np.union1d() for union in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to find the union of two arrays using numpy.

NumPy

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

Drag options to blanks, or click blank then click option'

Aintersect1d

Bappend

Cunion1d

Dconcatenate

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.intersect1d() which finds common elements instead of union.

Using np.concatenate() which just joins arrays without removing duplicates.

The np.union1d() function returns the sorted unique values that are in either of the two input arrays, effectively giving their union.

2fill in blank

medium

Complete the code to find the union of two numpy arrays and store it in 'union_result'.

NumPy

import numpy as np
array_a = np.array([10, 20, 30])
array_b = np.array([20, 40, 50])
union_result = np.[1](array_a, array_b)
print(union_result)

Drag options to blanks, or click blank then click option'

Aintersect1d

Bunion1d

Csetdiff1d

Dunique

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.intersect1d() which returns only common elements.

Using np.unique() on one array only.

np.union1d() returns the sorted unique elements from both arrays combined, which is the union.

3fill in blank

hard

Fix the error in the code to correctly compute the union of two numpy arrays.

NumPy

import numpy as np
x = np.array([1, 3, 5])
y = np.array([2, 3, 4])
union = np.[1](x, y)
print(union)

Drag options to blanks, or click blank then click option'

Aunion1d

Bunion

Cunion_1d

Dunion1D

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect function names like 'union' or 'union_1d'.

Capitalizing letters incorrectly in the function name.

The correct function name is union1d all lowercase. Other options are invalid function names and cause errors.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length only if the length is greater than 3.

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = {word: [1] for word in words if [2]

Drag options to blanks, or click blank then click option'

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Comparing the word string directly to a number.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if length is greater than 2.

NumPy

words = ['go', 'run', 'jump', 'fly']
result = [1] = { [2]: [3] for w in words if len(w) > 2 }

Drag options to blanks, or click blank then click option'

Aword_dict

Bw.upper()

Clen(w)

Dwords

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original list name as the dictionary variable.

Using the word itself instead of uppercase for keys.

Using the word instead of length for values.

The dictionary named word_dict maps each uppercase word w.upper() to its length len(w), filtering words longer than 2 characters.

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