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.

Practice

1. What is the main purpose of using set operations in NumPy arrays?

easy

A. To multiply elements of arrays

B. To sort arrays in ascending order

C. To reshape arrays into different dimensions

D. To find common or unique elements between arrays

Solution

1. Step 1: Understand set operations

   Set operations are used to compare arrays to find common or unique elements.

2. Step 2: Identify the main purpose

   Sorting, multiplication, and reshaping are different array operations, not set operations.

3. Final Answer:

   To find common or unique elements between arrays -> Option D

4. Quick Check:

   Set operations = find common/unique elements [OK]

Hint: Set operations = compare arrays for common or unique items [OK]

Common Mistakes:

• Confusing set operations with sorting or reshaping
• Thinking set operations multiply elements
• Assuming set operations change array shape

2. Which NumPy function is used to find the intersection of two arrays?

easy

A. np.intersect1d()

B. np.concatenate()

C. np.setdiff1d()

D. np.union1d()

Solution

1. Step 1: Recall function names for set operations

   np.intersect1d() finds common elements between arrays.

2. Step 2: Differentiate from other functions

   np.union1d() finds all unique elements combined, np.setdiff1d() finds differences, np.concatenate() joins arrays without set logic.

3. Final Answer:

   np.intersect1d() -> Option A

4. Quick Check:

   Intersection = np.intersect1d() [OK]

Hint: Intersection means common elements, use np.intersect1d() [OK]

Common Mistakes:

• Using np.union1d() for intersection
• Confusing set difference with intersection
• Using np.concatenate() which just joins arrays

3. What is the output of the following code?

import numpy as np
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([3, 4, 5, 6])
result = np.setdiff1d(arr1, arr2)
print(result)

medium

A. [1 2]

B. [3 4]

C. [5 6]

D. [1 2 3 4 5 6]

Solution

1. Step 1: Understand np.setdiff1d()

   This function returns elements in the first array not in the second.

2. Step 2: Apply to given arrays

   Elements in arr1 but not in arr2 are 1 and 2.

3. Final Answer:

   [1 2] -> Option A

4. Quick Check:

   Set difference arr1 - arr2 = [1 2] [OK]

Hint: Set difference = items in first array not in second [OK]

Common Mistakes:

• Confusing set difference with intersection
• Expecting union instead of difference
• Misreading which array is first

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

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = [2, 3, 4]
result = np.intersect1d(arr1, arr2)
print(result)

medium

A. arr2 is not a NumPy array

B. There is no error; code runs fine

C. np.intersect1d() cannot handle integers

D. np.intersect1d() requires both inputs to be lists

Solution

1. Step 1: Check input types for np.intersect1d()

   np.intersect1d() accepts array-like inputs, including lists.

2. Step 2: Verify code behavior

   arr2 is a list, which is valid input; code runs without error and outputs common elements.

3. Final Answer:

   There is no error; code runs fine -> Option B

4. Quick Check:

   np.intersect1d() accepts lists and arrays [OK]

Hint: np.intersect1d() accepts lists or arrays as input [OK]

Common Mistakes:

• Assuming inputs must be NumPy arrays
• Thinking np.intersect1d() only works with arrays
• Expecting error due to mixed input types

5. You have two arrays:

arr1 = np.array([1, 2, 2, 3, 4])
arr2 = np.array([2, 3, 5])

How can you find all unique elements that appear in either array but not in both?

hard

A. Use np.intersect1d(arr1, arr2)

B. Use np.union1d(arr1, arr2)

C. Use np.setxor1d(arr1, arr2)

D. Use np.setdiff1d(arr1, arr2)

Solution

1. Step 1: Understand the problem

   We want elements unique to each array, not shared by both.

2. Step 2: Identify correct function

   np.setxor1d() returns elements in either array but not in both (exclusive or).

3. Final Answer:

   Use np.setxor1d(arr1, arr2) -> Option C

4. Quick Check:

   Unique elements in either array = np.setxor1d() [OK]

Hint: Exclusive elements = np.setxor1d() finds unique non-shared items [OK]

Common Mistakes:

• Using union instead of exclusive or
• Using intersection which finds common elements
• Using set difference which is one-sided