Why set operations matter in NumPy - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a NumPy array from a Python list.

NumPy

import numpy as np
arr = np.[1]([1, 2, 3, 4])
print(arr)

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

Aset

Blist

Carray

Dmatrix

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'list' instead of 'array' causes an error.

Using 'set' creates a Python set, not a NumPy array.

Use np.array to create a NumPy array from a list.

2fill in blank

medium

Complete the code to find unique elements in a NumPy array.

NumPy

import numpy as np
arr = np.array([1, 2, 2, 3, 4, 4, 5])
unique_elements = np.[1](arr)
print(unique_elements)

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

Aset

Bunique

Cdistinct

Dsort

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'set' returns a Python set, not a NumPy array.

Using 'sort' only sorts but does not remove duplicates.

np.unique returns the sorted unique elements of an array.

3fill in blank

hard

Fix the error in the code to find the intersection of two NumPy arrays.

NumPy

import numpy as np
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([3, 4, 5, 6])
common = np.[1](arr1, arr2)
print(common)

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

Aintersect1d

Bintersect

Cintersection1d

Dintersect_1d

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'intersect' causes an AttributeError.

Using 'intersection1d' is a common misspelling and causes an error.

The correct NumPy function to find common elements is np.intersect1d.

4fill in blank

hard

Fill both blanks to create a dictionary of unique elements and their counts from a NumPy array.

NumPy

import numpy as np
arr = np.array([1, 2, 2, 3, 3, 3])
unique, counts = np.[1](arr, return_counts=True)
result = dict(zip(unique, [2]))
print(result)

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

Aunique

Bcounts

Ccount

Dfrequency

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'count' instead of 'counts' causes a NameError.

Not using return_counts=True in np.unique causes unpacking error.

np.unique with return_counts=True returns unique elements and their counts. Use counts to get counts.

5fill in blank

hard

Fill all three blanks to filter unique elements greater than 2 and create a dictionary with their counts.

NumPy

import numpy as np
arr = np.array([1, 2, 2, 3, 3, 3, 4])
unique, counts = np.unique(arr, return_counts=True)
filtered = unique[unique [1] 2]
filtered_counts = counts[unique [2] 2]
result = dict(zip(filtered, [3]))
print(result)

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

A>

Bcounts

C<

Dfiltered_counts

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' filters wrong elements.

Using 'counts' instead of 'filtered_counts' causes mismatch.

Use '>' to filter elements greater than 2. Use filtered_counts for counts of filtered elements.