np.in1d() for membership testing in NumPy - Mini Project: Build & Apply

Using np.in1d() for Membership Testing

📖 Scenario: You work in a store that tracks product IDs. You want to check which products from a new shipment are already in your current inventory.

🎯 Goal: Build a small program that uses np.in1d() to find which product IDs from the shipment are already in the inventory.

📋 What You'll Learn

Create two numpy arrays: one for current inventory product IDs and one for new shipment product IDs.

Create a variable to store the membership test result using np.in1d().

Print the boolean array showing which shipment products are in the inventory.

💡 Why This Matters

🌍 Real World

Stores and warehouses often need to check if new products are already in stock to avoid duplicates or manage inventory.

💼 Career

Data analysts and inventory managers use membership testing to clean and filter product data efficiently.

1

Create numpy arrays for inventory and shipment

Import numpy as np. Create a numpy array called inventory with these product IDs: 101, 102, 103, 104. Create another numpy array called shipment with these product IDs: 102, 105, 101, 107.

NumPy

import numpy as np
# Create the inventory and shipment arrays
# Your code here

Hint

Use np.array() to create arrays with the exact product IDs given.

2

Create a variable for membership test

Create a variable called in_inventory that uses np.in1d() to check which product IDs in shipment are also in inventory.

NumPy

import numpy as np
inventory = np.array([101, 102, 103, 104])
shipment = np.array([102, 105, 101, 107])
# Use np.in1d() to check membership
# Your code here

Hint

Use np.in1d(shipment, inventory) to get a boolean array.

3

Print the membership result

Print the variable in_inventory to show which shipment products are in the inventory.

NumPy

import numpy as np
inventory = np.array([101, 102, 103, 104])
shipment = np.array([102, 105, 101, 107])
in_inventory = np.in1d(shipment, inventory)
# Print the in_inventory array
# Your code here

Hint

Use print(in_inventory) to display the boolean array.

4

Filter shipment products that are in inventory

Create a variable called products_in_inventory that uses shipment[in_inventory] to get the product IDs from shipment that are in inventory. Then print products_in_inventory.

NumPy

import numpy as np
inventory = np.array([101, 102, 103, 104])
shipment = np.array([102, 105, 101, 107])
in_inventory = np.in1d(shipment, inventory)
print(in_inventory)
# Filter shipment products that are in inventory
# Your code here

Hint

Use boolean indexing: shipment[in_inventory] to get matching products.

Practice

1. What does the np.in1d() function do in NumPy?

easy

A. Finds the unique elements in an array.

B. Sorts the elements of an array in ascending order.

C. Calculates the sum of elements in an array.

D. Checks if elements of one array are present in another array and returns a boolean array.

Solution

1. Step 1: Understand the purpose of np.in1d()

   The function checks membership of each element in the first array against the second array.

2. Step 2: Identify the output type

   It returns a boolean array indicating True where elements are found and False otherwise.

3. Final Answer:

   Checks if elements of one array are present in another array and returns a boolean array. -> Option D

4. Quick Check:

   Membership test = Checks if elements of one array are present in another array and returns a boolean array. [OK]

Hint: Remember: np.in1d returns booleans for membership [OK]

Common Mistakes:

• Confusing np.in1d() with sorting or summing functions
• Expecting np.in1d() to return the matching elements instead of booleans
• Thinking np.in1d() modifies the original arrays

2. Which of the following is the correct syntax to check if elements of array a are in array b using np.in1d()?

easy

A. np.in1d(b, a)

B. np.in1d(a, b)

C. np.in1d(a == b)

D. np.in1d(a, b, axis=1)

Solution

1. Step 1: Recall np.in1d() parameter order

   The first argument is the array to test membership for, the second is the array to check against.

2. Step 2: Evaluate each option

   np.in1d(a, b) uses correct order: np.in1d(a, b). np.in1d(b, a) reverses arrays, np.in1d(a == b) uses invalid syntax, np.in1d(a, b, axis=1) uses unsupported axis parameter.

3. Final Answer:

   np.in1d(a, b) -> Option B

4. Quick Check:

   Correct syntax = np.in1d(a, b) [OK]

Hint: First array is tested, second array is reference [OK]

Common Mistakes:

• Swapping the order of arrays in np.in1d()
• Adding unsupported parameters like axis
• Using comparison operators inside np.in1d()

3. What is the output of the following code?

import numpy as np
x = np.array([1, 3, 5, 7])
y = np.array([3, 4, 5])
result = np.in1d(x, y)
print(result)

medium

A. [False True True False]

B. [True False True False]

C. [False True False False]

D. [True True True True]

Solution

1. Step 1: Check each element of x against y

   1 in y? No (False), 3 in y? Yes (True), 5 in y? Yes (True), 7 in y? No (False).

2. Step 2: Form the boolean array

   Result is [False, True, True, False].

3. Final Answer:

   [False True True False] -> Option A

4. Quick Check:

   Membership booleans = [False True True False] [OK]

Hint: Check each element one by one for membership [OK]

Common Mistakes:

• Mixing up True and False positions
• Assuming np.in1d returns matching elements instead of booleans
• Forgetting to import numpy

4. The following code throws an error. What is the mistake?

import numpy as np
x = [1, 2, 3]
y = np.array([2, 3, 4])
result = np.in1d(x, y, axis=0)
print(result)

medium

A. np.in1d() requires both inputs to be lists.

B. x should be converted to a NumPy array before using np.in1d().

C. np.in1d() does not accept the 'axis' parameter.

D. The arrays x and y must have the same shape.

Solution

1. Step 1: Check np.in1d() parameters

   np.in1d() accepts only two main parameters: the test array and the array to check against. It does not support an 'axis' parameter.

2. Step 2: Identify the error cause

   Passing axis=0 causes a TypeError because it's not a valid argument.

3. Final Answer:

   np.in1d() does not accept the 'axis' parameter. -> Option C

4. Quick Check:

   Invalid parameter = np.in1d() does not accept the 'axis' parameter. [OK]

Hint: np.in1d() only takes two main arguments [OK]

Common Mistakes:

• Trying to use axis parameter with np.in1d()
• Assuming input types must match exactly
• Thinking np.in1d() requires both inputs as arrays

5. You have two arrays:

data = np.array([10, 20, 30, 40, 50])
filter_vals = np.array([20, 40, 60])

You want to create a new array containing only elements from data that are present in filter_vals. Which code snippet correctly achieves this?

hard

A. filtered = data[np.in1d(data, filter_vals)]

B. filtered = filter_vals[np.in1d(filter_vals, data)]

C. filtered = np.in1d(data, filter_vals)

D. filtered = data[filter_vals]

Solution

1. Step 1: Use np.in1d() to get boolean mask

   np.in1d(data, filter_vals) returns a boolean array marking elements of data present in filter_vals.

2. Step 2: Use boolean mask to filter data

   Indexing data with this boolean mask selects only matching elements.

3. Final Answer:

   filtered = data[np.in1d(data, filter_vals)] -> Option A

4. Quick Check:

   Boolean mask indexing = filtered = data[np.in1d(data, filter_vals)] [OK]

Hint: Use np.in1d() mask to index original array [OK]

Common Mistakes:

• Indexing filter_vals instead of data
• Using np.in1d() without indexing
• Trying to index with filter_vals directly