KD-Tree for nearest neighbors in SciPy - Mini Project: Build & Apply

KD-Tree for nearest neighbors

📖 Scenario: You work at a delivery company. You have a list of warehouse locations with their coordinates. You want to find the closest warehouse to a customer's location quickly.

🎯 Goal: Build a KD-Tree from warehouse coordinates and find the nearest warehouse to a given customer location.

📋 What You'll Learn

Create a list of warehouse coordinates as tuples

Create a KD-Tree from the warehouse coordinates

Query the KD-Tree to find the nearest warehouse to a customer location

Print the nearest warehouse coordinates

💡 Why This Matters

🌍 Real World

Delivery companies use KD-Trees to quickly find the closest warehouse or delivery center to a customer, saving time and fuel.

💼 Career

Data scientists and engineers use KD-Trees for fast spatial searches in logistics, mapping, and recommendation systems.

1

Create warehouse coordinates list

Create a list called warehouses with these exact coordinate tuples: (2, 3), (5, 4), (9, 6), (4, 7), (8, 1), (7, 2).

SciPy

# Create the list called warehouses with the given coordinates
# Your code here

Need a hint?

Use square brackets to create a list and put tuples inside it.

2

Import KDTree and create tree

Import KDTree from scipy.spatial. Then create a KD-Tree called tree using the warehouses list.

SciPy

warehouses = [(2, 3), (5, 4), (9, 6), (4, 7), (8, 1), (7, 2)]
# Import KDTree and create tree from warehouses
# Your code here

Need a hint?

Use from scipy.spatial import KDTree to import. Then call KDTree(warehouses).

3

Query nearest warehouse

Create a variable called customer_location and set it to the tuple (6, 3). Then use the tree.query() method with customer_location to find the nearest warehouse. Store the result distance in distance and the index in index.

SciPy

from scipy.spatial import KDTree
warehouses = [(2, 3), (5, 4), (9, 6), (4, 7), (8, 1), (7, 2)]
tree = KDTree(warehouses)
# Set customer_location and query the tree for nearest warehouse
# Your code here

Need a hint?

Use tree.query(customer_location) to get distance and index.

4

Print nearest warehouse coordinates

Print the string Nearest warehouse: followed by the coordinates of the nearest warehouse using warehouses[index].

SciPy

from scipy.spatial import KDTree
warehouses = [(2, 3), (5, 4), (9, 6), (4, 7), (8, 1), (7, 2)]
tree = KDTree(warehouses)
customer_location = (6, 3)
distance, index = tree.query(customer_location)
# Print nearest warehouse coordinates
# Your code here

Need a hint?

Use print("Nearest warehouse:", warehouses[index]) to show the result.