np.intersect1d() for intersection in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to find the common elements between two arrays using numpy.

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

Bunion1d

Csetdiff1d

Dunique

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.union1d() which returns all unique elements from both arrays.

Using np.setdiff1d() which returns elements in one array but not the other.

The np.intersect1d() function returns the sorted, unique values that are in both input arrays.

2fill in blank

medium

Complete the code to find the intersection of two lists converted to numpy arrays.

NumPy

import numpy as np
list1 = [10, 20, 30, 40]
list2 = [30, 40, 50, 60]
arr1 = np.array(list1)
arr2 = np.array(list2)
result = np.intersect1d(arr1, [1])
print(result)

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

Alist2

Barr2

Cnp.array(list2)

Dnp.array(list1)

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list instead of a numpy array as the second argument.

Passing the first array again instead of the second.

The intersection function requires two numpy arrays as input. Here, arr2 is the second numpy array.

3fill in blank

hard

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

NumPy

import numpy as np
arr1 = np.array([7, 8, 9])
arr2 = np.array([8, 9, 10])
common = np.intersect1d(arr1, arr2[1])
print(common)

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

A, assume_unique=True

B, axis=0

C)

D, return_indices=True

Attempts:

3 left

💡 Hint

Common Mistakes

Adding unnecessary parameters that cause errors.

Forgetting to close the function call with a parenthesis.

The function call was missing the closing parenthesis. Adding ) fixes the syntax error.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths only if length is greater than 3.

NumPy

words = ['data', 'science', 'ai', 'ml']
lengths = {word: [1] for word in words if [2]
print(lengths)

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

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.

Checking if the word string is greater than 3 instead of its length.

The dictionary comprehension uses len(word) as the value and filters words where length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values only for items with values greater than 0.

NumPy

data = {'a': 1, 'b': -1, 'c': 3}
result = { [1]: [2] for k, v in data.items() if v [3] 0 }
print(result)

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original keys without uppercase.

Using keys as values or vice versa.

Using wrong comparison operators.

The dictionary comprehension uses uppercase keys with k.upper(), values v, and filters items where value is greater than 0.

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