To compute pairwise distances quickly in Python, you can use the `scipy.spatial.distance.pdist`

function. This function efficiently computes pairwise distances between observations in a dataset.

Here’s an example:

```
from scipy.spatial.distance import pdist
# Sample data
data = [[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]
# Compute pairwise distances
distances = pdist(data)
print(distances)
```

In this example, we have a sample dataset represented by the `data`

list. The `pdist`

function is called with the `data`

as the input. It computes the pairwise distances between all pairs of observations in the dataset. The resulting distances are returned as a condensed distance matrix.

The `pdist`

function supports various distance metrics, such as Euclidean distance, Manhattan distance, and cosine distance. By default, it computes the Euclidean distance. You can specify the distance metric by passing the `metric`

parameter to the `pdist`

function.

The `pdist`

function efficiently computes pairwise distances by utilizing optimized algorithms and data structures. It is particularly useful for large datasets where computing all pairwise distances explicitly would be computationally expensive and memory-consuming.

Note that the `pdist`

function returns the distances in a condensed form, which is a one-dimensional array representing the upper triangular part of the pairwise distance matrix. If you need the pairwise distance matrix in a square form, you can use the `scipy.spatial.distance.squareform`

function to convert the condensed distances to a square matrix.

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