Boolean indexing for filtering in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to filter the array and keep only values greater than 5.

NumPy

import numpy as np
arr = np.array([3, 7, 2, 9, 5])
filtered = arr[arr [1] 5]
print(filtered)

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

A>

B<

C==

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using == instead of > which selects only values equal to 5.

Using < which selects values less than 5.

We use > to select values greater than 5.

2fill in blank

medium

Complete the code to filter the array and keep only even numbers.

NumPy

import numpy as np
arr = np.array([1, 4, 7, 8, 10])
even = arr[arr [1] 2 == 0]
print(even)

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

A+

B//

C**

D%

Attempts:

3 left

💡 Hint

Common Mistakes

Using floor division // which does not check for evenness.

Using exponentiation ** which is unrelated.

The modulo operator % gives the remainder. Even numbers have remainder 0 when divided by 2.

3fill in blank

hard

Fix the error in the code to filter values less than 10.

NumPy

import numpy as np
arr = np.array([12, 5, 8, 15, 3])
filtered = arr[arr [1] 10]
print(filtered)

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

A=>

B<

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using invalid operator => which causes syntax error.

Using == which selects only values equal to 10.

The operator < selects values less than 10.

4fill in blank

hard

Fill both blanks to create a dictionary of words and their lengths for words longer than 3 characters.

NumPy

words = ['cat', 'elephant', 'dog', 'lion']
lengths = {word: [1] for word in words if [2]
print(lengths)

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

Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using word > 3 which compares string to number and causes error.

Using word instead of length in the dictionary values.

We use len(word) to get the length and filter words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values, only for words longer than 4 characters.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
result = { [1]: [2] for w in words if [3] }
print(result)

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

Aw.upper()

Blen(w)

Clen(w) > 4

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using w.lower() instead of uppercase for keys.

Filtering with len(w) >= 4 which includes words of length 4.

Keys are uppercase words using w.upper(), values are lengths with len(w), filtered by length > 4.

Practice

1. What does boolean indexing in numpy allow you to do?

easy

A. Select elements from an array based on True/False conditions

B. Sort an array in ascending order

C. Change the data type of an array

D. Calculate the sum of all elements in an array

Solution

1. Step 1: Understand boolean indexing concept

   Boolean indexing uses a True/False array to pick elements from another array.

2. Step 2: Compare with other options

   Sorting, changing data type, and summing are different numpy operations, not boolean indexing.

3. Final Answer:

   Select elements from an array based on True/False conditions -> Option A

4. Quick Check:

   Boolean indexing = filtering by True/False [OK]

Hint: Boolean indexing picks elements where condition is True [OK]

Common Mistakes:

• Confusing boolean indexing with sorting
• Thinking it changes data types
• Assuming it calculates sums

2. Which of the following is the correct syntax to filter array arr for values greater than 5 using boolean indexing?

easy

A. arr > 5[arr]

B. arr[arr > 5]

C. arr.filter(arr > 5)

D. arr[arr < 5]

Solution

1. Step 1: Identify correct boolean indexing syntax

   In numpy, filtering uses arr[condition] where condition is a boolean array.

2. Step 2: Check each option

   arr[arr > 5] uses correct syntax. arr > 5[arr] is invalid syntax. arr.filter(arr > 5) is not a numpy method. arr[arr < 5] filters for less than 5, not greater.

3. Final Answer:

   arr[arr > 5] -> Option B

4. Quick Check:

   Correct syntax is arr[condition] [OK]

Hint: Use arr[condition] to filter arrays in numpy [OK]

Common Mistakes:

• Placing condition outside brackets
• Using non-existent filter method
• Mixing up greater than and less than

3. What is the output of the following code?

import numpy as np
arr = np.array([2, 7, 4, 9, 1])
filtered = arr[arr % 2 == 1]

medium

A. [7 4 9]

B. [2 4]

C. [7 9 1]

D. [2 7 4 9 1]

Solution

1. Step 1: Understand the condition arr % 2 == 1

   This condition selects odd numbers because odd numbers have remainder 1 when divided by 2.

2. Step 2: Apply condition to array elements

   Elements 7, 9, and 1 are odd, so they are selected.

3. Final Answer:

   [7 9 1] -> Option C

4. Quick Check:

   Filter odd numbers = [7 9 1] [OK]

Hint: Use modulo (%) to filter odd/even numbers [OK]

Common Mistakes:

• Selecting even numbers instead of odd
• Including all elements without filtering
• Misunderstanding modulo operator

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

import numpy as np
arr = np.array([10, 15, 20, 25])
filtered = arr[arr > 15 and arr < 25]

medium

A. Using 'and' instead of '&' for element-wise condition

B. Missing parentheses around conditions

C. Using 'or' instead of 'and'

D. Array is not defined properly

Solution

1. Step 1: Identify boolean operator error

   In numpy, element-wise logical operations require '&' instead of Python's 'and'.

2. Step 2: Understand why 'and' causes error

   'and' expects single boolean, but arr > 15 and arr < 25 returns arrays, causing TypeError.

3. Final Answer:

   Using 'and' instead of '&' for element-wise condition -> Option A

4. Quick Check:

   Use '&' for element-wise logical AND [OK]

Hint: Use & with parentheses for multiple conditions [OK]

Common Mistakes:

• Using 'and' instead of '&' in numpy conditions
• Forgetting parentheses around each condition
• Assuming 'or' works like '|'

5. Given a numpy array data = np.array([3, 6, 9, 12, 15, 18]), how would you filter values that are divisible by 3 but not by 6 using boolean indexing?

hard

A. data[(data % 3 == 0) & (data % 6 == 0)]

B. data[(data % 3 == 0) | (data % 6 != 0)]

C. data[(data % 3 != 0) & (data % 6 == 0)]

D. data[(data % 3 == 0) & (data % 6 != 0)]

Solution

1. Step 1: Define conditions for filtering

   We want numbers divisible by 3 (data % 3 == 0) but not divisible by 6 (data % 6 != 0).

2. Step 2: Combine conditions with element-wise AND

   Use '&' to combine both conditions inside parentheses for correct boolean indexing.

3. Step 3: Apply combined condition to data array

   data[(data % 3 == 0) & (data % 6 != 0)] correctly applies both conditions with '&'. Others use wrong operators or conditions.

4. Final Answer:

   data[(data % 3 == 0) & (data % 6 != 0)] -> Option D

5. Quick Check:

   Use & and parentheses for combined conditions [OK]

Hint: Combine conditions with & and parentheses for filtering [OK]

Common Mistakes:

• Using | instead of & for AND condition
• Mixing up divisibility conditions
• Forgetting parentheses around each condition