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]

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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 }

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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.