np.intersect1d() for intersection in NumPy - Mini Project: Build & Apply

Find Common Elements Using np.intersect1d()

📖 Scenario: Imagine you have two lists of favorite fruits from two friends. You want to find which fruits both friends like.

🎯 Goal: Build a small program that finds the common fruits between two lists using np.intersect1d().

📋 What You'll Learn

Create two numpy arrays with exact fruit names

Use a variable to store the common fruits

Use np.intersect1d() to find the intersection

Print the common fruits array

💡 Why This Matters

🌍 Real World

Finding common items between lists is useful in surveys, recommendations, and data comparison tasks.

💼 Career

Data scientists often need to compare datasets to find overlaps or shared features.

1

Create two numpy arrays of fruits

Import numpy as np and create two numpy arrays called fruits1 and fruits2 with these exact values: ['apple', 'banana', 'cherry', 'date'] and ['banana', 'date', 'fig', 'grape'] respectively.

NumPy

import numpy as np
# Create numpy arrays fruits1 and fruits2 with the exact fruit names
# Your code here

Hint

Use np.array() to create numpy arrays from lists.

2

Create a variable to store common fruits

Create a variable called common_fruits to hold the common fruits between fruits1 and fruits2. Use np.intersect1d(fruits1, fruits2) to find the intersection.

NumPy

import numpy as np
fruits1 = np.array(['apple', 'banana', 'cherry', 'date'])
fruits2 = np.array(['banana', 'date', 'fig', 'grape'])
# Create common_fruits using np.intersect1d with fruits1 and fruits2
# Your code here

Hint

Use np.intersect1d(array1, array2) to get common elements.

3

Print the common fruits

Write a print() statement to display the common_fruits array.

NumPy

import numpy as np
fruits1 = np.array(['apple', 'banana', 'cherry', 'date'])
fruits2 = np.array(['banana', 'date', 'fig', 'grape'])
common_fruits = np.intersect1d(fruits1, fruits2)
# Print the common_fruits array
# Your code here

Hint

Use print(common_fruits) to show the result.

4

Final check: Display the common fruits clearly

Print a friendly message followed by the common_fruits array to clearly show the fruits both friends like.

NumPy

import numpy as np
fruits1 = np.array(['apple', 'banana', 'cherry', 'date'])
fruits2 = np.array(['banana', 'date', 'fig', 'grape'])
common_fruits = np.intersect1d(fruits1, fruits2)
# Print a message and the common_fruits array
# Your code here

Hint

Use print('Fruits both friends like:', common_fruits) to show a clear message.

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