Boolean indexing in NumPy - Step-by-Step Execution

Concept Flow - Boolean indexing

Start with array

↓

Create boolean mask

↓

Apply mask to array

↓

Get filtered array

↓

Use filtered array for analysis or output

Boolean indexing uses a mask of True/False values to select elements from an array.

Execution Sample

NumPy

import numpy as np
arr = np.array([10, 15, 20, 25, 30])
mask = arr > 20
filtered = arr[mask]
print(filtered)

This code selects elements from arr that are greater than 20 using boolean indexing.

Execution Table

Step	Action	Array 'arr'	Mask 'mask'	Filtered array 'filtered'
1	Create array	[10, 15, 20, 25, 30]	N/A	N/A
2	Create mask arr > 20	[10, 15, 20, 25, 30]	[False, False, False, True, True]	N/A
3	Apply mask to arr	[10, 15, 20, 25, 30]	[False, False, False, True, True]	[25, 30]
4	Print filtered	[10, 15, 20, 25, 30]	[False, False, False, True, True]	[25, 30]

💡 Finished filtering; mask selects elements > 20, resulting in [25, 30]

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
arr	N/A	[10, 15, 20, 25, 30]	[10, 15, 20, 25, 30]	[10, 15, 20, 25, 30]	[10, 15, 20, 25, 30]
mask	N/A	N/A	[False, False, False, True, True]	[False, False, False, True, True]	[False, False, False, True, True]
filtered	N/A	N/A	N/A	[25, 30]	[25, 30]

Key Moments - 3 Insights

Why does the mask have True only for some elements?

The mask is created by comparing each element to 20 (arr > 20). Only elements greater than 20 get True, as shown in step 2 of the execution_table.

What happens if we use the mask directly without applying it to arr?

The mask itself is just True/False values, not the filtered data. You must apply it to arr (arr[mask]) to get the selected elements, as in step 3.

Can the filtered array have fewer elements than the original?

Yes, because the mask selects only elements where the condition is True, so filtered array is smaller or equal in size, shown in step 3.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what is the value of mask?

A [False, False, False, True, True]

B [True, True, False, False, False]

C [False, True, True, False, True]

D [True, False, True, True, False]

💡 Hint

Check the 'Mask mask' column at step 2 in the execution_table.

At which step does the filtered array get its values?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'Filtered array filtered' column in the execution_table to see when it changes from N/A.

If we change the condition to arr >= 25, how would the mask change at step 2?

A [False, False, False, True, True]

B [False, False, False, False, True]

C [False, False, False, True, False]

D [True, True, True, True, True]

💡 Hint

Compare arr values to 25 and mark True where arr >= 25, then check mask pattern.

Concept Snapshot

Boolean indexing in numpy:
- Create a boolean mask by comparing array elements.
- Use mask to select elements: filtered = arr[mask]
- Mask is same shape as array, with True/False.
- Result is array of elements where mask is True.
- Useful for filtering data easily.

Full Transcript

Boolean indexing lets you pick elements from a numpy array using True/False values. First, you make a mask by checking a condition on each element. For example, arr > 20 creates a mask with True where elements are greater than 20. Then you use this mask to get only those elements from the array by writing arr[mask]. The filtered array contains only the selected elements. This method is simple and powerful for filtering data in numpy.