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.

Practice

1. What does the function np.intersect1d() do in NumPy?

easy

A. Finds common elements between two arrays

B. Combines two arrays into one

C. Sorts an array in descending order

D. Removes duplicates from an array

Solution

1. Step 1: Understand the function purpose

   np.intersect1d() is designed to find elements that appear in both input arrays.

2. Step 2: Compare with other options

   Options A, B, and D describe different functions: sorting descending, combining arrays, and removing duplicates, which are not the purpose of np.intersect1d().

3. Final Answer:

   Finds common elements between two arrays -> Option A

4. Quick Check:

   Intersection = common elements [OK]

Hint: Think 'intersection' means shared items [OK]

Common Mistakes:

• Confusing intersection with concatenation
• Thinking it sorts descending
• Mixing with duplicate removal

2. Which of the following is the correct syntax to find the intersection of arrays a and b using NumPy?

easy

A. np.intersect(a, b)

B. np.intersection(a, b)

C. np.intersect1d(a, b)

D. np.intersect1d([a, b])

Solution

1. Step 1: Recall the correct function name and parameters

   The correct function is np.intersect1d() and it takes two arrays as separate arguments.

2. Step 2: Check each option

   np.intersect1d(a, b) uses the correct function and syntax. Options B and C use incorrect function names. np.intersect1d([a, b]) incorrectly passes a list of arrays instead of two separate arguments.

3. Final Answer:

   np.intersect1d(a, b) -> Option C

4. Quick Check:

   Correct function and parameters [OK]

Hint: Use exact function name and separate arrays as arguments [OK]

Common Mistakes:

• Using wrong function names
• Passing arrays inside a list
• Missing one argument

3. What is the output of the following code?

import numpy as np
x = np.array([3, 1, 4, 1, 5])
y = np.array([5, 9, 2, 6, 5])
print(np.intersect1d(x, y))

medium

A. [1 5]

B. [3 5]

C. [1 5 9]

D. [5]

Solution

1. Step 1: Identify unique elements in both arrays

   Array x has elements {1, 3, 4, 5} (1 appears twice but counted once). Array y has elements {2, 5, 6, 9} (5 appears twice but counted once).

2. Step 2: Find common elements

   The only common element between x and y is 5.

3. Final Answer:

   [5] -> Option D

4. Quick Check:

   Common elements = [5] [OK]

Hint: Look for numbers appearing in both arrays [OK]

Common Mistakes:

• Including duplicates
• Adding elements not in both arrays
• Confusing order of output

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

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

medium

A. Missing comma between arguments in np.intersect1d()

B. Arrays must be NumPy arrays, not lists

C. np.intersect1d() does not accept two arguments

D. print() function is used incorrectly

Solution

1. Step 1: Check the function call syntax

   The call np.intersect1d(arr1 arr2) is missing a comma between the two arguments.

2. Step 2: Verify other parts

   Passing lists is allowed because NumPy converts them internally. The function accepts two arguments. The print statement is correct.

3. Final Answer:

   Missing comma between arguments in np.intersect1d() -> Option A

4. Quick Check:

   Arguments must be separated by commas [OK]

Hint: Check commas between function arguments [OK]

Common Mistakes:

• Forgetting commas
• Thinking lists are invalid inputs
• Misreading error source

5. You have two arrays representing product IDs sold in two stores:

store1 = np.array([101, 102, 103, 104, 105])
store2 = np.array([104, 105, 106, 107])

How can you find the sorted list of product IDs sold in both stores using np.intersect1d()?

hard

A. np.union1d(store1, store2)

B. np.intersect1d(store1, store2)

C. np.setdiff1d(store1, store2)

D. np.concatenate((store1, store2))

Solution

1. Step 1: Understand the problem

   We want product IDs common to both stores, which means intersection.

2. Step 2: Choose the correct function

   np.intersect1d(store1, store2) returns sorted common elements. Other options return union, difference, or concatenation, which are not correct here.

3. Final Answer:

   np.intersect1d(store1, store2) -> Option B

4. Quick Check:

   Intersection = common products [OK]

Hint: Use intersect1d for common elements between arrays [OK]

Common Mistakes:

• Using union instead of intersection
• Using difference or concatenation
• Not sorting output