np.union1d() for union in NumPy - Mini Project: Build & Apply

Using np.union1d() to Find the Union of Two Arrays

📖 Scenario: Imagine you have two lists of product IDs from two different stores. You want to find all unique product IDs available in either store combined.

🎯 Goal: Build a small program that uses np.union1d() to find the union of two numpy arrays representing product IDs.

📋 What You'll Learn

Create two numpy arrays with exact product ID values

Create a variable to hold the union result

Use np.union1d() to find the union of the two arrays

Print the union array

💡 Why This Matters

🌍 Real World

Finding the combined list of unique items from multiple sources is common in inventory management, customer data merging, and survey analysis.

💼 Career

Data scientists often need to combine datasets and find unique entries efficiently using numpy functions like <code>np.union1d()</code>.

1

Create two numpy arrays with product IDs

Import numpy as np and create two numpy arrays called store1_products and store2_products with these exact values: [101, 102, 103, 104] and [103, 104, 105, 106] respectively.

NumPy

import numpy as np
# Create store1_products and store2_products arrays
# Your code here

Hint

Use np.array() to create numpy arrays with the exact product IDs.

2

Create a variable to hold the union result

Create a variable called all_products that will store the union of store1_products and store2_products.

NumPy

import numpy as np
store1_products = np.array([101, 102, 103, 104])
store2_products = np.array([103, 104, 105, 106])
# Create all_products variable
# Your code here

Hint

Just create the variable all_products and set it to None for now.

3

Use np.union1d() to find the union of the two arrays

Assign to all_products the result of np.union1d(store1_products, store2_products) to find the union of the two arrays.

NumPy

import numpy as np
store1_products = np.array([101, 102, 103, 104])
store2_products = np.array([103, 104, 105, 106])
all_products = None
# Use np.union1d() to find the union
# Your code here

Hint

Use np.union1d() with the two arrays as arguments and assign the result to all_products.

4

Print the union array

Print the variable all_products to display the union of product IDs.

NumPy

import numpy as np
store1_products = np.array([101, 102, 103, 104])
store2_products = np.array([103, 104, 105, 106])
all_products = np.union1d(store1_products, store2_products)
# Print all_products
# Your code here

Hint

Use print(all_products) to show the union array.

Practice

1. What does the function np.union1d() do when applied to two arrays?

easy

A. Finds the common elements between two arrays

B. Combines two arrays and returns unique sorted elements

C. Concatenates two arrays without removing duplicates

D. Sorts a single array in descending order

Solution

1. Step 1: Understand the purpose of np.union1d()

   This function merges two arrays and removes any duplicate values.

2. Step 2: Check the output characteristics

   The result is sorted and contains only unique elements from both arrays.

3. Final Answer:

   Combines two arrays and returns unique sorted elements -> Option B

4. Quick Check:

   Union = unique sorted merge [OK]

Hint: Think of union as merging without duplicates [OK]

Common Mistakes:

• Confusing union with intersection
• Assuming duplicates remain
• Thinking it sorts in descending order

2. Which of the following is the correct syntax to find the union of arrays a and b using numpy?

easy

A. np.union1d[a, b]

B. np.union(a, b)

C. np.union_1d(a, b)

D. np.union1d(a, b)

Solution

1. Step 1: Recall the correct function name

   The correct numpy function is np.union1d() with parentheses.

2. Step 2: Check syntax details

   Arguments are passed inside parentheses, not square brackets, and spelling must be exact.

3. Final Answer:

   np.union1d(a, b) -> Option D

4. Quick Check:

   Correct function call syntax [OK]

Hint: Use parentheses and exact function name np.union1d() [OK]

Common Mistakes:

• Using square brackets instead of parentheses
• Misspelling the function name
• Using a non-existent function np.union

3. What is the output of the following code?

import numpy as np
x = np.array([1, 3, 5])
y = np.array([3, 4, 5, 6])
result = np.union1d(x, y)
print(result)

medium

A. [1 3 4 5 6]

B. [1 3 5]

C. [3 4 5 6]

D. [1 3 5 3 4 5 6]

Solution

1. Step 1: Identify unique elements from both arrays

   Array x has [1, 3, 5], array y has [3, 4, 5, 6]. The union combines all unique values.

2. Step 2: Sort and remove duplicates

   Unique elements combined are [1, 3, 4, 5, 6], sorted in ascending order.

3. Final Answer:

   [1 3 4 5 6] -> Option A

4. Quick Check:

   Union = unique sorted merge [OK]

Hint: Union merges unique sorted elements from both arrays [OK]

Common Mistakes:

• Forgetting to remove duplicates
• Not sorting the result
• Printing only one array

4. The following code throws an error. What is the mistake?

import numpy as np
arr1 = [1, 2, 3]
arr2 = [3, 4, 5]
result = np.union1d(arr1 arr2)
print(result)

medium

A. Missing comma between arr1 and arr2 in function call

B. np.union1d() does not accept lists as input

C. np.union1d() requires arrays to be sorted first

D. print() function is used incorrectly

Solution

1. Step 1: Check function call syntax

   The function call np.union1d(arr1 arr2) is missing a comma between arguments.

2. Step 2: Confirm input types and print usage

   np.union1d accepts lists or arrays, and print() is correctly used.

3. Final Answer:

   Missing comma between arr1 and arr2 in function call -> Option A

4. Quick Check:

   Comma separates arguments [OK]

Hint: Check commas between function arguments [OK]

Common Mistakes:

• Omitting commas between arguments
• Thinking input must be numpy arrays only
• Assuming print() causes error

5. You have two datasets of customer IDs:

dataset1 = np.array([101, 102, 103, 104])
dataset2 = np.array([103, 104, 105, 106])

You want to create a combined list of all unique customer IDs sorted in ascending order. Which code snippet correctly achieves this?

hard

A. combined = np.intersect1d(dataset1, dataset2)

B. combined = np.concatenate((dataset1, dataset2))

C. combined = np.union1d(dataset1, dataset2)

D. combined = np.sort(np.append(dataset1, dataset2))

Solution

1. Step 1: Understand the goal

   We want all unique customer IDs from both datasets, sorted ascending.

2. Step 2: Evaluate each option

   combined = np.union1d(dataset1, dataset2) uses np.union1d which merges and sorts unique elements. combined = np.concatenate((dataset1, dataset2)) concatenates but keeps duplicates. combined = np.intersect1d(dataset1, dataset2) finds only common IDs. combined = np.sort(np.append(dataset1, dataset2)) appends and sorts but does not remove duplicates.

3. Final Answer:

   combined = np.union1d(dataset1, dataset2) -> Option C

4. Quick Check:

   Union merges unique sorted elements [OK]

Hint: Use np.union1d to merge unique sorted values [OK]

Common Mistakes:

• Using concatenate without removing duplicates
• Using intersect1d which finds only common elements
• Appending and sorting without removing duplicates