Computing Manhattan distance using Python

Overview:

The Manhattan distance or City Block distance is computed by summing the distances in each axis of the given two points.
It is never the shortest distance between the two points in real co-ordinate space as it involves traversing in each direction given by the data point.
The Manhattan distance is greater in value than the Euclidean distance. Euclidean distance is the shortest distance between two points in real co-ordinate space.

Example:

The example uses the first two points of the dataset from the hierarchical clustering using Python. The Euclidean distance between the two points is 0.05040794 whereas the Manhattan distance between the two points is 0.05639999.

# Example Python program that finds the
# Manhattan distance between a set of 2-dimensional
# data points

import scipy.spatial.distance as dist
import numpy

# Body weights and brain weights of crow and parrot
parrot = [0.5, 0.01365]
crow = [0.45, 0.00725]

# Compute the Manhattan distance
manhattan_distance = dist.cityblock(parrot, crow)
print("Manhattan distance between the points:")
print(manhattan_distance)

# Compute the Euclidean distance
euclidean_distance = dist.euclidean(parrot, crow)
print("Euclidean distance between the points:")
print(euclidean_distance)

# Compare the Manhattan distance with the Euclidean distance
result = manhattan_distance < euclidean_distance
print("Is the Manhattan distance lesser than Euclidean distance?")
print(result)

Output:

Manhattan distance between the points:
0.05639999999999999
Euclidean distance between the points:
0.05040793588315236
Is the Manhattan distance lesser than Euclidean distance?
False