np.where() for conditional selection in NumPy - Step-by-Step Execution

Concept Flow - np.where() for conditional selection

Start with array

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Apply condition

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np.where(condition, x, y)

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For each element: if condition True -> take x else y

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Return new array with selected values

np.where() checks a condition on each element and picks values from two options accordingly, creating a new array.

Execution Sample

NumPy

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

This code replaces elements in arr with 0 if they are not greater than 3.

Execution Table

Step	Element Index	Element Value	Condition (arr > 3)	Value if True	Value if False	Result Element
1	0	1	False	1	0	0
2	1	2	False	2	0	0
3	2	3	False	3	0	0
4	3	4	True	4	0	4
5	4	5	True	5	0	5
6	-	-	-	-	-	[0 0 0 4 5]

💡 All elements processed, result array created with condition applied.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	Final
arr	[1 2 3 4 5]	[1 2 3 4 5]	[1 2 3 4 5]	[1 2 3 4 5]	[1 2 3 4 5]	[1 2 3 4 5]	[1 2 3 4 5]
result	[]	[0]	[0 0]	[0 0 0]	[0 0 0 4]	[0 0 0 4 5]	[0 0 0 4 5]

Key Moments - 2 Insights

Why does np.where return 0 for elements not greater than 3 instead of the original element?

Because np.where uses the second argument (here 0) for elements where the condition is False, as shown in execution_table rows 1-3.

Is the original array changed after np.where is applied?

No, np.where creates a new array with selected values; the original array 'arr' remains unchanged as shown in variable_tracker.

Visual Quiz - 3 Questions

Look at the execution table, what is the result element at index 2?

A 3

B 0

C 2

D 4

💡 Hint

Check row 3 in the execution_table where index 2 is processed.

At which step does the condition become True for the first time?

A Step 1

B Step 3

C Step 4

D Step 5

💡 Hint

Look at the 'Condition (arr > 3)' column in execution_table rows.

If we change the false value from 0 to -1, how would the result element at index 1 change?

A It would be 2

B It would be -1

C It would be 0

D It would be 1

💡 Hint

Refer to how np.where picks values based on condition in execution_table.

Concept Snapshot

np.where(condition, x, y)
Checks each element against condition.
If True, picks x; if False, picks y.
Returns new array with selected values.
Does not change original array.

Full Transcript

This visual execution shows how np.where() works for conditional selection. We start with an array and a condition (arr > 3). For each element, np.where checks if the condition is true. If yes, it picks the element itself; if no, it picks 0. The execution table traces each element's check and the resulting value. The variable tracker shows the result array building up step by step, while the original array stays the same. Key moments clarify why values change and that the original array is not modified. The quiz tests understanding of element-wise selection and condition evaluation.