Set operations on structured data in NumPy - Mini Project: Build & Apply

Set operations on structured data

📖 Scenario: You work in a store that tracks products by their name and price. You want to find which products are unique to each store's list and which products are common.

🎯 Goal: Build a program that uses numpy structured arrays to find the intersection, union, and difference of two product lists.

📋 What You'll Learn

Create two numpy structured arrays with product data

Define a configuration variable for the data type

Use numpy set operations on structured arrays

Print the results clearly

💡 Why This Matters

🌍 Real World

Stores and businesses often compare product lists from different branches or suppliers to manage inventory and pricing.

💼 Career

Data scientists use structured arrays and set operations to clean, compare, and analyze datasets with multiple fields efficiently.

1

DATA SETUP: Create two numpy structured arrays

Import numpy as np. Create a numpy structured array called store1 with these exact entries: ('apple', 0.50), ('banana', 0.30), ('orange', 0.80). Create another numpy structured array called store2 with these exact entries: ('banana', 0.30), ('kiwi', 1.00), ('apple', 0.50). Use a data type with fields 'name' as 'U10' and 'price' as 'f4'.

NumPy

# Import numpy as np
# Create store1 and store2 structured arrays with given entries and dtype

Need a hint?

Use np.array with a list of tuples and specify dtype for structured arrays.

2

CONFIGURATION: Define the data type variable

Create a variable called dtype and set it to a list of tuples with fields ('name', 'U10') and ('price', 'f4').

NumPy

import numpy as np

# Define dtype variable for structured arrays
# Your code here

Need a hint?

Set dtype exactly as a list of tuples with field names and types.

3

CORE LOGIC: Use numpy set operations on structured arrays

Use np.intersect1d with store1 and store2 to find common products and save to common. Use np.union1d to find all unique products and save to all_products. Use np.setdiff1d with store1 and store2 to find products only in store1 and save to only_store1.

NumPy

import numpy as np

dtype = [('name', 'U10'), ('price', 'f4')]
store1 = np.array([('apple', 0.50), ('banana', 0.30), ('orange', 0.80)], dtype=dtype)
store2 = np.array([('banana', 0.30), ('kiwi', 1.00), ('apple', 0.50)], dtype=dtype)

# Find common products
# Find all unique products
# Find products only in store1
# Your code here

Need a hint?

Use np.intersect1d, np.union1d, and np.setdiff1d with the two arrays.

4

OUTPUT: Print the results clearly

Print the variables common, all_products, and only_store1 each on a separate line with labels: "Common products:", "All products:", and "Only in store1:" respectively.

NumPy

import numpy as np

dtype = [('name', 'U10'), ('price', 'f4')]
store1 = np.array([('apple', 0.50), ('banana', 0.30), ('orange', 0.80)], dtype=dtype)
store2 = np.array([('banana', 0.30), ('kiwi', 1.00), ('apple', 0.50)], dtype=dtype)

common = np.intersect1d(store1, store2)
all_products = np.union1d(store1, store2)
only_store1 = np.setdiff1d(store1, store2)

# Print common, all_products, and only_store1 with labels
# Your code here

Need a hint?

Use print with labels and variables on separate lines.