Distance matrix computation in SciPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a distance matrix in data science?

A distance matrix is a table that shows the distance between each pair of points in a dataset. It helps us understand how close or far points are from each other.

beginner

Which function in scipy computes the distance matrix between points?

The function scipy.spatial.distance_matrix computes the distance matrix between two sets of points.

beginner

How does the Euclidean distance between two points get calculated?

Euclidean distance is the straight-line distance between two points. It is calculated by taking the square root of the sum of squared differences of their coordinates.

beginner

What is the shape of the distance matrix if you have 5 points?

The distance matrix will be a 5x5 square matrix, where each cell shows the distance between two points.

intermediate

Why is computing a distance matrix useful in clustering?

It helps group points that are close together by showing how far apart each pair of points is. This is important for algorithms like k-means or hierarchical clustering.

Which scipy function is used to compute the distance matrix?

A scipy.spatial.distance_matrix

B scipy.linalg.inv

C scipy.optimize.minimize

D scipy.stats.norm

The function scipy.spatial.distance_matrix computes distances between points.

What does each element in a distance matrix represent?

A The difference between two points

B The sum of two points

C The product of two points

D The distance between two points

Each element shows the distance between a pair of points.

If you have 3 points, what will be the size of the distance matrix?

A 3x3

B 1x3

C 3x1

D 1x1

The distance matrix is square with size equal to the number of points.

Which distance metric does scipy.spatial.distance_matrix use by default?

A Cosine distance

B Manhattan distance

C Euclidean distance

D Hamming distance

It uses Euclidean distance by default.

Why might you want to compute a distance matrix before clustering?

A To sort points alphabetically

B To find how close points are to each other

C To calculate the average value of points

D To remove duplicate points

Distance matrices help identify groups of close points for clustering.

Explain what a distance matrix is and why it is useful in data science.

Think about how you measure distance between places on a map.

Describe how to compute a distance matrix using scipy and what the output looks like.

Imagine you have a list of points and want to know how far each is from the others.