude distances to self
np.fill_diagonal(distance_matrix, np.inf)
print(distance_matrix)
```

In the example above, `data`

is a 2D NumPy array where each row represents a data point. The `pdist`

function computes the pairwise Euclidean distances between all rows of the array, excluding distances to self. The `squareform`

function then reshapes the output to a square distance matrix.

The resulting `distance_matrix`

will be a 2D NumPy array representing the pairwise Euclidean distances between the rows of `data`

, excluding the distances to self (the diagonal values will be set to infinity).

Make sure to replace the `data`

array with your actual data. The above code snippet assumes that you have already imported the necessary libraries (`numpy`

and `scipy.spatial.distance`

).