np.where() for conditional selection in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select elements greater than 5 using np.where().

NumPy

import numpy as np
arr = np.array([3, 7, 1, 9, 4])
result = np.where(arr [1] 5)
print(result)

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

A!=

B<

C==

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > will select wrong elements.

Using == will only select elements exactly equal to 5.

The np.where() function returns indices where the condition is true. Here, we want indices where elements are greater than 5.

2fill in blank

medium

Complete the code to replace values less than 4 with 0 using np.where().

NumPy

import numpy as np
arr = np.array([2, 5, 3, 7, 1])
new_arr = np.where(arr [1] 4, 0, arr)
print(new_arr)

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

A>

B<

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using > will replace wrong elements.

Forgetting to provide the else part keeps all elements unchanged.

The condition arr < 4 selects elements less than 4. np.where() replaces those with 0, else keeps original.

3fill in blank

hard

Fix the error in the code to select elements equal to 10 using np.where().

NumPy

import numpy as np
arr = np.array([10, 5, 10, 3, 10])
indices = np.where(arr [1] 10)
print(indices)

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

A===

B=

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using single = causes syntax error.

Using === is invalid in Python.

In Python, == is used to check equality. = is assignment and causes error here.

4fill in blank

hard

Fill both blanks to create a new array where values greater than 3 are replaced by 1, else 0.

NumPy

import numpy as np
arr = np.array([2, 4, 1, 5, 3])
new_arr = np.where(arr [1] 3, [2], 0)
print(new_arr)

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

A>

B1

C<

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using < instead of > selects wrong elements.

Replacing with 0 instead of 1 changes the output.

The condition arr > 3 selects elements greater than 3. These are replaced by 1, else 0.

5fill in blank

hard

Fill all three blanks to create a new array where values less than 5 are replaced by 0, values equal to 5 by 5, else by 10.

NumPy

import numpy as np
arr = np.array([3, 5, 7, 2, 5])
new_arr = np.where(arr [1] 5, 0, np.where(arr [2] 5, [3], 10))
print(new_arr)

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

A<

B==

C5

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up < and == operators.

Using wrong replacement values changes the output.

The first condition checks for values less than 5 to replace with 0. The nested np.where() checks for values equal to 5 to replace with 5, else 10.

Practice

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

easy

A. Selects elements from two arrays based on a condition

B. Sorts an array in ascending order

C. Calculates the mean of an array

D. Creates a new array filled with zeros

Solution

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

   np.where() is used to choose elements from two arrays depending on a condition.

2. Step 2: Compare with other options

   Sorting, calculating mean, or creating zeros are different functions like np.sort(), np.mean(), and np.zeros().

3. Final Answer:

   Selects elements from two arrays based on a condition -> Option A

4. Quick Check:

   np.where() = conditional selection [OK]

Hint: np.where picks from two arrays based on a condition [OK]

Common Mistakes:

• Confusing np.where() with sorting or aggregation functions
• Thinking np.where() creates new arrays without conditions
• Assuming np.where() only finds indices, not selects values

2. Which of the following is the correct syntax to use np.where() to select elements from arrays a and b based on condition cond?

easy

A. np.where(a, cond, b)

B. np.where(cond, a, b)

C. np.where(a, b, cond)

D. np.where(cond, b, a)

Solution

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

   The correct order is np.where(condition, x, y) where elements from x are chosen if condition is True, else from y.

2. Step 2: Match parameters with options

   np.where(cond, a, b) matches this order: np.where(cond, a, b). Others mix the order incorrectly.

3. Final Answer:

   np.where(cond, a, b) -> Option B

4. Quick Check:

   np.where(condition, x, y) correct syntax [OK]

Hint: Remember: condition first, then true array, then false array [OK]

Common Mistakes:

• Swapping the order of arrays and condition
• Putting arrays before the condition
• Using np.where() without parentheses

3. What is the output of the following code?

import numpy as np
arr = np.array([10, 20, 30, 40])
result = np.where(arr > 25, arr, 0)
print(result)

medium

A. [ 0 0 30 40]

B. [10 20 30 40]

C. [10 20 0 0]

D. [30 40 0 0]

Solution

1. Step 1: Evaluate the condition arr > 25

   Elements greater than 25 are 30 and 40 at indices 2 and 3.

2. Step 2: Apply np.where() selection

   For elements > 25, keep original value; else replace with 0. So result is [0, 0, 30, 40].

3. Final Answer:

   [ 0 0 30 40] -> Option A

4. Quick Check:

   Condition true keeps value, false replaces with 0 [OK]

Hint: Check condition element-wise, replace false with second array [OK]

Common Mistakes:

• Confusing which elements satisfy the condition
• Mixing up true and false array values
• Forgetting np.where returns a new array

4. Identify the error in this code snippet:

import numpy as np
x = np.array([1, 2, 3, 4])
y = np.array([10, 20, 30, 40])
result = np.where(x > 2, y)
print(result)

medium

A. Condition x > 2 is invalid syntax

B. Arrays x and y have different lengths

C. Missing third argument for false condition in np.where()

D. np.where() cannot be used with arrays

Solution

1. Step 1: Check np.where() arguments

   np.where() requires three arguments for conditional selection: condition, array if True, array if False.

2. Step 2: Identify missing argument

   The code only provides condition and one array, missing the third argument for false case.

3. Final Answer:

   Missing third argument for false condition in np.where() -> Option C

4. Quick Check:

   np.where needs condition, true array, false array [OK]

Hint: np.where needs three arguments for selection [OK]

Common Mistakes:

• Using np.where with only two arguments for selection
• Assuming np.where can infer missing arrays
• Confusing np.where with np.nonzero

5. Given arrays a = np.array([5, 15, 25, 35]) and b = np.array([100, 200, 300, 400]), how can you create a new array that takes elements from b when a is greater than 20, otherwise from a, using np.where()?

hard

A. np.where(a > 20, a, b)

B. np.where(b > 20, a, b)

C. np.where(a < 20, b, a)

D. np.where(a > 20, b, a)

Solution

1. Step 1: Understand the condition and selection

   We want elements from b where a > 20, else from a.

2. Step 2: Apply np.where() with correct order

   Use np.where(condition, x, y) where x is chosen if condition True, else y. So np.where(a > 20, b, a).

3. Final Answer:

   np.where(a > 20, b, a) -> Option D

4. Quick Check:

   Condition true picks b, false picks a [OK]

Hint: Condition first, then true array, then false array [OK]

Common Mistakes:

• Swapping arrays in np.where() parameters
• Using wrong comparison operator
• Confusing which array to pick on true condition