np.intersect1d() for intersection in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.intersect1d() with simple arrays

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.intersect1d(arr1, arr2)
print(result)

NumPy

import numpy as np
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([3, 4, 5, 6])
result = np.intersect1d(arr1, arr2)
print(result)

A [3 4]

B [1 2]

C [5 6]

D []

💡 Hint

np.intersect1d() returns sorted unique values common to both arrays.

The common elements between arr1 and arr2 are 3 and 4. np.intersect1d returns them sorted as [3 4].

❓ data_output

intermediate

Number of elements in intersection

Given two arrays, what is the number of elements in their intersection?

import numpy as np
arr1 = np.array([10, 20, 30, 40, 50])
arr2 = np.array([30, 40, 50, 60, 70])
intersect = np.intersect1d(arr1, arr2)
print(len(intersect))

NumPy

import numpy as np
arr1 = np.array([10, 20, 30, 40, 50])
arr2 = np.array([30, 40, 50, 60, 70])
intersect = np.intersect1d(arr1, arr2)
print(len(intersect))

A 3

B 2

C 5

D 0

💡 Hint

Count how many unique values appear in both arrays.

The common elements are 30, 40, and 50, so the length of the intersection is 3.

🔧 Debug

advanced

Error type from incorrect np.intersect1d() usage

What error does the following code raise?

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

NumPy

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

A No error, prints []

B ValueError

C IndexError

D TypeError

💡 Hint

Check if the second argument is iterable as expected by np.intersect1d.

np.intersect1d expects both inputs to be array-like. Passing an integer causes a TypeError because it is not iterable.

🚀 Application

advanced

Using np.intersect1d() to find common words

You have two lists of words from two documents:

doc1 = ['apple', 'banana', 'cherry', 'date']
doc2 = ['banana', 'date', 'fig', 'grape']
common_words = np.intersect1d(doc1, doc2)
print(common_words)

What will be printed?

NumPy

doc1 = ['apple', 'banana', 'cherry', 'date']
doc2 = ['banana', 'date', 'fig', 'grape']
common_words = np.intersect1d(doc1, doc2)
print(common_words)

A ['fig' 'grape']

B ['apple' 'cherry']

C ['banana' 'date']

D []

💡 Hint

np.intersect1d returns sorted unique common elements.

The common words between doc1 and doc2 are 'banana' and 'date'. They are printed as a numpy array of strings.

🧠 Conceptual

expert

Behavior of np.intersect1d() with duplicates and sorting

Consider these arrays:

arr1 = np.array([5, 3, 3, 1, 2, 2])
arr2 = np.array([2, 3, 3, 4, 5])
result = np.intersect1d(arr1, arr2)
print(result)

Which statement about the output is true?

NumPy

arr1 = np.array([5, 3, 3, 1, 2, 2])
arr2 = np.array([2, 3, 3, 4, 5])
result = np.intersect1d(arr1, arr2)
print(result)

A The output is [3 3 2 2 5], duplicates preserved in original order

B The output is [2 3 5], duplicates removed and sorted ascending

C The output is [5 3 2], duplicates removed but order is from arr1

D The output is [1 2 3 4 5], all unique elements combined

💡 Hint

np.intersect1d returns sorted unique values common to both arrays.

np.intersect1d removes duplicates and sorts the result ascending. The common unique elements are 2, 3, and 5.