Distance computation (distance.cdist) in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that computes distances between two sets of points.

SciPy

from scipy.spatial.distance import [1]

points1 = [[0, 0], [1, 1]]
points2 = [[1, 0], [2, 2]]
distances = [1](points1, points2)
print(distances)

Drag options to blanks, or click blank then click option'

Apdist

Bcdist

Ceuclidean

Dsquareform

Attempts:

3 left

💡 Hint

Common Mistakes

Using pdist instead of cdist

Importing euclidean directly instead of cdist

Not importing the function at all

The cdist function computes distances between two collections of points.

2fill in blank

medium

Complete the code to compute Euclidean distances between two sets of points using cdist.

SciPy

from scipy.spatial.distance import cdist

points1 = [[0, 0], [1, 1]]
points2 = [[1, 0], [2, 2]]
distances = cdist(points1, points2, metric=[1])
print(distances)

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A'cosine'

B'hamming'

C'euclidean'

D'cityblock'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cityblock' which computes Manhattan distance

Using 'cosine' which computes cosine similarity

Using metric without quotes

The metric parameter set to 'euclidean' computes straight-line distances.

3fill in blank

hard

Fix the error in the code to correctly compute Manhattan distances between two sets of points.

SciPy

from scipy.spatial.distance import cdist

points1 = [[0, 0], [1, 1]]
points2 = [[1, 0], [2, 2]]
distances = cdist(points1, points2, metric=[1])
print(distances)

Drag options to blanks, or click blank then click option'

Amanhattan

B'manhattan'

Ccityblock

D'cityblock'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing metric without quotes

Using 'manhattan' which is not recognized

Using cityblock without quotes

The metric parameter must be a string. For Manhattan distance, use 'cityblock' as a string.

4fill in blank

hard

Fill both blanks to create a dictionary of distances from points1 to points2 using cosine metric and convert the result to a list.

SciPy

from scipy.spatial.distance import cdist

points1 = [[1, 0], [0, 1]]
points2 = [[1, 1], [0, 0]]
dist_matrix = cdist(points1, points2, metric=[1])
dist_list = dist_matrix.[2]()
print(dist_list)

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A'cosine'

Btolist

Cflatten

D'euclidean'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'euclidean' instead of 'cosine'

Using flatten() which returns a flattened array, not a list

Not calling tolist() as a method

Use 'cosine' as the metric string and tolist() to convert the numpy array to a Python list.

5fill in blank

hard

Fill all three blanks to compute Chebyshev distances between two sets of points and print the maximum distance value.

SciPy

from scipy.spatial.distance import cdist

points1 = [[1, 2], [3, 4]]
points2 = [[5, 6], [7, 8]]
dist_matrix = cdist(points1, points2, metric=[1])
max_dist = dist_matrix.[2]()
print(max_dist[3])

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A'chebyshev'

Bmax

C()

Dmax()

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'chebyshev' without quotes

Using max() as an attribute without parentheses

Using max() twice or incorrectly

Use 'chebyshev' as the metric string. To find the maximum value in the distance matrix, use dist_matrix.max(). Here, max is the attribute and () calls it as a method.