np.setdiff1d() for difference in NumPy - Mini Project: Build & Apply

Find Unique Items Using np.setdiff1d()

📖 Scenario: Imagine you have two lists of products from two different stores. You want to find which products are available in the first store but not in the second store.

🎯 Goal: Build a small program that uses np.setdiff1d() to find items unique to the first list.

📋 What You'll Learn

Create two numpy arrays with exact product names

Create a variable to hold the difference using np.setdiff1d()

Print the resulting array of unique items

💡 Why This Matters

🌍 Real World

Finding unique items between two lists is common in inventory management, customer lists, or comparing datasets.

💼 Career

Data analysts and scientists often need to compare datasets to find differences or unique entries.

1

Create the product lists

Create a numpy array called store1_products with these exact items: 'apple', 'banana', 'cherry', 'date', 'fig'.

NumPy

# Create the numpy array store1_products with the given items
# Your code here

Hint

Use np.array([...]) to create the array with the exact product names.

2

Create the second product list

Create a numpy array called store2_products with these exact items: 'banana', 'date', 'elderberry', 'grape'.

NumPy

import numpy as np
store1_products = np.array(['apple', 'banana', 'cherry', 'date', 'fig'])
# Create the numpy array store2_products with the given items
# Your code here

Hint

Use np.array([...]) again to create the second list with the exact product names.

3

Find products unique to the first store

Create a variable called unique_to_store1 that uses np.setdiff1d() to find items in store1_products that are not in store2_products.

NumPy

import numpy as np
store1_products = np.array(['apple', 'banana', 'cherry', 'date', 'fig'])
store2_products = np.array(['banana', 'date', 'elderberry', 'grape'])
# Use np.setdiff1d() to find unique items in store1_products
# Your code here

Hint

Use np.setdiff1d(array1, array2) to find items in array1 not in array2.

4

Print the unique products

Print the variable unique_to_store1 to display the products unique to the first store.

NumPy

import numpy as np
store1_products = np.array(['apple', 'banana', 'cherry', 'date', 'fig'])
store2_products = np.array(['banana', 'date', 'elderberry', 'grape'])
unique_to_store1 = np.setdiff1d(store1_products, store2_products)
# Print the unique_to_store1 array
# Your code here

Hint

Use print(unique_to_store1) to show the result.

Practice

1.

What does the function np.setdiff1d(arr1, arr2) do?

easy

A. Finds elements in arr1 that are not in arr2

B. Finds elements common to both arr1 and arr2

C. Combines arr1 and arr2 into one array

D. Sorts arr1 in descending order

Solution

1. Step 1: Understand the function purpose

   np.setdiff1d(arr1, arr2) returns elements unique to arr1 that are not found in arr2.

2. Step 2: Compare with options

   Finds elements in arr1 that are not in arr2 matches this behavior exactly, while others describe different operations.

3. Final Answer:

   Finds elements in arr1 that are not in arr2 -> Option A

4. Quick Check:

   Unique elements in arr1 = D [OK]

Hint: Remember: setdiff1d finds what is only in first array [OK]

Common Mistakes:

• Confusing setdiff1d with intersection
• Thinking it merges arrays
• Assuming it sorts in descending order

2.

Which of the following is the correct syntax to find elements in a not in b using np.setdiff1d()?

a = np.array([1, 2, 3])
b = np.array([2, 3, 4])

easy

A. np.setdiff1d(a + b)

B. np.setdiff1d(b, a)

C. np.setdiff1d(a)

D. np.setdiff1d(a, b)

Solution

1. Step 1: Identify correct parameter order

   The first argument is the array to find unique elements from; the second is the array to exclude elements from.

2. Step 2: Match with options

   To find elements in a not in b, use np.setdiff1d(a, b), which is np.setdiff1d(a, b).

3. Final Answer:

   np.setdiff1d(a, b) -> Option D

4. Quick Check:

   First array minus second array = A [OK]

Hint: First argument is the array to subtract from [OK]

Common Mistakes:

• Swapping the order of arrays
• Passing only one array
• Trying to add arrays inside setdiff1d

3.

What is the output of the following code?

import numpy as np
x = np.array([5, 3, 9, 1])
y = np.array([3, 7, 1])
result = np.setdiff1d(x, y)
print(result)

medium

A. [1 3 5 7 9]

B. [5 9]

C. [3 7]

D. [1 5 9]

Solution

1. Step 1: Identify elements in x not in y

   Elements in x are [5, 3, 9, 1]. Elements in y are [3, 7, 1]. The elements in x but not in y are 5 and 9.

2. Step 2: Check output format

   np.setdiff1d returns a sorted array without duplicates, so output is [5 9].

3. Final Answer:

   [5 9] -> Option B

4. Quick Check:

   Unique elements in x = A [OK]

Hint: Output is sorted unique elements from first array only [OK]

Common Mistakes:

• Including elements from second array
• Not sorting output
• Confusing with intersection output

4.

Find the error in this code snippet:

import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([2, 3, 4])
result = np.setdiff1d(arr1 arr2)
print(result)

medium

A. Missing comma between arguments in setdiff1d

B. Arrays must be lists, not numpy arrays

C. Function name is misspelled

D. print statement syntax is incorrect

Solution

1. Step 1: Check function call syntax

   The call np.setdiff1d(arr1 arr2) is missing a comma between arr1 and arr2.

2. Step 2: Verify other parts

   Arrays are correctly numpy arrays, function name is correct, and print syntax is valid in Python 3.

3. Final Answer:

   Missing comma between arguments in setdiff1d -> Option A

4. Quick Check:

   Comma separates arguments = C [OK]

Hint: Check commas between function arguments carefully [OK]

Common Mistakes:

• Forgetting commas between parameters
• Thinking numpy arrays are invalid inputs
• Assuming print needs parentheses in Python 3

5.

You have two arrays representing IDs of users who visited two different websites:

site1 = np.array([101, 102, 103, 104, 105])
site2 = np.array([103, 104, 106])

How can you find the IDs of users who visited only the first site?

hard

A. np.setdiff1d(site2, site1)

B. np.intersect1d(site1, site2)

C. np.setdiff1d(site1, site2)

D. np.union1d(site1, site2)

Solution

1. Step 1: Understand the problem

   We want users who visited site1 but not site2.

2. Step 2: Apply np.setdiff1d

   Using np.setdiff1d(site1, site2) returns elements in site1 not in site2.

3. Step 3: Check other options

   np.setdiff1d(site2, site1) reverses the order, giving users only in site2. Options C and D find common or combined users, not unique to site1.

4. Final Answer:

   np.setdiff1d(site1, site2) -> Option C

5. Quick Check:

   Unique to first array = B [OK]

Hint: Use setdiff1d with first array as main set [OK]

Common Mistakes:

• Swapping arrays order
• Using intersection instead of difference
• Using union which combines all