np.intersect1d() for intersection in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.intersect1d() do?

It finds the common elements between two arrays and returns them as a sorted array without duplicates.

beginner

How does np.intersect1d() handle duplicates in input arrays?

It removes duplicates and returns only unique common elements in the output.

beginner

Example: What is the output of np.intersect1d([1, 2, 3], [2, 3, 4])?

The output is array([2, 3]) because 2 and 3 are common in both arrays.

intermediate

Can np.intersect1d() be used with arrays of different data types?

Yes, but the arrays should be compatible types so that comparison is possible.

intermediate

What is the difference between np.intersect1d() and Python's set intersection?

np.intersect1d() works on numpy arrays and returns a sorted array without duplicates, while set intersection works on Python sets and returns an unordered set.

What does np.intersect1d() return?

A Common elements sorted without duplicates

B All elements from the first array

C All elements from the second array

D Concatenation of both arrays

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

If a = [1, 2, 2, 3] and b = [2, 2, 4], what is np.intersect1d(a, b)?

A [2, 2]

B [2]

C [1, 2, 3, 4]

D [1, 3, 4]

Duplicates are removed, so only one 2 is returned.

Which of these is true about np.intersect1d()?

A Returns unsorted array

B Returns only elements from first array

C Returns sorted array

D Returns duplicates

The output is always sorted and unique.

Can np.intersect1d() be used with string arrays?

A Only with boolean arrays

B No, only numeric arrays

C Only with integer arrays

D Yes, it works with any comparable data type

It works with any arrays where elements can be compared, including strings.

What happens if there are no common elements between arrays?

A Returns an empty array

B Returns the first array

C Returns the second array

D Raises an error

If no elements match, the result is an empty array.

Explain how np.intersect1d() works and give a simple example.

Think about finding shared items in two lists.

Describe the difference between np.intersect1d() and Python set intersection.

Consider data types and output format.

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