np.intersect1d() for intersection in NumPy

Introduction

We use np.intersect1d() to find common items between two lists or arrays. It helps us see what values appear in both sets.

Finding common customers between two sales lists.

Checking shared tags between two groups of articles.

Comparing two exam answer sheets to find matching answers.

Identifying overlapping products in two store inventories.

Syntax

NumPy

np.intersect1d(array1, array2, assume_unique=False, return_indices=False)

array1 and array2 are the two arrays to compare.

assume_unique=True speeds up the function if you know arrays have unique elements.

Examples

This finds numbers present in both arrays: 3 and 4.

NumPy

import numpy as np

arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([3, 4, 5, 6])
common = np.intersect1d(arr1, arr2)
print(common)

Works with strings too, showing common fruits.

NumPy

import numpy as np

arr1 = np.array(['apple', 'banana', 'cherry'])
arr2 = np.array(['banana', 'dragonfruit', 'apple'])
common = np.intersect1d(arr1, arr2)
print(common)

Sample Program

This program finds which students are in both classes by comparing their ID numbers.

NumPy

import numpy as np

# Two lists of student IDs from two classes
class_a = np.array([101, 102, 103, 104, 105])
class_b = np.array([104, 105, 106, 107])

# Find students in both classes
common_students = np.intersect1d(class_a, class_b)

print("Students in both classes:", common_students)

Important Notes

The result is always sorted in ascending order.

If you want to know the positions of common elements in the original arrays, use return_indices=True.

Summary

np.intersect1d() finds common elements between two arrays.

It works with numbers, strings, or any comparable data.

The output is a sorted array of shared values.

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