Distance matrix computation in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that computes pairwise distances.

SciPy

from scipy.spatial.distance import [1]

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Aeuclidean

Bcdist

Cpdist

Dsquareform

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing cdist which is for distances between two different sets.

Choosing squareform which converts distance vectors to matrices.

The pdist function computes pairwise distances between points in a single set.

2fill in blank

medium

Complete the code to compute the pairwise Euclidean distances of points in array X.

SciPy

distances = [1](X, metric='euclidean')

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Apdist

Bcdist

Csquareform

Deuclidean

Attempts:

3 left

💡 Hint

Common Mistakes

Using cdist with only one array causes an error.

Using squareform directly on the data instead of on distances.

pdist computes pairwise distances within one set of points.

3fill in blank

hard

Fix the error in the code to convert the condensed distance vector d to a square matrix.

SciPy

distance_matrix = [1](d)

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Asquareform

Bcdist

Ceuclidean

Dpdist

Attempts:

3 left

💡 Hint

Common Mistakes

Using pdist or cdist which expect raw data, not condensed distances.

Using euclidean which is a metric, not a function to convert formats.

squareform converts condensed distance vectors to square matrices.

4fill in blank

hard

Fill both blanks to compute the distance matrix between two different sets X and Y using the cosine metric.

SciPy

dist_matrix = [1](X, [2], metric='cosine')

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Acdist

Bpdist

CY

DX

Attempts:

3 left

💡 Hint

Common Mistakes

Using pdist which only takes one array.

Passing X as the second argument instead of Y.

cdist computes distances between two sets; the second argument must be the other set Y.

5fill in blank

hard

Fill all three blanks to create a dictionary mapping each point's index to its Euclidean distance from the origin (0,0).

SciPy

origin = [0, 0]
distances = {i: [1](point, [2]) for i, point in enumerate(X)}
sorted_distances = sorted(distances.items(), key=lambda x: x[[3]])

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Acdist

Borigin

C1

Deuclidean

Attempts:

3 left

💡 Hint

Common Mistakes

Using cdist which requires arrays, not single points.

Sorting by x[0] which sorts by index, not distance.

Use euclidean metric to compute distance from origin. Sorting by x[1] sorts by distance values.