np.in1d() for membership testing in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check which elements of array1 are in array2 using np.in1d().

NumPy

import numpy as np
array1 = np.array([1, 2, 3, 4])
array2 = np.array([3, 4, 5, 6])
result = np.in1d(array1, [1])
print(result)

Drag options to blanks, or click blank then click option'

Aarray2

Barray1

C[3, 4]

Dnp.array([1, 2])

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same array for both arguments.

Passing a list instead of a numpy array.

The second argument of np.in1d() is the array to check membership against. Here, array2 is correct.

2fill in blank

medium

Complete the code to get a boolean array showing membership of elements in arr1 within arr2.

NumPy

import numpy as np
arr1 = np.array(['apple', 'banana', 'cherry'])
arr2 = np.array(['banana', 'date', 'fig'])
membership = np.in1d(arr1, [1])
print(membership)

Drag options to blanks, or click blank then click option'

Aarr2

B['banana', 'date']

Carr1

D['apple', 'fig']

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the arrays in the function call.

Passing a list instead of a numpy array.

The second argument is the array to check membership against. Here, arr2 is correct.

3fill in blank

hard

Fix the error in the code to correctly test membership using np.in1d().

NumPy

import numpy as np
x = np.array([10, 20, 30])
y = np.array([20, 40, 60])
result = np.in1d([1], y)
print(result)

Drag options to blanks, or click blank then click option'

Ay

Bnp.array([20, 40])

C[10, 20]

Dx

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the order of arrays in np.in1d.

Passing a list instead of a numpy array.

The first argument should be the array whose elements you want to test for membership in the second array. Here, x is correct.

4fill in blank

hard

Fill both blanks to create a dictionary showing elements of words and whether their length is greater than 5 and if they are in the list check_list using np.in1d().

NumPy

words = ['apple', 'banana', 'cherry', 'date']
check_list = ['banana', 'date', 'fig']
result = {word: (len(word) [1] 5 and np.in1d([word], [2])[0]) for word in words}
print(result)

Drag options to blanks, or click blank then click option'

A>

B<

Ccheck_list

Dwords

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong comparison operator.

Checking membership against the wrong list.

The condition checks if the length of the word is greater than 5 and if the word is in check_list. So, > and check_list are correct.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their membership boolean in check_words if their length is less than 6.

NumPy

words = ['apple', 'banana', 'cherry', 'date']
check_words = ['APPLE', 'DATE', 'FIG']
result = {word[1]: np.in1d([word[2]], [3])[0] for word in words if len(word) < 6}
print(result)

Drag options to blanks, or click blank then click option'

A.upper()

B.lower()

Ccheck_words

Dwords

Attempts:

3 left

💡 Hint

Common Mistakes

Using .lower() instead of .upper().

Checking membership against the wrong list.

The keys are uppercase words, so .upper() is used twice. Membership is checked against check_words.

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