Distance computation (distance.cdist) in SciPy - Mini Project: Build & Apply

Distance Computation with scipy.spatial.distance.cdist

📖 Scenario: Imagine you work in a delivery company. You have a list of warehouses and a list of customer locations. You want to find out how far each customer is from each warehouse to plan deliveries efficiently.

🎯 Goal: You will create two sets of points representing warehouses and customers. Then, you will use scipy.spatial.distance.cdist to calculate the distances between every warehouse and every customer.

📋 What You'll Learn

Create two lists of 2D points for warehouses and customers

Set a distance metric variable

Use scipy.spatial.distance.cdist to compute distances

Print the resulting distance matrix

💡 Why This Matters

🌍 Real World

Delivery companies and logistics planners use distance calculations to optimize routes and reduce delivery times.

💼 Career

Data scientists and analysts often compute distances between points for clustering, recommendation systems, and geographic analysis.

1

Create warehouse and customer location lists

Create a variable called warehouses as a list of points: [1, 2], [3, 4], and [5, 6]. Also create a variable called customers as a list of points: [7, 8] and [9, 10].

SciPy

# Create the warehouses and customers lists with exact points
# Your code here

Need a hint?

Use lists of lists to represent points. Each point has two numbers for x and y coordinates.

2

Set the distance metric

Create a variable called metric and set it to the string 'euclidean' to specify the distance type.

SciPy

warehouses = [[1, 2], [3, 4], [5, 6]]
customers = [[7, 8], [9, 10]]
# Set the distance metric to 'euclidean'
# Your code here

Need a hint?

The metric is a string that tells the function how to measure distance. 'euclidean' means straight-line distance.

3

Compute distances using scipy.spatial.distance.cdist

Import cdist from scipy.spatial.distance. Then create a variable called distances by calling cdist with warehouses, customers, and metric as arguments.

SciPy

warehouses = [[1, 2], [3, 4], [5, 6]]
customers = [[7, 8], [9, 10]]
metric = 'euclidean'
# Import cdist and compute distances
# Your code here

Need a hint?

Use from scipy.spatial.distance import cdist to import the function. Then call it with the two lists and the metric.

4

Print the distance matrix

Print the variable distances to see the matrix of distances between warehouses and customers.

SciPy

from scipy.spatial.distance import cdist

warehouses = [[1, 2], [3, 4], [5, 6]]
customers = [[7, 8], [9, 10]]
metric = 'euclidean'

distances = cdist(warehouses, customers, metric)
# Print the distances matrix
# Your code here

Need a hint?

Use print(distances) to display the matrix. Each row is a warehouse, each column a customer.