np.union1d() for union in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.union1d() with integer arrays

What is the output of this code snippet using np.union1d()?

NumPy

import numpy as np
arr1 = np.array([1, 3, 5, 7])
arr2 = np.array([3, 4, 5, 6])
result = np.union1d(arr1, arr2)
print(result)

A [1 3 4 5 6 7]

B [1 3 5 7 3 4 5 6]

C [3 4 5 6]

D [1 3 5 7]

💡 Hint

np.union1d() returns sorted unique elements from both arrays combined.

The function combines both arrays, removes duplicates, and sorts the result. So all unique elements from both arrays appear once in sorted order.

❓ data_output

intermediate

Number of elements in union of two arrays

Given two arrays, how many elements are in the union computed by np.union1d()?

NumPy

import numpy as np
arr1 = np.array([10, 20, 30, 40])
arr2 = np.array([30, 40, 50, 60, 70])
union = np.union1d(arr1, arr2)
print(len(union))

A 8

B 5

C 7

D 6

💡 Hint

Count unique elements after merging both arrays.

The union contains unique elements: 10, 20, 30, 40, 50, 60, 70. That's 7 elements.

🔧 Debug

advanced

Identify the error in using np.union1d() with lists

What error will this code raise?

NumPy

import numpy as np
list1 = [1, 2, 3]
list2 = [3, 4, 5]
result = np.union1d(list1, list2)
print(result)

A No error, prints [1 2 3 4 5]

B TypeError: unhashable type: 'list'

C ValueError: operands could not be broadcast together

D [1 2 3 4 5]

💡 Hint

np.union1d() accepts array-like inputs, including lists.

np.union1d() converts lists to arrays internally and returns the sorted unique union without error.

🧠 Conceptual

advanced

Behavior of np.union1d() with string arrays

What is the output of this code?

NumPy

import numpy as np
arr1 = np.array(['apple', 'banana', 'cherry'])
arr2 = np.array(['banana', 'date', 'fig'])
result = np.union1d(arr1, arr2)
print(result)

A ['apple' 'banana' 'banana' 'cherry' 'date' 'fig']

B ['apple' 'banana' 'cherry' 'date' 'fig']

C ['banana' 'date' 'fig']

D ['apple' 'cherry' 'date' 'fig']

💡 Hint

np.union1d() works with strings and sorts them alphabetically.

The function returns all unique strings from both arrays sorted alphabetically.

🚀 Application

expert

Using np.union1d() to find unique categories from two datasets

You have two datasets with category labels as arrays. Which code snippet correctly finds all unique categories combined?

A unique_categories = np.setdiff1d(dataset1['category'], dataset2['category'])

B unique_categories = np.concatenate((dataset1['category'], dataset2['category']))

C unique_categories = np.intersect1d(dataset1['category'], dataset2['category'])

D unique_categories = np.union1d(dataset1['category'], dataset2['category'])

💡 Hint

np.union1d() returns all unique elements from both arrays combined.

Option D correctly uses np.union1d() to find all unique categories from both datasets. Others either concatenate without uniqueness or find intersection/difference.