Why set operations matter in NumPy - See It in Action

Why Set Operations Matter

📖 Scenario: Imagine you work in a store that sells fruits. You have two lists: one for fruits currently in stock and one for fruits customers want to buy. You want to find out which fruits are both in stock and wanted by customers, which fruits are only in stock, and which fruits are only wanted by customers.

🎯 Goal: You will use numpy set operations to find common fruits, fruits only in stock, and fruits only wanted by customers.

📋 What You'll Learn

Create two numpy arrays with exact fruit names

Create a variable to hold the common fruits

Use numpy set operations to find fruits only in stock and only wanted

Print the results clearly

💡 Why This Matters

🌍 Real World

Stores and businesses often need to compare lists of items, like stock vs customer demand, to make smart decisions.

💼 Career

Data analysts and scientists use set operations to clean data, find overlaps, and identify unique entries in datasets.

1

Create the fruit lists as numpy arrays

Create a numpy array called stock with these fruits: 'apple', 'banana', 'orange', 'kiwi', 'mango'.

NumPy

# Create the numpy array called stock with the exact fruits
# Your code here

Need a hint?

Use np.array([...]) to create the array with the exact fruit names.

2

Create the customer wanted fruits array

Create a numpy array called wanted with these fruits: 'banana', 'kiwi', 'grape', 'mango', 'pineapple'.

NumPy

import numpy as np
stock = np.array(['apple', 'banana', 'orange', 'kiwi', 'mango'])
# Create the numpy array called wanted with the exact fruits
# Your code here

Need a hint?

Use np.array([...]) again to create the wanted array with the exact fruits.

3

Find common and unique fruits using numpy set operations

Create three variables: common for fruits in both stock and wanted using np.intersect1d(stock, wanted), only_in_stock for fruits only in stock using np.setdiff1d(stock, wanted), and only_wanted for fruits only in wanted using np.setdiff1d(wanted, stock).

NumPy

import numpy as np
stock = np.array(['apple', 'banana', 'orange', 'kiwi', 'mango'])
wanted = np.array(['banana', 'kiwi', 'grape', 'mango', 'pineapple'])
# Create common, only_in_stock, and only_wanted using numpy set operations
# Your code here

Need a hint?

Use np.intersect1d to find common fruits and np.setdiff1d to find fruits only in one array.

4

Print the results

Print the variables common, only_in_stock, and only_wanted each on a separate line with clear labels.

NumPy

import numpy as np
stock = np.array(['apple', 'banana', 'orange', 'kiwi', 'mango'])
wanted = np.array(['banana', 'kiwi', 'grape', 'mango', 'pineapple'])
common = np.intersect1d(stock, wanted)
only_in_stock = np.setdiff1d(stock, wanted)
only_wanted = np.setdiff1d(wanted, stock)
# Print the results with labels
# Your code here

Need a hint?

Use print("Label:", variable) to show each result clearly.